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Heidinger, Andrew K., Changyong Cao, and Jerry T. Sullivan. "Using Moderate Resolution Imaging Spectrometer (MODIS) to calibrate advanced very high resolution radiometer reflectance channels." Journal of Geophysical Research: Atmospheres 107, no. D23 (2002): AAC 11–1—AAC 11–10. http://dx.doi.org/10.1029/2001jd002035.
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Davis, Robert E., Thomas H. Painter, Rick Forster, et al. "NASA Cold Land Processes Experiment (CLPX 2002/03): Spaceborne Remote Sensing." Journal of Hydrometeorology 9, no. 6 (2008): 1427–33. http://dx.doi.org/10.1175/2008jhm926.1.
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Abstract This paper describes satellite data collected as part of the 2002/03 Cold Land Processes Experiment (CLPX). These data include multispectral and hyperspectral optical imaging, and passive and active microwave observations of the test areas. The CLPX multispectral optical data include the Advanced Very High Resolution Radiometer (AVHRR), the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Multi-angle Imaging Spectroradiometer (MISR). The spaceborne hyperspectral optical data consist of measurements acquired with the NASA Earth Observing-1 (EO-1) Hyperion imaging spectrometer. The passive microwave data include observations from the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Scanning Radiometer (AMSR) for Earth Observing System (EOS; AMSR-E). Observations from the Radarsat synthetic aperture radar and the SeaWinds scatterometer flown on QuikSCAT make up the active microwave data.
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Albert, P., R. Bennartz, R. Preusker, R. Leinweber, and J. Fischer. "Remote Sensing of Atmospheric Water Vapor Using the Moderate Resolution Imaging Spectroradiometer." Journal of Atmospheric and Oceanic Technology 22, no. 3 (2005): 309–14. http://dx.doi.org/10.1175/jtech1708.1.
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Abstract This paper presents first validation results for an algorithm developed for the retrieval of integrated columnar water vapor from measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board the polar-orbiting Terra and Aqua platforms. The algorithm is based on the absorption of reflected solar radiation by atmospheric water vapor and allows the retrieval of integrated water vapor above cloud-free land surfaces. A comparison of the retrieved water vapor with measurements of the Microwave Water Radiometer at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for a 10-month period in 2002 showed an rms deviation of 1.7 kg m−2 and a bias of 0.6 kg m−2. A comparison with radio soundings in central Europe from July 2002 to April 2003 showed an rms deviation of 2 kg m−2 and a bias of −0.8 kg m−2.
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Lee, Thomas F., Steven D. Miller, Carl Schueler, and Shawn Miller. "NASA MODIS Previews NPOESS VIIRS Capabilities." Weather and Forecasting 21, no. 4 (2006): 649–55. http://dx.doi.org/10.1175/waf935.1.
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Abstract The Visible/Infrared Imager Radiometer Suite (VIIRS), scheduled to fly on the satellites of the National Polar-orbiting Operational Environmental Satellite System, will combine the missions of the Advanced Very High Resolution Radiometer (AVHRR), which flies on current National Oceanic and Atmospheric Administration satellites, and the Operational Linescan System aboard the Defense Meteorological Satellite Program satellites. VIIRS will offer a number of improvements to weather forecasters. First, because of a sophisticated downlink and relay system, VIIRS latencies will be 30 min or less around the globe, improving the timeliness and therefore the operational usefulness of the images. Second, with 22 channels, VIIRS will offer many more products than its predecessors. As an example, a true-color simulation is shown using data from the Earth Observing System’s Moderate Resolution Imaging Spectroradiometer (MODIS), an application current geostationary imagers cannot produce because of a missing “green” wavelength channel. Third, VIIRS images will have improved quality. Through a unique pixel aggregation strategy, VIIRS pixels will not expand rapidly toward the edge of a scan like those of MODIS or AVHRR. Data will retain nearly the same resolution at the edge of the swath as at nadir. Graphs and image simulations depict the improvement in output image quality. Last, the NexSat Web site, which provides near-real-time simulations of VIIRS products, is introduced.
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Hall, Dorothy K., Josefino C. Comiso, Nicolo E. DiGirolamo, Christopher A. Shuman, Jeffrey R. Key, and Lora S. Koenig. "A Satellite-Derived Climate-Quality Data Record of the Clear-Sky Surface Temperature of the Greenland Ice Sheet." Journal of Climate 25, no. 14 (2012): 4785–98. http://dx.doi.org/10.1175/jcli-d-11-00365.1.
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Abstract The authors have developed a climate-quality data record of the clear-sky surface temperature of the Greenland Ice Sheet using the Moderate-Resolution Imaging Spectroradiometer (MODIS) ice-surface temperature (IST) algorithm. Daily and monthly quality-controlled MODIS ISTs of the Greenland Ice Sheet beginning on 1 March 2000 and continuing through 31 December 2010 are presented at 6.25-km spatial resolution on a polar stereographic grid along with metadata to permit detailed accuracy assessment. The ultimate goal is to develop a climate data record (CDR) that starts in 1981 with the Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder (APP) dataset and continues with MODIS data from 2000 to the present, and into the Visible Infrared Imager Radiometer Suite (VIIRS) era (the first VIIRS instrument was launched in October 2011). Differences in the APP and MODIS cloud masks have thus far precluded merging the APP and MODIS IST records, though this will be revisited after the APP dataset has been reprocessed with an improved cloud mask. IST of Greenland may be used to study temperature and melt trends and may also be used in data assimilation modeling and to calculate ice sheet mass balance. The MODIS IST climate-quality dataset provides a highly consistent and well-characterized record suitable for merging with earlier and future IST data records for climate studies. The complete MODIS IST daily and monthly data record is available online.
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Hadjimitsis, D., Z. Mitraka, I. Gazani, A. Retalis, N. Chrysoulakis, and S. Michaelides. "Estimation of spatio-temporal distribution of precipitable water using MODIS and AVHRR data: a case study for Cyprus." Advances in Geosciences 30 (May 9, 2011): 23–29. http://dx.doi.org/10.5194/adgeo-30-23-2011.
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Abstract. In this paper, the atmospheric precipitable water (PW) over the area of Cyprus was estimated by means of Advanced Very High Resolution Radiometer (AVHRR) thermal channels brightness temperature difference (ΔT). The AVHRR derived ΔT was calculated in a grid of 5 × 5 km cells; the corresponding PW value in each grid cell was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 product (near-infrared algorithm). Once the PW – ΔT relationship coefficients corresponding to the area of Cyprus were calculated, the relationship was applied to AVHRR data for one month period. Radiosonde derived PW values, as well as MODIS independent PW values were used to validate the estimations and a good agreement was noted.
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Fan, Xia, and Chen. "Intercomparison of Multiple Satellite Aerosol Products against AERONET over the North China Plain." Atmosphere 10, no. 9 (2019): 480. http://dx.doi.org/10.3390/atmos10090480.
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In this study, using Aerosol Robotic Network aerosol optical depth (AOD) products at three stations in the North China Plain (NCP)—a heavily polluted region in China—the AOD products from six satellite-borne radiometers: the Moderate Resolution Imagining Spectroradiometer (MODIS), the Multiangle Imaging Spectroradiometer (MISR), Ozone Mapping Imaging (OMI), the Visible Infrared Imaging Radiometer (VIIRS), the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and Polarization and Directionality of the Earth’s Reflectances (POLDER), were thoroughly validated, shedding new light on their advantages and disadvantages. The MODIS Deep Blue (DB) products provide more accurate retrievals than the MODIS Dark Target (DT) and other satellite products at the Beijing site (BJ,a megacity), with higher correlations with AERONET (R > 0.93), lower mean absolute bias (MB < 0.012), and higher percentages (>68%) falling within the expected error (EE). All MODIS DT and DB products perform better than the other satellite products at the Xianghe site (XH, a suburb). The MODIS/Aqua DT products at both 3-km and 10-km resolutions performed better than the other space-borne AOD products at the Xinglong site (XL, a rural area at the top of a mountain). MISR, VIIRS, and SeaWiFS tend to underestimate high AOD values and overestimate AOD values under very low AOD conditions in the NCP. Both OMI and POLDER significantly underestimate the AOD. In terms of data volume, MISR with the limited swath width of 380 km has less data volume than the other satellite sensors. MODIS products have the highest sampling rate, especially the MODIS DT and DB merged products, and can be used for various climate study and air-quality monitoring.
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Uprety, Sirish, Changyong Cao, Xiaoxiong Xiong, Slawomir Blonski, Aisheng Wu, and Xi Shao. "Radiometric Intercomparison between Suomi-NPP VIIRS and Aqua MODIS Reflective Solar Bands Using Simultaneous Nadir Overpass in the Low Latitudes." Journal of Atmospheric and Oceanic Technology 30, no. 12 (2013): 2720–36. http://dx.doi.org/10.1175/jtech-d-13-00071.1.
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Abstract On-orbit radiometric performance of the Suomi National Polar-Orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) is studied using the extended simultaneous nadir overpass (SNO-x) approach. Unlike the traditional SNO analysis of data in the high latitudes, this study extends the analysis to the low latitudes—in particular, over desert and ocean sites with relatively stable and homogeneous radiometric properties—for intersatellite comparisons. This approach utilizes a pixel-by-pixel match with an efficient geospatial matching algorithm to map VIIRS data into the Moderate Resolution Imaging Spectroradiometer (MODIS). VIIRS moderate-resolution bands M-1 through M-8 are compared with Aqua MODIS equivalent bands to quantify radiometric bias over the North African desert and over the ocean. Biases exist between VIIRS and MODIS in several bands, primarily because of spectral differences as well as possible calibration uncertainties, residual cloud contamination, and bidirectional reflectance distribution function (BRDF). The impact of spectral differences on bias is quantified by using the Moderate Resolution Atmospheric Transmission (MODTRAN) and hyperspectral measurements from the Earth Observing-1 (EO-1) Hyperion and the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS). After accounting for spectral differences and bias uncertainties, the VIIRS radiometric bias over desert agrees with MODIS measurements within 2% except for the VIIRS shortwave infrared (SWIR) band M-8, which indicates a nearly 3% bias. Over ocean, VIIRS agrees with MODIS within 2% by the end of January 2013 with uncertainty less than 1%. Furthermore, VIIRS bias relative to MODIS is also computed at the Antarctica Dome C site for validation and the result agrees well within 1% with the bias estimated using SNO-x over desert.
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Mizuochi, Hiroki, Yoshihiro Iijima, Hirohiko Nagano, Ayumi Kotani, and Tetsuya Hiyama. "Dynamic Mapping of Subarctic Surface Water by Fusion of Microwave and Optical Satellite Data Using Conditional Adversarial Networks." Remote Sensing 13, no. 2 (2021): 175. http://dx.doi.org/10.3390/rs13020175.
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Surface water monitoring with fine spatiotemporal resolution in the subarctic is important for understanding the impact of climate change upon hydrological cycles in the region. This study provides dynamic water mapping with daily frequency and a moderate (500 m) resolution over a heterogeneous thermokarst landscape in eastern Siberia. A combination of random forest and conditional generative adversarial networks (pix2pix) machine learning (ML) methods were applied to data fusion between the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer 2, with the addition of ancillary hydrometeorological information. The results show that our algorithm successfully filled in observational gaps in the MODIS data caused by cloud interference, thereby improving MODIS data availability from 30.3% to almost 100%. The water fraction estimated by our algorithm was consistent with that derived from the reference MODIS data (relative mean bias: −2.43%; relative root mean squared error: 14.7%), and effectively rendered the seasonality and heterogeneous distribution of the Lena River and the thermokarst lakes. Practical knowledge of the application of ML to surface water monitoring also resulted from the preliminary experiments involving the random forest method, including timing of the water-index thresholding and selection of the input features for ML training.
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Borbas, Eva E., and Paul W. Menzel. "Observed HIRS and Aqua MODIS Thermal Infrared Moisture Determinations in the 2000s." Remote Sensing 13, no. 3 (2021): 502. http://dx.doi.org/10.3390/rs13030502.
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This paper compares the tropospheric moisture data records derived from High-resolution Infrared Radiation Sounder (HIRS) and Moderate Resolution Imaging Spectro-radiometer (MODIS) measurements from the years 2003 through 2013. Total Precipitable Water Vapor (TPW) and Upper Tropospheric Precipitable Water Vapor (UTPW) are derived using the infrared spectral bands in the CO2 and H2O absorption bands as well as in the atmospheric windows. Retrieval of TPW and UTPW uses a statistical regression algorithm performed using clear sky radiances (and Brightness Temperatures) measured over land and ocean for both day and night. The TPW and UTPW seasonal cycles of HIRS and MODIS observations are found to be in synchronization with zonal mean values for one degree latitude bands within 2.0 mm and 0.07 mm, respectively.
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Frey, Richard A., Steven A. Ackerman, Robert E. Holz, Steven Dutcher, and Zach Griffith. "The Continuity MODIS-VIIRS Cloud Mask." Remote Sensing 12, no. 20 (2020): 3334. http://dx.doi.org/10.3390/rs12203334.
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This paper introduces the Continuity Moderate Resolution Imaging Spectroradiometer (MODIS)-Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Mask (MVCM), a cloud detection algorithm designed to facilitate continuity in cloud detection between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Soumi National Polar-orbiting Partnership (SNPP) spacecraft. It is based on the MODIS cloud mask that has been operating since 2000 with the launch of the Terra spacecraft (MOD35) and continuing in 2002 with Aqua (MYD35). The MVCM makes use of fourteen spectral bands that are common to both MODIS and VIIRS so as to create consistent cloud detection between the two instruments and across the years 2000–2020 and beyond. Through comparison data sets, including collocated Aqua MODIS and Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) from the A-Train, this study was designed to assign statistical consistency benchmarks between the MYD35 and MVCM cloud masks. It is shown that the MVCM produces consistent cloud detection results between Aqua MODIS, SNPP VIIRS, and NOAA-20 VIIRS and that the quality is comparable to the standard Aqua MODIS cloud mask. Globally, comparisons with collocated CALIOP lidar show combined clear and cloudy sky hit rates of 88.2%, 87.5%, 86.8%, and 86.8% for MYD35, MVCM Aqua MODIS, MVCM SNPP VIIRS, and MVCM NOAA-20 VIIRS, respectively, for June through until August, 2018. For the same months and in the same order for 60S–60N, hit rates are 90.7%, 90.5%, 90.1%, and 90.3%. From the time series constructed from gridded daily means of 60S–60N cloud fractions, we found that the mean day-to-day cloud fraction differences/standard deviations in percent to be 0.68/0.55, 0.94/0.64, −0.20/0.50, and 0.44/0.82 for MVCM Aqua MODIS-MVCM SNPP VIIRS day and night, and MVCM NOAA-20 VIIRS-MVCM SNPP VIIRS day and night, respectively. It is seen that the MODIS and VIIRS 1.38 µm cirrus detection bands perform similarly but with MODIS detecting slightly more clouds in the middle to high levels of the troposphere and the VIIRS detecting more in the upper troposphere above 16 km. In the Arctic, MVCM Aqua MODIS and SNPP VIIRS reported cloud fraction differences of 0–3% during the mid-summer season and −3–4% during the mid-winter.
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Fang, Bin, Venkat Lakshmi, Rajat Bindlish, and Thomas Jackson. "AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data." Remote Sensing 10, no. 10 (2018): 1575. http://dx.doi.org/10.3390/rs10101575.
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Soil moisture (SM) applications in terrestrial hydrology require higher spatial resolution soil moisture products than those provided by passive microwave remote sensing instruments (grid resolution of 9 km or larger). In this investigation, an innovative algorithm that uses visible/infrared remote sensing observations to downscale Advanced Microwave Scanning Radiometer 2 (AMSR2) coarse spatial resolution SM products was developed and implemented for use with data provided by the Advanced Microwave Scanning Radiometer 2 (AMSR2). The method is based on using the Normalized Difference Vegetation Index (NDVI) modulated relationships between day/night SM and temperature change at corresponding times. Land surface model output variables from the North America Land Data Assimilation System (NLDAS), remote sensing data from the Moderate-Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) were used in this methodology. The functional relationships developed using NLDAS data at a grid resolution of 12.5 km were applied to downscale AMSR2 JAXA (Japan Aerospace Exploration Agency) SM product (25 km) using MODIS land surface temperature (LST) and NDVI observations (1 km) to produce the 1 km SM estimates. The downscaled SM estimates were validated by comparing them with ISMN (International Soil Moisture Network) in situ SM in the Black Bear–Red Rock watershed, central Oklahoma between 2015–2017. The overall statistical variables of the downscaled AMSR2 SM validation R2, slope, RMSE and bias, demonstrate good accuracy. The downscaled SM better characterized the spatial and temporal variability of SM at watershed scales than the original SM product.
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Diedrich, H., R. Preusker, R. Lindstrot, and J. Fischer. "Retrieval of daytime total columnar water vapour from MODIS measurements over land surfaces." Atmospheric Measurement Techniques Discussions 7, no. 7 (2014): 7753–92. http://dx.doi.org/10.5194/amtd-7-7753-2014.
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Abstract. A retrieval of total column water vapour (TCWV) from MODIS (Moderate-resolution Imaging Spectroradiometer) measurements is presented. The algorithm is adapted from a retrieval for MERIS (Medium Resolution Imaging Spectrometer) from Lindstrot et al. (2012). It obtains the TCWV for cloud-free scenes above land at spatial resolution of 1 km × 1 km and provides uncertainties on a pixel-by-pixel basis. The algorithm has been extended by introducing correction coefficients for the transmittance calculation within the forward operator. With that a wet bias of the MODIS algorithm against ARM-Microwave Radiometer data has been eliminated. An extensive validation against other ground-based measurements (GNSS-water vapour stations, GUAN Radiosondes) on a global scale reveals a bias between −0.8 and −1.6 mm and root mean square deviations between 0.9 and 1.9 mm. This is an improvement in comparison to the operational TCWV Level 2 product (bias between −1.9 and −3.2 mm and root mean square deviations between 1.9 and 2.7 mm).
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Benedict, Trenton D., Jesslyn F. Brown, Stephen P. Boyte, et al. "Exploring VIIRS Continuity with MODIS in an Expedited Capability for Monitoring Drought-Related Vegetation Conditions." Remote Sensing 13, no. 6 (2021): 1210. http://dx.doi.org/10.3390/rs13061210.
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Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.
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Engel, Chermelle B., Simon D. Jones, and Karin J. Reinke. "Real-Time Detection of Daytime and Night-Time Fire Hotspots from Geostationary Satellites." Remote Sensing 13, no. 9 (2021): 1627. http://dx.doi.org/10.3390/rs13091627.
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This paper introduces an enhanced version of the Biogeographical Region and Individual Geostationary HHMMSS Threshold (BRIGHT) algorithm. The algorithm runs in real-time and operates over 24 h to include both daytime and night-time detections. The algorithm was executed and tested on 12 months of Himawari-8 data from 1 April 2019 to 31 March 2020, for every valid 10-min observation. The resulting hotspots were compared to those from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The modified BRIGHT hotspots matched with fire detections in VIIRS 96% and MODIS 95% of the time. The number of VIIRS and MODIS hotspots with matches in the coincident modified BRIGHT dataset was lower (at 33% and 46%, respectively). This paper demonstrates a clear link between the number of VIIRS and MODIS hotspots with matches and the minimum fire radiative power considered.
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Zagade, Nayan, Ajaykumar Kadam, Bhavana Umrikar, and Bhagyashri Maggirwar. "Remote Sensing Based Assessment of Agricultural Droughts in Sub-Watersheds of Upper Bhima Basin, India." Remote Sensing of Land 2, no. 2 (2019): 105–11. http://dx.doi.org/10.21523/gcj1.18020204.
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Drought assessment for agricultural sector is vital in order to deal with the water scarcity in Ahmednagar and Pune districts, particularly in sub-watersheds of upper catchment of the River Bhima. Moderate Resolution Imaging Spectro-radiometer (MODIS) satellite data (2000, 2002, 2009, 2014, 2015 and 2017) for the years receiving less rainfall have been procured and various indices were computed to understand the intensity of agricultural droughts in the area. Vegetation health index (VHI) is computed on the basis of vegetation moisture, vegetation condition and land surface temperature condition. Most of the reviewed area shows moderate to extreme drought conditions.
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Borbas, E. Eva, Elisabeth Weisz, Chris Moeller, W. Paul Menzel, and Bryan A. Baum. "Improvement in tropospheric moisture retrievals from VIIRS through the use of infrared absorption bands constructed from VIIRS and CrIS data fusion." Atmospheric Measurement Techniques 14, no. 2 (2021): 1191–203. http://dx.doi.org/10.5194/amt-14-1191-2021.
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Abstract. An operational data product available for both the Suomi National Polar-orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration-20 (NOAA-20) platforms provides high-spatial-resolution infrared (IR) absorption band radiances for Visible Infrared Imaging Radiometer Suite (VIIRS) based on a VIIRS and Crosstrack Infrared Sounder (CrIS) data fusion method. This study investigates the use of these IR radiances, centered at 4.5, 6.7, 7.3, 9.7, 13.3, 13.6, 13.9, and 14.2 µm, to construct atmospheric moisture products (e.g., total precipitable water and upper tropospheric humidity) and to evaluate their accuracy. Total precipitable water (TPW) and upper tropospheric humidity (UTH) retrieved from hyperspectral sounder CrIS measurements are provided at the associated VIIRS sensor's high spatial resolution (750 m) and are compared subsequently to collocated operational Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and S-NPP VIIRS moisture products. This study suggests that the use of VIIRS IR absorption band radiances will provide continuity with Aqua MODIS moisture products.
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Prigent, Catherine, Lise Kilic, Filipe Aires, Victor Pellet, and Carlos Jimenez. "Ice Concentration Retrieval from the Analysis of Microwaves: Evaluation of a New Methodology Optimized for the Copernicus Imaging Microwave Radiometer." Remote Sensing 12, no. 10 (2020): 1594. http://dx.doi.org/10.3390/rs12101594.
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A new methodology has been described in Kilic et al. (Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer, Remote Sensing 2020, 12, 1060, Part 1 of this study) to estimate Sea Ice Concentration (SIC) from satellite passive microwave observations between 6 and 36 GHz. The Ice Concentration Retrieval from the Analysis of Microwaves (IceCREAM) algorithm is based on an optimal estimation, with a simple radiative transfer model derived from satellite observations at 0% and 100% SIC. Observations at low and high frequencies have different spatial resolutions, and a scheme is developed to benefit from the low errors of the low frequencies and the high spatial resolutions of the high frequencies. This effort is specifically designed for the Copernicus Imaging Microwave Radiometer (CIMR) project, equipped with a large deployable antenna to provide a spatial resolution of ∼5 km at 18 and 36 GHz, and ∼15 km at 6 and 10 GHz. The algorithm is tested with Advanced Microwave Scanning Radiometer 2 (AMSR2) observations, for a clear scene over the north polar region, with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) estimates and the Ocean Sea Ice—Satellite Application Facilities (OSI SAF) operational products. Several algorithm options are tested, and the study case shows that both high spatial resolution and low errors are obtained with the IceCREAM method. It is also tested for the full polar regions, winter and summer, under clear and cloudy conditions. Our method is globally applicable, without fine-tuning or further weather filtering. The systematic use of all channels from 6 to 36 GHz makes it robust to changes in ice surface conditions and to weather interactions.
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Riggs, George, and Dorothy Hall. "Continuity of MODIS and VIIRS Snow Cover Extent Data Products for Development of an Earth Science Data Record." Remote Sensing 12, no. 22 (2020): 3781. http://dx.doi.org/10.3390/rs12223781.
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An Earth Observing System global snow cover extent data products record at moderate spatial resolution (375–500 m) began in February 2000 with the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite. The record continued with the Aqua MODIS in July 2002, the Suomi-National Polar Platform (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) in January 2012 and continues with the Joint Polar Satellite System-1 (JPSS-1) VIIRS, launched in November of 2017. The objective of this work is to develop a snow cover extent Earth Science Data Record (ESDR) using different satellites, sensors and algorithms. There are many issues to understand when data from different algorithms and sensors are used over a decade-scale time period to create a continuous dataset. Issues may also arise with sensor degradation and even differences in sensor band locations. In this paper we describe development of an ESDR derived from existing MODIS and VIIRS data products and demonstrate continuity among the products. The MODIS and VIIRS snow cover detection algorithms produce very similar daily snow cover maps, with 90–97% agreement in snow cover extent (SCE) in different landscapes. Differences in SCE between products ranged from 2–15% and are attributable to convolved factors of viewing geometry, pixel spread across a scan and time of observation. Compared at a common grid size of 1 km, there is a mean of 95% agreement in SCE and a difference range of 1–10% between the MODIS and VIIRS SCE maps. Mapping sensor observations to a coarser resolution grid reduces the effect of the factors convolved in the 500 m tile to tile comparisons. We conclude that the MODIS and VIIRS SCE data products are reliable constituents of a moderate-resolution ESDR.
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Sayer, Andrew M., N. Christina Hsu, Corey Bettenhausen, et al. "Cross-calibration of S-NPP VIIRS moderate-resolution reflective solar bands against MODIS Aqua over dark water scenes." Atmospheric Measurement Techniques 10, no. 4 (2017): 1425–44. http://dx.doi.org/10.5194/amt-10-1425-2017.
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Abstract. The Visible Infrared Imaging Radiometer Suite (VIIRS) is being used to continue the record of Earth Science observations and data products produced routinely from National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. However, the absolute calibration of VIIRS's reflected solar bands is thought to be biased, leading to offsets in derived data products such as aerosol optical depth (AOD) as compared to when similar algorithms are applied to different sensors. This study presents a cross-calibration of these VIIRS bands against MODIS Aqua over dark water scenes, finding corrections to the NASA VIIRS Level 1 (version 2) reflectances between approximately +1 and −7 % (dependent on band) are needed to bring the two into alignment (after accounting for expected differences resulting from different band spectral response functions), and indications of relative trending of up to ∼ 0.35 % per year in some bands. The derived calibration gain corrections are also applied to the VIIRS reflectance and then used in an AOD retrieval, and they are shown to decrease the bias and total error in AOD across the mid-visible spectral region compared to the standard VIIRS NASA reflectance calibration. The resulting AOD bias characteristics are similar to those of NASA MODIS AOD data products, which is encouraging in terms of multi-sensor data continuity.
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Miller, Steven D., Thomas F. Lee, and Robert L. Fennimore. "Satellite-Based Imagery Techniques for Daytime Cloud/Snow Delineation from MODIS." Journal of Applied Meteorology 44, no. 7 (2005): 987–97. http://dx.doi.org/10.1175/jam2252.1.
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Abstract This paper presents two multispectral enhancement techniques for distinguishing between regions of cloud and snow cover using optical spectrum passive radiometer satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Fundamental to the techniques are the 1.6- and 2.2-μm shortwave infrared bands that are useful in distinguishing between absorbing snow cover (having low reflectance) and less absorbing liquid-phase clouds (higher reflectance). The 1.38-μm band helps to overcome ambiguities that arise in the case of optically thin cirrus. Designed to provide straightforward, stand-alone environmental characterization for operational forecasters (e.g., military weather forecasters in the context of mission planning), these products portray the information that is contained within complex scenes as value-added, readily interpretable imagery at the highest available spatial resolution. Their utility in scene characterization and quality control of digital snow maps is demonstrated.
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Sai Suman, M. N., H. Gadhavi, V. Ravi Kiran, A. Jayaraman, and S. V. B. Rao. "Role of coarse and fine mode aerosols in MODIS AOD retrieval: a case study." Atmospheric Measurement Techniques Discussions 6, no. 5 (2013): 9109–32. http://dx.doi.org/10.5194/amtd-6-9109-2013.
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Abstract. In the present study we have compared the MODIS (Moderate Resolution Imaging Spectroradiometer) derived aerosol optical depth (AOD) data with that obtained from operating sky-radiometer at a remote rural location in South India (Gadanki, 13.45° N, 79.18° E). While the comparison between total (coarse mode + fine mode) AOD shows R2 value of about 0.71 with a negligible bias of 0.01, if one separates the AOD into fine and coarse mode, the comparison becomes very poor, particularly for fine mode with an R2 value of 0.44. The coarse mode AOD derived from MODIS and sky-radiometer compare better with an R2 value of 0.74 and also the seasonal variation is well captured by both measurements. It is shown that the fine mode fraction derived from MODIS data is more than a factor of two smaller than that derived from the sky-radiometer data. Based on these observations we argue that the selection of aerosol types used in the MODIS retrieval algorithm are not appropriate particularly in the case of South India. Instead of selecting a moderately absorbing aerosol type (as being done currently in the MODIS retrieval) a more absorbing type aerosol is better suited for fine mode aerosols, while reverse is true for the coarse mode aerosols, where instead of using "dust aerosols" which is relatively more absorbing, usage of coarse sea-salt particles which is less absorbing is more appropriate.
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Diedrich, H., R. Preusker, R. Lindstrot, and J. Fischer. "Retrieval of daytime total columnar water vapour from MODIS measurements over land surfaces." Atmospheric Measurement Techniques 8, no. 2 (2015): 823–36. http://dx.doi.org/10.5194/amt-8-823-2015.
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Abstract. A retrieval of total column water vapour (TCWV) from MODIS (Moderate-resolution Imaging Spectroradiometer) measurements is presented. The algorithm is adapted from a retrieval for MERIS (Medium Resolution Imaging Spectrometer) from Lindstrot et al. (2012). It obtains the TCWV for cloud-free scenes above land at a spatial resolution of 1 km × 1 km and provides uncertainties on a pixel-by-pixel basis. The algorithm has been extended by introducing empirical correction coefficients for the transmittance calculation within the forward operator. With that, a wet bias of the MODIS algorithm against ARM microwave radiometer data has been eliminated. The validation against other ground-based measurements (GNSS water vapour stations, GUAN radiosondes, and AERONET sun photometers) on a global scale reveals a bias between −0.8 and −1.6 mm and root mean square deviations between 0.9 and 2 mm. This is an improvement in comparison to the operational TCWV Level 2 product (bias between −1.9 and −3.2 mm and root mean square deviations between 1.9 and 3.4 mm). The comparison to MERIS TCWV for an example overpass exposes a systematic dry bias.
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Wei, Jing, Yiran Peng, Rashed Mahmood, Lin Sun, and Jianping Guo. "Intercomparison in spatial distributions and temporal trends derived from multi-source satellite aerosol products." Atmospheric Chemistry and Physics 19, no. 10 (2019): 7183–207. http://dx.doi.org/10.5194/acp-19-7183-2019.
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Abstract. Satellite-derived aerosol products provide long-term and large-scale observations for analysing aerosol distributions and variations, climate-scale aerosol simulations, and aerosol–climate interactions. Therefore, a better understanding of the consistencies and differences among multiple aerosol products is important. The objective of this study is to compare 11 global monthly aerosol optical depth (AOD) products, which are the European Space Agency Climate Change Initiative (ESA-CCI) Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High Resolution Radiometer (AVHRR), Multi-angle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Visible Infrared Imaging Radiometer (VIIRS), and POLarization and Directionality of the Earth's Reflectance (POLDER) products. AErosol RObotic NEtwork (AERONET) Version 3 Level 2.0 monthly measurements at 308 sites around the world are selected for comparison. Our results illustrate that the spatial distributions and temporal variations of most aerosol products are highly consistent globally but exhibit certain differences on regional and site scales. In general, the AATSR Dual View (ADV) and SeaWiFS products show the lowest spatial coverage with numerous missing values, while the MODIS products can cover most areas (average of 87 %) of the world. The best performance is observed in September–October–November (SON) and the worst is in June–July–August (JJA). All the products perform unsatisfactorily over northern Africa and Middle East, southern and eastern Asia, and their coastal areas due to the influence from surface brightness and human activities. In general, the MODIS products show the best agreement with the AERONET-based AOD values on different spatial scales among all the products. Furthermore, all aerosol products can capture the correct aerosol trends at most cases, especially in areas where aerosols change significantly. The MODIS products perform best in capturing the global temporal variations in aerosols. These results provide a reference for users to select appropriate aerosol products for their particular studies.
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Tereshchenko, Iryna, Alexander N. Zolotokrylin, Tatiana B. Titkova, Luis Brito-Castillo, and Cesar Octavio Monzon. "Seasonal Variation of Surface Temperature–Modulating Factors in the Sonoran Desert in Northwestern Mexico." Journal of Applied Meteorology and Climatology 51, no. 8 (2012): 1519–30. http://dx.doi.org/10.1175/jamc-d-11-0160.1.
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AbstractThe authors explore a new approach to monitoring of desertification that is based on use of results on the relation between albedo and surface temperature for the Sonoran Desert in northwestern Mexico. The criteria of predominance of radiation by using the threshold value of Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) were determined. The radiation mechanism for regulating the temperature of the surface and the definition of threshold values for AVHRR and MODIS NDVI have an objective justification for the energy budget, which is based on the dominance of radiation surface temperature regulation in relation to evapotranspiration. Changes in the extent of arid regions with AVHRR NDVI of <0.08 and MODIS NDVI of <0.10 can be considered to be a characteristic in the evolution of desertification in the Sonoran Desert region. This is true because, in a certain year, the time span of the period when radiation factor predominates is important for the desertification process.
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Li, Fangjun, Xiaoyang Zhang, and Shobha Kondragunta. "Biomass Burning in Africa: An Investigation of Fire Radiative Power Missed by MODIS Using the 375 m VIIRS Active Fire Product." Remote Sensing 12, no. 10 (2020): 1561. http://dx.doi.org/10.3390/rs12101561.
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Biomass burning plays a key role in the interaction between the atmosphere and the biosphere. The nearly two-decade-old Moderate Resolution Imaging Spectroradiometer (MODIS) active fire product provides critical information (e.g., fire radiative power or FRP) for characterizing fires and estimating smoke emissions. Due to limitations of sensing geometry, MODIS fire detection capability degrades at off-nadir angles and the sensor misses the observation of fires occurring inside its equatorial swath gaps. This study investigates missing MODIS FRP observations using the 375 m Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data across Africa where fire occurs in the majority of vegetation-covered areas and significantly contributes to global biomass-burning emissions. We first examine the FRP relationship between the two sensors on a continental scale and in grids of seven different resolutions. We find that MODIS misses a considerable number of low-intensity fires across Africa, which results in the underestimation of daily MODIS FRP by at least 42.8% compared to VIIRS FRP. The underestimation of MODIS FRP varies largely with grid size and satellite view angle. Based on comparisons of grid-level FRP from the two sensors, adjustment models are established at seven resolutions from 0.05°–0.5° for mitigating the underestimation of MODIS grid FRP. Furthermore, the investigation of the effect of equatorial swath gaps on MODIS FRP observations reveals that swath gaps could lead to the underestimation of MODIS monthly summed FRP by 12.5%. The quantitative information of missing MODIS FRP helps to improve our understanding of potential uncertainties in the MODIS FRP based applications, especially emissions estimation.
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Li, Yao, Gang Zhao, Deep Shah, et al. "NASA’s MODIS/VIIRS Global Water Reservoir Product Suite from Moderate Resolution Remote Sensing Data." Remote Sensing 13, no. 4 (2021): 565. http://dx.doi.org/10.3390/rs13040565.
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Global reservoir information can not only benefit local water management but can also improve our understanding of the hydrological cycle. This information includes water area, elevation, and storage; evaporation rate and volume values; and other characteristics. However, operational wall-to-wall reservoir storage and evaporation monitoring information is lacking on a global scale. Here we introduce NASA’s new MODIS/VIIRS Global Water Reservoir product suite based on moderate resolution remote sensing data—the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). This product consists of 8-day (MxD28C2 and VNP28C2) and monthly (MxD28C3 and VNP28C3) measurements for 164 large reservoirs (MxD stands for the product from both Terra (MOD) or Aqua (MYD) satellites). The 8-day product provides area, elevation, and storage values, which were generated by first extracting water areas from surface reflectance data and then applying the area estimations to the pre-established Area–Elevation (A–E) relationships. These values were then further aggregated to monthly, with the evaporation rate and volume information added. The evaporation rate and volume values were calculated after the Lake Temperature and Evaporation Model (LTEM) using MODIS/VIIRS land surface temperature product and meteorological data from the Global Land Data Assimilation System (GLDAS). Validation results show that the 250 m area classifications from MODIS agree well with the high-resolution classifications from Landsat (R2 = 0.99). Validation of elevation and storage products for twelve Indian reservoirs show good agreement in terms of R2 values (0.71–0.96 for elevation, and 0.79–0.96 for storage) and normalized root-mean-square error (NRMSE) values (5.08–19.34% for elevation, and 6.39–18.77% for storage). The evaporation rate results for two reservoirs (Lake Nasser and Lake Mead) agree well with in situ measurements (R2 values of 0.61 and 0.66, and NRMSE values of 16.25% and 21.76%). Furthermore, preliminary results from the VIIRS reservoir product have shown good consistency with the MODIS based product, confirming the continuity of this 20-year product suite. This new global water reservoir product suite can provide valuable information with regard to water-sources-related studies, applications, management, and hydrological modeling and change analysis such as drought monitoring.
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Chu, Mike, Junqiang Sun, and Menghua Wang. "Performance Evaluation of On-Orbit Calibration of SNPP VIIRS Reflective Solar Bands via Intersensor Comparison with Aqua MODIS." Journal of Atmospheric and Oceanic Technology 35, no. 2 (2018): 385–403. http://dx.doi.org/10.1175/jtech-d-17-0008.1.
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AbstractAn intersensor comparison is carried out to evaluate the radiometric performance of the reflective solar bands (RSBs) of the first Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. Two versions of sensor data records (SDRs) for moderate-resolution RSBs M1–M8 (410–1238 nm)—one version from the NOAA Ocean Color (OC) Team and the operational version from the Interface Data Processing Segment (IDPS)—are compared against the well-calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite. This comparison fully exploits the moderate resolution of the sensors and a precise simultaneous nadir overpass (SNO) analysis in a “nadir only” approach to achieve a precision better than 1%. The key issues found to impact the SNO analysis are 1) an underlying bias beyond the 80-km spatial scale, 2) a scene-based sporadic variability of about 2% affecting the sample size selection criteria, and 3) large relative deviations at low radiances. It is shown that the OC SDRs achieve significantly better agreement with Aqua MODIS, such as smaller temporal variation, improved agreement in the early mission, and no observable long-term drift. The lone exception is the downward drift of about 1% in the Aqua MODIS band 8 (412 nm) versus SNPP VIIRS band M1 time series that possibly started in late 2013, which is ultimately attributed to errors in Aqua MODIS band 8. Finally, the long-term drift in the IDPS SDRs further illustrates the consequence of the worsening bias within the standard RSB calibration that will infect any versions of the VIIRS SDRs not mitigated for this error.
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Nuris, Rayhan, Jonson Lumban Gaol, and Teguh Prayogo. "CHLOROPHYLL-A CONCENTRATIONS ESTIMATION FROM AQUA-MODIS AND VIIRS-NPP SATELLITE SENSORS IN SOUTH JAVA SEA WATERS." International Journal of Remote Sensing and Earth Sciences (IJReSES) 12, no. 1 (2017): 63. http://dx.doi.org/10.30536/j.ijreses.2015.v12.a2673.
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This study aimed to estimate the concentration of chlorophyll-a from satellite imagery of National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) in the south Java Sea waters and compare it to the concentrations of chlorophyll-a estimation result from the MODIS-Aqua satellite. NPP satellite had Visible/Infrared Imager Radiometer Suite (VIIRS) sensors which performance was same as Moderate Resolution Imaging Spectroradiometer (MODIS) sensor with a better spatial resolution. This study used daily satellite imagery of VIIRS-NPP for the period of September 2012 to August 2013. The algorithm that was used to estimate the concentration of chlorophyll-a was Ocean Color 3-band ratio (OC-3). The results showed that the spatial distribution pattern of chlorophyll-a concentration between VIIRS - NPP sensor and MODIS had the same pattern, but the estimation of chlorophyll-a concentration from the MODIS sensor was higher than VIIRS -NPP sensor. The concentration of chlorophyll-a showed that there were spatial and temporal variation in the south Java Sea waters. Generally, concentrations of chlorophyll-a was higher in East monsoon than West monsoon.
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Dickinson, Matthew B., Andrew T. Hudak, Thomas Zajkowski, et al. "Measuring radiant emissions from entire prescribed fires with ground, airborne and satellite sensors – RxCADRE 2012." International Journal of Wildland Fire 25, no. 1 (2016): 48. http://dx.doi.org/10.1071/wf15090.
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Characterising radiation from wildland fires is an important focus of fire science because radiation relates directly to the combustion process and can be measured across a wide range of spatial extents and resolutions. As part of a more comprehensive set of measurements collected during the 2012 Prescribed Fire Combustion and Atmospheric Dynamics Research (RxCADRE) field campaign, we used ground, airborne and spaceborne sensors to measure fire radiative power (FRP) from whole fires, applying different methods to small (2 ha) and large (>100 ha) burn blocks. For small blocks (n = 6), FRP estimated from an obliquely oriented long-wave infrared (LWIR) camera mounted on a boom lift were compared with FRP derived from combined data from tower-mounted radiometers and remotely piloted aircraft systems (RPAS). For large burn blocks (n = 3), satellite FRP measurements from the Moderate-resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors were compared with near-coincident FRP measurements derived from a LWIR imaging system aboard a piloted aircraft. We describe measurements and consider their strengths and weaknesses. Until quantitative sensors exist for small RPAS, their use in fire research will remain limited. For oblique, airborne and satellite sensors, further FRP measurement development is needed along with greater replication of coincident measurements, which we show to be feasible.
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Ichoku, C., and L. Ellison. "Global top-down smoke-aerosol emissions estimation using satellite fire radiative power measurements." Atmospheric Chemistry and Physics 14, no. 13 (2014): 6643–67. http://dx.doi.org/10.5194/acp-14-6643-2014.
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Abstract. Fire emissions estimates have long been based on bottom-up approaches that are not only complex, but also fraught with compounding uncertainties. We present the development of a global gridded (1° × 1°) emission coefficients (Ce) product for smoke total particulate matter (TPM) based on a top-down approach using coincident measurements of fire radiative power (FRP) and aerosol optical thickness (AOT) from the Moderate-resolution Imaging Spectro-radiometer (MODIS) sensors aboard the Terra and Aqua satellites. This new Fire Energetics and Emissions Research version 1.0 (FEER.v1) Ce product has now been released to the community and can be obtained from
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Sawyer, Virginia, Robert C. Levy, Shana Mattoo, Geoff Cureton, Yingxi Shi, and Lorraine A. Remer. "Continuing the MODIS Dark Target Aerosol Time Series with VIIRS." Remote Sensing 12, no. 2 (2020): 308. http://dx.doi.org/10.3390/rs12020308.
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For reflected sunlight observed from space at visible and near-infrared wavelengths, particles suspended in Earth’s atmosphere provide contrast with vegetation or dark water at the surface. This is the physical motivation for the Dark Target (DT) aerosol retrieval algorithm developed for the Moderate Resolution Imaging Spectrometer (MODIS). To extend the data record of aerosol optical depth (AOD) beyond the expected 20-year lifespan of the MODIS sensors, DT must be adapted for other sensors. A version of the DT AOD retrieval for the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar-Orbiting Partnership (SNPP) is now mature enough to be released as a standard data product, and includes some upgraded features from the MODIS version. Differences between MODIS Aqua and VIIRS SNPP lead to some inevitable disagreement between their respective AOD measurements, but the offset between the VIIRS SNPP and MODIS Aqua records is smaller than the offset between those of MODIS Aqua and MODIS Terra. The VIIRS SNPP retrieval shows good agreement with ground-based measurements. For most purposes, DT for VIIRS SNPP is consistent enough and in close enough agreement with MODIS to continue the record of satellite AOD. The reasons for the offset from MODIS Aqua, and its spatial and temporal variability, are investigated in this study.
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Yan, Hao, and Song Yang. "A MODIS Dual Spectral Rain Algorithm." Journal of Applied Meteorology and Climatology 46, no. 9 (2007): 1305–23. http://dx.doi.org/10.1175/jam2541.1.
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Abstract The Moderate Resolution Imaging Spectroradiometer (MODIS) dual spectral rain algorithm (MODRA) is developed for rain retrievals over the northern midlatitudes. The reflectance of the MODIS water vapor absorption channel at 1.38 μm (R1.38 μm) has a potential to represent the cloud-top height displayed by the brightness temperature (TB) of the MODIS channel at 11 μm, because of an excellent negative relationship (correlation coefficient ≤−0.9) between R1.38 μm and TB11 μm for optically thick clouds with reflectance (R0.65 μm) greater than 0.75. With a training rainfall dataset from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) aboard the same Aqua satellite platform, two MODIS channels (R1.38 μm and R0.65 μm) are applied to form multiregression curves to estimate daytime rainfall. Results demonstrate that the instantaneous rain rates from MODRA, independent AMSR-E rainfall products, and surface rain gauge measurements are consistent. This study explores a new way to estimate rainfall from MODIS water vapor and cloud channels. The resulting technique could be applied to other similar satellite instruments for rain retrievals.
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Patadia, Falguni, Robert C. Levy, and Shana Mattoo. "Correcting for trace gas absorption when retrieving aerosol optical depth from satellite observations of reflected shortwave radiation." Atmospheric Measurement Techniques 11, no. 6 (2018): 3205–19. http://dx.doi.org/10.5194/amt-11-3205-2018.
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Abstract. Retrieving aerosol optical depth (AOD) from top-of-atmosphere (TOA) satellite-measured radiance requires separating the aerosol signal from the total observed signal. Total TOA radiance includes signal from the underlying surface and from atmospheric constituents such as aerosols, clouds and gases. Multispectral retrieval algorithms, such as the dark-target (DT) algorithm that operates upon the Moderate Resolution Imaging Spectroradiometer (MODIS, on board Terra and Aqua satellites) and Visible Infrared Imaging Radiometer Suite (VIIRS, on board Suomi-NPP) sensors, use wavelength bands in “window” regions. However, while small, the gas absorptions in these bands are non-negligible and require correction. In this paper, we use the High-resolution TRANsmission (HITRAN) database and Line-By-Line Radiative Transfer Model (LBLRTM) to derive consistent gas corrections for both MODIS and VIIRS wavelength bands. Absorptions from H2O, CO2 and O3 are considered, as well as other trace gases. Even though MODIS and VIIRS bands are “similar”, they are different enough that applying MODIS-specific gas corrections to VIIRS observations results in an underestimate of global mean AOD (by 0.01), but with much larger regional AOD biases of up to 0.07. As recent studies have been attempting to create a long-term data record by joining multiple satellite data sets, including MODIS and VIIRS, the consistency of gas correction has become even more crucial.
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Fu, Yuyun, Rui Li, Xuewen Wang, et al. "Fire Detection and Fire Radiative Power in Forests and Low-Biomass Lands in Northeast Asia: MODIS versus VIIRS Fire Products." Remote Sensing 12, no. 18 (2020): 2870. http://dx.doi.org/10.3390/rs12182870.
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Fire omission and commission errors, and the accuracy of fire radiative power (FRP) from satellite moderate-resolution impede the studies on fire regimes and FRP-based fire emissions estimation. In this study, we compared the accuracy between the extensively used 1-km fire product of MYD14 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the 375-m fire product of VNP14IMG from the Visible Infrared Imaging Radiometer Suite (VIIRS) in Northeastern Asia using data from 2012–2017. We extracted almost simultaneous observation of fire detection and FRP from MODIS-VIIRS overlapping orbits from the two fire products, and identified and removed duplicate fire detections and corresponding FRP in each fire product. We then compared the performance of the two products between forests and low-biomass lands (croplands, grasslands, and herbaceous vegetation). Among fire pixels detected by VIIRS, 65% and 83% were missed by MODIS in forests and low-biomass lands, respectively; whereas associated omission rates by VIIRS for MODIS fire pixels were 35% and 53%, respectively. Commission errors of the two fire products, based on the annual mean measurements of burned area by Landsat, decreased with increasing FRP per fire pixel, and were higher in low-biomass lands than those in forests. Monthly total FRP from MODIS was considerably lower than that from VIIRS due to more fire omission by MODIS, particularly in low-biomass lands. However, for fires concurrently detected by both sensors, total FRP was lower with VIIRS than with MODIS. This study contributes to a better understanding of fire detection and FRP retrieval performance between MODIS and its successor VIIRS, providing valuable information for using those data in the study of fire regimes and FRP-based fire emission estimation.
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Riggs, George A., Dorothy K. Hall, and Miguel O. Román. "Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records." Earth System Science Data 9, no. 2 (2017): 765–77. http://dx.doi.org/10.5194/essd-9-765-2017.
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Abstract. Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.
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Mei, L., Y. Xue, A. A. Kokhanovsky, W. von Hoyningen-Huene, G. de Leeuw, and J. P. Burrows. "Retrieval of aerosol optical depth over land surfaces from AVHRR data." Atmospheric Measurement Techniques Discussions 6, no. 1 (2013): 2227–51. http://dx.doi.org/10.5194/amtd-6-2227-2013.
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Abstract. The Advanced Very High Resolution Radiometer (AVHRR) radiance data provide a global, long-term, consistent time series having high spectral and spatial resolution and thus being valuable for the retrieval of surface spectral reflectance, albedo and surface temperature. Long term time series of such data products are necessary for studies addressing climate change, sea ice distribution and movement, and ice sheet coastal configuration. These data have also been used to retrieve aerosol properties over ocean and land surfaces. However, the retrieval of aerosol over land and land surface albedo are challenging because of the information content of the measurement is limited and the inversion of these data products being ill defined. Solving the radiative transfer equations requires additional information and knowledge to reduce the number of unknowns. In this contribution we utilise an empirical linear relationship between the surface reflectances in the AVHRR channels at wavelengths of 3.75 μm and 2.1 μm, which has been identified in Moderate Resolution Imaging Spectroradiometer (MODIS) data. Next, following the MODIS dark target approach, the surface reflectance at 0.64 μm was obtained. The comparison of the estimated surface reflectance at 0.64 μm with MODIS reflectance products (MOD09) shows a strong correlation (R = 0.7835). Once this was established, the MODIS "dark-target" aerosol retrieval method was adapted to Advanced Very High Resolution Radiometer (AVHRR) data. A simplified Look-Up Table (LUT) method, adopted from Bremen AErosol Retrieval (BAER) algorithm, was used in the retrieval. The Aerosol Optical Depth (AOD) values retrieved from AVHRR with this method compare favourably with ground-based measurements, with a correlation coefficient R = 0.861 and Root Mean Square Error (RMSE) = 0.17. This method can be easily applied to other satellite instruments which do not have a 2.1 μm channel, such as those currently planned to geostationary satellites.
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Hillger, Donald, Thomas Kopp, Thomas Lee, et al. "First-Light Imagery from Suomi NPP VIIRS." Bulletin of the American Meteorological Society 94, no. 7 (2013): 1019–29. http://dx.doi.org/10.1175/bams-d-12-00097.1.
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The Suomi National Polar-Orbiting Partnership (NPP) satellite was launched on 28 October 2011, heralding the next generation of operational U.S. polar-orbiting satellites. It carries the Visible– Infrared Imaging Radiometer Suite (VIIRS), a 22-band visible/infrared sensor that combines many of the best aspects of the NOAA Advanced Very High Resolution Radiometer (AVHRR), the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. VIIRS has nearly all the capabilities of MODIS, but offers a wider swath width (3,000 versus 2,330 km) and much higher spatial resolution at swath edge. VIIRS also has a day/night band (DNB) that is sensitive to very low levels of visible light at night such as those produced by moonlight reflecting off low clouds, fog, dust, ash plumes, and snow cover. In addition, VIIRS detects light emissions from cities, ships, oil flares, and lightning flashes. NPP crosses the equator at about 0130 and 1330 local time, with VIIRS covering the entire Earth twice daily. Future members of the Joint Polar Satellite System (JPSS) constellation will also carry VIIRS. This paper presents dramatic early examples of multispectral VIIRS imagery capabilities and demonstrates basic applications of that imagery for a wide range of operational users, such as for fire detection, monitoring ice break up in rivers, and visualizing dust plumes over bright surfaces. VIIRS imagery, both single and multiband, as well as the day/night band, is shown to exceed both requirements and expectations.
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Marais, Willem J., Robert E. Holz, Jeffrey S. Reid, and Rebecca M. Willett. "Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations." Atmospheric Measurement Techniques 13, no. 10 (2020): 5459–80. http://dx.doi.org/10.5194/amt-13-5459-2020.
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Abstract. Current cloud and aerosol identification methods for multispectral radiometers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), employ multichannel spectral tests on individual pixels (i.e., fields of view). The use of the spatial information in cloud and aerosol algorithms has been primarily through statistical parameters such as nonuniformity tests of surrounding pixels with cloud classification provided by the multispectral microphysical retrievals such as phase and cloud top height. With these methodologies there is uncertainty in identifying optically thick aerosols, since aerosols and clouds have similar spectral properties in coarse-spectral-resolution measurements. Furthermore, identifying clouds regimes (e.g., stratiform, cumuliform) from just spectral measurements is difficult, since low-altitude cloud regimes have similar spectral properties. Recent advances in computer vision using deep neural networks provide a new opportunity to better leverage the coherent spatial information in multispectral imagery. Using a combination of machine learning techniques combined with a new methodology to create the necessary training data, we demonstrate improvements in the discrimination between cloud and severe aerosols and an expanded capability to classify cloud types. The labeled training dataset was created from an adapted NASA Worldview platform that provides an efficient user interface to assemble a human-labeled database of cloud and aerosol types. The convolutional neural network (CNN) labeling accuracy of aerosols and cloud types was quantified using independent Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and MODIS cloud and aerosol products. By harnessing CNNs with a unique labeled dataset, we demonstrate the improvement of the identification of aerosols and distinct cloud types from MODIS and VIIRS images compared to a per-pixel spectral and standard deviation thresholding method. The paper concludes with case studies that compare the CNN methodology results with the MODIS cloud and aerosol products.
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Hall, Dorothy K., George A. Riggs, Nicolo E. DiGirolamo, and Miguel O. Román. "Evaluation of MODIS and VIIRS cloud-gap-filled snow-cover products for production of an Earth science data record." Hydrology and Earth System Sciences 23, no. 12 (2019): 5227–41. http://dx.doi.org/10.5194/hess-23-5227-2019.
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Abstract. MODerate resolution Imaging Spectroradiometer (MODIS) cryosphere products have been available since 2000 – following the 1999 launch of the Terra MODIS and the 2002 launch of the Aqua MODIS – and include global snow-cover extent (SCE) (swath, daily, and 8 d composites) at 500 m and ∼5 km spatial resolutions. These products are used extensively in hydrological modeling and climate studies. Reprocessing of the complete snow-cover data record, from Collection 5 (C5) to Collection 6 (C6) and Collection 6.1 (C6.1), has provided improvements in the MODIS product suite. Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products at a 375 m spatial resolution have been available since 2011 and are currently being reprocessed for Collection 2 (C2). Both the MODIS C6.1 and the VIIRS C2 products will be available for download from the National Snow and Ice Data Center beginning in early 2020 with the complete time series available in 2020. To address the need for a cloud-reduced or cloud-free daily SCE product for both MODIS and VIIRS, a daily cloud-gap-filled (CGF) snow-cover algorithm was developed for MODIS C6.1 and VIIRS C2 processing. MOD10A1F (Terra) and MYD10A1F (Aqua) are daily, 500 m resolution CGF SCE map products from MODIS. VNP10A1F is the daily, 375 m resolution CGF SCE map product from VIIRS. These CGF products include quality-assurance data such as cloud-persistence statistics showing the age of the observation in each pixel. The objective of this paper is to introduce the new MODIS and VIIRS standard CGF daily SCE products and to provide a preliminary evaluation of uncertainties in the gap-filling methodology so that the products can be used as the basis for a moderate-resolution Earth science data record (ESDR) of SCE. Time series of the MODIS and VIIRS CGF products have been developed and evaluated at selected study sites in the US and southern Canada. Observed differences, although small, are largely attributed to cloud masking and differences in the time of day of image acquisition. A nearly 3-month time-series comparison of Terra MODIS and S-NPP VIIRS CGF snow-cover maps for a large study area covering all or parts of 11 states in the western US and part of southwestern Canada reveals excellent correspondence between the Terra MODIS and S-NPP VIIRS products, with a mean difference of 11 070 km2, which is ∼0.45 % of the study area. According to our preliminary validation of the Terra and Aqua MODIS CGF SCE products in the western US study area, we found higher accuracy of the Terra product compared with the Aqua product. The MODIS CGF SCE data record beginning in 2000 has been extended into the VIIRS era, which should last at least through the early 2030s.
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Sporre, Moa K., Ewan J. O'Connor, Nina Håkansson, Anke Thoss, Erik Swietlicki, and Tuukka Petäjä. "Comparison of MODIS and VIIRS cloud properties with ARM ground-based observations over Finland." Atmospheric Measurement Techniques 9, no. 7 (2016): 3193–203. http://dx.doi.org/10.5194/amt-9-3193-2016.
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Abstract. Cloud retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the satellites Terra and Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument aboard the Suomi-NPP satellite are evaluated using a combination of ground-based instruments providing vertical profiles of clouds. The ground-based measurements are obtained from the Atmospheric Radiation Measurement (ARM) programme mobile facility, which was deployed in Hyytiälä, Finland, between February and September 2014 for the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) campaign. The satellite cloud parameters cloud top height (CTH) and liquid water path (LWP) are compared with ground-based CTH obtained from a cloud mask created using lidar and radar data and LWP acquired from a multi-channel microwave radiometer. Clouds from all altitudes in the atmosphere are investigated. The clouds are diagnosed as single or multiple layer using the ground-based cloud mask. For single-layer clouds, satellites overestimated CTH by 326 m (14 %) on average. When including multilayer clouds, satellites underestimated CTH by on average 169 m (5.8 %). MODIS collection 6 overestimated LWP by on average 13 g m−2 (11 %). Interestingly, LWP for MODIS collection 5.1 is slightly overestimated by Aqua (4.56 %) but is underestimated by Terra (14.3 %). This underestimation may be attributed to a known issue with a drift in the reflectance bands of the MODIS instrument on Terra. This evaluation indicates that the satellite cloud parameters selected show reasonable agreement with their ground-based counterparts over Finland, with minimal influence from the large solar zenith angle experienced by the satellites in this high-latitude location.
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Lacava, Teodosio, Emanuele Ciancia, Mariapia Faruolo, Nicola Pergola, Valeria Satriano, and Valerio Tramutoli. "On the Potential of RST-FLOOD on Visible Infrared Imaging Radiometer Suite Data for Flooded Areas Detection." Remote Sensing 11, no. 5 (2019): 598. http://dx.doi.org/10.3390/rs11050598.
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Timely and continuous information about flood spatiotemporal evolution are fundamental to ensure an effective implementation of the relief and rescue operations in case of inundation events. In this framework, satellite remote sensing may provide a valuable contribution provided that robust data analysis methods are implemented and suitable data, in terms of spatial, spectral and temporal resolutions, are employed. In this paper, the Robust Satellite Techniques (RST) approach, a satellite-based differential approach, already applied at detecting flooded areas (and therefore christened RST-FLOOD) with good results on different polar orbiting optical sensors (i.e., Advanced Very High Resolution Radiometer – AVHRR – and Moderate Resolution Imaging Spectroradiometer – MODIS), has been fully implemented on time series of Suomi National Polar-orbiting Partnership (Suomi-NPP-SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) data. The flooding event affecting the Metaponto Plain in Basilicata and Puglia regions (southern Italy) in December 2013 was selected as a case study and investigated by analysing five years (only December month) of VIIRS Imagery bands at 375 m spatial resolution. The achieved results clearly indicate the potential of the proposed approach, especially when compared with a satellite-based high resolution map of flooded area, as well as with the official flood hazard map of the area and the outputs of a recent published VIIRS-based method. Both flood extent and dynamics have been recognized with good reliability during the investigated period, with only a residual 11.5% of possible false positives over an inundated area extent of about 73 km2. In addition, a flooded area of about 18 km2 was found outside the hazard map, suggesting it requires updating to better manage flood risk and prevent future damages. Finally, the achieved results indicate that medium-resolution optical data, if analysed with robust methodologies like RST-FLOOD, can be suitable for detecting and monitoring floods also in case of small hydrological basins.
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Xiao, Q., H. Zhang, M. Choi, et al. "Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer measurements over East Asia." Atmospheric Chemistry and Physics Discussions 15, no. 15 (2015): 20709–41. http://dx.doi.org/10.5194/acpd-15-20709-2015.
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Abstract. Persistent high aerosol loadings together with extremely high population density have raised serious air quality and public health concerns in many urban centers in East Asia. However, ground based air quality monitoring is relatively limited in this area. Recently, satellite retrieved Aerosol Optical Depth (AOD) at high resolution has become a powerful tool to characterize aerosol patterns in space and time. Using ground AOD measurements from the Aerosol Robotic Network (AERONET) and the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-Asia Campaign, as well as from handheld sunphotometers, we evaluated emerging aerosol products from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP), the Geostationary Ocean Color Imager (GOCI) aboard the Communication, Ocean, and Meteorology Satellite (COMS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) (Collection 6) in East Asia in 2012 and 2013. In the case study in Beijing, when compared with AOD measured by handheld sunphotometers, 51 % of VIIRS Environmental Data Record (EDR) AOD, 33 % of VIIRS Intermediate Product (IP) AOD, 31 % of GOCI AOD, 26 % of Terra MODIS C6 3 km AOD, and 16 % of Aqua MODIS C6 3 km AOD fell within the reference expected error (EE) envelop (±0.05 ± 0.15 AOD). Comparing against AERONET measurements over the Japan–South Korea region, 64 % of EDR, 37 % of IP, 62 % of GOCI, 39 % of Terra MODIS and 56 % of Aqua MODIS C6 3 km AOD fell within the EE. In general, satellite aerosol products performed better in tracking the day-to-day variability than tracking the spatial variability at high resolutions. The VIIRS EDR and GOCI products provided the most accurate AOD retrievals, while VIIRS IP and MODIS C6 3 km products had positive biases.
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Xiao, Q., H. Zhang, M. Choi, et al. "Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer observations over East Asia." Atmospheric Chemistry and Physics 16, no. 3 (2016): 1255–69. http://dx.doi.org/10.5194/acp-16-1255-2016.
Full textAbstract:
Abstract. Persistent high aerosol loadings together with extremely high population densities have raised serious air quality and public health concerns in many urban centers in East Asia. However, ground-based air quality monitoring is relatively limited in this area. Recently, satellite-retrieved Aerosol Optical Depth (AOD) at high resolution has become a powerful tool to characterize aerosol patterns in space and time. Using ground AOD observations from the Aerosol Robotic Network (AERONET) and the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-Asia Campaign, as well as from handheld sunphotometers, we evaluated emerging aerosol products from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP), the Geostationary Ocean Color Imager (GOCI) aboard the Communication, Ocean, and Meteorology Satellite (COMS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) (Collection 6) in East Asia in 2012 and 2013. In the case study in Beijing, when compared with AOD observations from handheld sunphotometers, 51 % of VIIRS Environmental Data Record (EDR) AOD, 37 % of GOCI AOD, 33 % of VIIRS Intermediate Product (IP) AOD, 26 % of Terra MODIS C6 3 km AOD, and 16 % of Aqua MODIS C6 3 km AOD fell within the reference expected error (EE) envelope (±0.05 ± 0.15 AOD). Comparing against AERONET AOD over the Japan–South Korea region, 64 % of EDR, 37 % of IP, 61 % of GOCI, 39 % of Terra MODIS, and 56 % of Aqua MODIS C6 3 km AOD fell within the EE. In general, satellite aerosol products performed better in tracking the day-to-day variability than tracking the spatial variability at high resolutions. The VIIRS EDR and GOCI products provided the most accurate AOD retrievals, while VIIRS IP and MODIS C6 3 km products had positive biases.
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Frey, Richard A., Steven A. Ackerman, Yinghui Liu, et al. "Cloud Detection with MODIS. Part I: Improvements in the MODIS Cloud Mask for Collection 5." Journal of Atmospheric and Oceanic Technology 25, no. 7 (2008): 1057–72. http://dx.doi.org/10.1175/2008jtecha1052.1.
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Abstract Significant improvements have been made to the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask (MOD35 and MYD35) for Collection 5 reprocessing and forward stream data production. Most of the modifications are realized for nighttime scenes where polar and oceanic regions will see marked improvement. For polar night scenes, two new spectral tests using the 7.2-μm water vapor absorption band have been added as well as updates to the 3.9–12- and 11–12-μm cloud tests. More non-MODIS ancillary input data have been added. Land and sea surface temperature maps provide crucial information for mid- and low-level cloud detection and lessen dependence on ocean brightness temperature variability tests. Sun-glint areas are also improved by use of sea surface temperatures to aid in resolving observations with conflicting cloud versus clear-sky signals, where visible and near-infrared (NIR) reflectances are high, but infrared brightness temperatures are relatively warm. Day and night Arctic cloud frequency results are compared to those created by the Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder-Extended (APP-X) algorithm. Day versus night sea surface temperatures derived from MODIS radiances and using only the MODIS cloud mask for cloud screening are contrasted. Frequencies of cloud from sun-glint regions are shown as a function of sun-glint angle to gain a sense of cloud mask quality in those regions. Continuing validation activities are described in Part II of this paper.
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Tran, Ngoc Nguyen, Alfredo Huete, Ha Nguyen, et al. "Seasonal Comparisons of Himawari-8 AHI and MODIS Vegetation Indices over Latitudinal Australian Grassland Sites." Remote Sensing 12, no. 15 (2020): 2494. http://dx.doi.org/10.3390/rs12152494.
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The Advanced Himawari Imager (AHI) on board the Himawari-8 geostationary (GEO) satellite offers comparable spectral and spatial resolutions as low earth orbiting (LEO) sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors, but with hypertemporal image acquisition capability. This raises the possibility of improved monitoring of highly dynamic ecosystems, such as grasslands, including fine-scale phenology retrievals from vegetation index (VI) time series. However, identifying and understanding how GEO VI temporal profiles would be different from traditional LEO VIs need to be evaluated, especially with the new generation of geostationary satellites, with unfamiliar observation geometries not experienced with MODIS, VIIRS, or Advanced Very High Resolution Radiometer (AVHRR) VI time series data. The objectives of this study were to investigate the variations in AHI reflectances and normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and two-band EVI (EVI2) in relation to diurnal phase angle variations, and to compare AHI VI seasonal datasets with MODIS VIs (standard and sun and view angle-adjusted VIs) over a functional range of dry grassland sites in eastern Australia. Strong NDVI diurnal variations and negative NDVI hotspot effects were found due to differential red and NIR band sensitivities to diurnal phase angle changes. In contrast, EVI and EVI2 were nearly insensitive to diurnal phase angle variations and displayed nearly flat diurnal profiles without noticeable hotspot influences. At seasonal time scales, AHI NDVI values were consistently lower than MODIS NDVI values, while AHI EVI and EVI2 values were significantly higher than MODIS EVI and EVI2 values, respectively. We attributed the cross-sensor differences in VI patterns to the year-round smaller phase angles and backscatter observations from AHI, in which the sunlit canopies induced a positive EVI/ EVI2 response and negative NDVI response. BRDF adjustments of MODIS VIs to solar noon and to the oblique view zenith angle of AHI resulted in strong cross-sensor convergence of VI values (R2 > 0.94, mean absolute difference <0.02). These results highlight the importance of accounting for cross-sensor observation geometries for generating compatible AHI and MODIS annual VI time series. The strong agreement found in this study shows promise in cross-sensor applications and suggests that a denser time series can be formed through combined GEO and LEO measurement synergies.
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Valenti, Michael. "Tracking Africa’s Inferno." Mechanical Engineering 122, no. 12 (2000): 66–70. http://dx.doi.org/10.1115/1.2000-dec-6.
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This article focuses on instruments aboard an orbiting satellite and high-flying aircraft study grass fires that straddle a continent. NASA designed its $1.3 billion Terra to be the flagship in a new series of Earth-observing satellites that will study phenomena affecting the climate. The instruments carried by Terra that were most active during the Safari 2000 field experiment were Moderate-Resolution Imaging Spectro-Radiometer (MODIS), Multi-Angle Imaging Spectro-Radiometer (MISR), and Measurements of Pollution in the Troposphere (MOPITT). MOPITT accomplishes its mission by using gas correlation spectroscopy to measure rising and reflected infrared radiance in three absorption bands of carbon monoxide and methane. The Terra’s Safari 2000 observations were augmented by measurements taken by instruments aboard several aircraft, including the high-altitude Lockheed-Martin ER-2 that NASA flew from Pietersburg, South Africa, as part of the African field experiment. The South African Weather Bureau contributed two Aerocommander 690A aircraft to Safari 2000. One of the twin-engine, turboprop planes was used for aerosol research, while the other one helped validate the carbon monoxide measurements obtained by MOPITT.
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Walther, Andi, and Andrew K. Heidinger. "Implementation of the Daytime Cloud Optical and Microphysical Properties Algorithm (DCOMP) in PATMOS-x." Journal of Applied Meteorology and Climatology 51, no. 7 (2012): 1371–90. http://dx.doi.org/10.1175/jamc-d-11-0108.1.
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AbstractThis paper describes the daytime cloud optical and microphysical properties (DCOMP) retrieval for the Pathfinder Atmosphere’s Extended (PATMOS-x) climate dataset. Within PATMOS-x, DCOMP is applied to observations from the Advanced Very High Resolution Radiometer and employs the standard bispectral approach to estimate cloud optical depth and particle size. The retrievals are performed within the optimal estimation framework. Atmospheric-correction and forward-model parameters, such as surface albedo and gaseous absorber amounts, are obtained from numerical weather prediction reanalysis data and other climate datasets. DCOMP is set up to run on sensors with similar channel settings and has been successfully exercised on most current meteorological imagers. This quality makes DCOMP particularly valuable for climate research. Comparisons with the Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 dataset are used to estimate the performance of DCOMP.
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Rosenfeld, D., G. Liu, X. Yu, et al. "High resolution (375 m) cloud microstructure as seen from the NPP/VIIRS Satellite imager." Atmospheric Chemistry and Physics Discussions 13, no. 11 (2013): 29845–94. http://dx.doi.org/10.5194/acpd-13-29845-2013.
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Abstract. The VIIRS (Visible Infrared Imaging Radiometer Suite) onboard the Suomi NPP (National Polar-Orbiting Partnership) satellite has improved resolution of 750 m with respect to 1000 m of the MODerate-resolution Imaging Spectroradiometer, for the channels that allow retrieving cloud microphysical parameters such as cloud drop effective radius (re). The VIIRS has also an imager with 5 channels of double resolution of 375 m, which was not designed for retrieving cloud products. A methodology for a high resolution retrieval of re and microphysical presentation of the cloud field based on the VIIRS imager was developed and evaluated with respect to MODIS in this study. The tripled microphysical resolution with respect to MODIS allows obtaining new insights for cloud aerosol interactions, especially at the smallest cloud scales, because the VIIRS imager can resolve the small convective elements that are sub-pixel for MODIS cloud products. Examples are given for new insights on ship tracks in marine stratocumulus, pollution tracks from point and diffused sources in stratocumulus and cumulus clouds over land, deep tropical convection in pristine air mass over ocean and land, tropical clouds that develop in smoke from forest fires and in heavy pollution haze over densely populated regions in southeast Asia, and for pyro-cumulonimbus clouds. It is found that the VIIRS imager provides more robust physical interpretation and refined information for cloud and aerosol microphysics as compared to MODIS, especially in the initial stage of cloud formation. VIIRS is found to identify much more full-cloudy pixels when small boundary layer convective elements are present. This, in turn, allows a better quantification of cloud aerosol interactions and impacts on precipitation forming processes.
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Rosenfeld, D., G. Liu, X. Yu, et al. "High-resolution (375 m) cloud microstructure as seen from the NPP/VIIRS satellite imager." Atmospheric Chemistry and Physics 14, no. 5 (2014): 2479–96. http://dx.doi.org/10.5194/acp-14-2479-2014.
Full textAbstract:
Abstract. VIIRS (Visible Infrared Imaging Radiometer Suite), onboard the Suomi NPP (National Polar-orbiting Partnership) satellite, has an improved resolution of 750 m with respect to the 1000 m of the Moderate Resolution Imaging Spectroradiometer for the channels that allow retrieving cloud microphysical parameters such as cloud drop effective radius (re). VIIRS also has an imager with five channels of double resolution of 375 m, which was not designed for retrieving cloud products. A methodology for a high-resolution retrieval of re and microphysical presentation of the cloud field based on the VIIRS imager was developed and evaluated with respect to MODIS in this study. The tripled microphysical resolution with respect to MODIS allows obtaining new insights for cloud–aerosol interactions, especially at the smallest cloud scales, because the VIIRS imager can resolve the small convective elements that are sub-pixel for MODIS cloud products. Examples are given for new insights into ship tracks in marine stratocumulus, pollution tracks from point and diffused sources in stratocumulus and cumulus clouds over land, deep tropical convection in pristine air mass over ocean and land, tropical clouds that develop in smoke from forest fires and in heavy pollution haze over densely populated regions in southeastern Asia, and for pyro-cumulonimbus clouds. It is found that the VIIRS imager provides more robust physical interpretation and refined information for cloud and aerosol microphysics as compared to MODIS, especially in the initial stage of cloud formation. VIIRS is found to identify significantly more fully cloudy pixels when small boundary layer convective elements are present. This, in turn, allows for a better quantification of cloud–aerosol interactions and impacts on precipitation-forming processes.