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Articoli di riviste sul tema "Water Remote sensing"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 28 gennaio 2023

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1

Moore, Gerald K. "Remote sensing in hydrology, remote sensing applications." Journal of Hydrology 131, no. 1-4 (February 1992): 388–89. http://dx.doi.org/10.1016/0022-1694(92)90228-n.

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2

KYO, Masanori. "Under water remote sensing." Journal of the Visualization Society of Japan 10, no. 37 (1990): 81–87. http://dx.doi.org/10.3154/jvs.10.81.

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3

Tang, Qiuhong, Huilin Gao, Hui Lu, and Dennis P. Lettenmaier. "Remote sensing: hydrology." Progress in Physical Geography: Earth and Environment 33, no. 4 (August 2009): 490–509. http://dx.doi.org/10.1177/0309133309346650.

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Satellite remote sensing is a viable source of observations of land surface hydrologic fluxes and state variables, particularly in regions where in situ networks are sparse. Over the last 10 years, the study of land surface hydrology using remote sensing techniques has advanced greatly with the launch of NASA’s Earth Observing System (EOS) and other research satellite platforms, and with the development of more sophisticated retrieval algorithms. Most of the constituent variables in the land surface water balance (eg, precipitation, evapotranspiration, snow and ice, soil moisture, and terrestr

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4

Mayani, Kaushikkumar R., and V. M. Patel V. M. Patel. "Relevance of Remote Sensing and GIS in Water Resoureces Engineering." Indian Journal of Applied Research 1, no. 11 (October 1, 2011): 50–51. http://dx.doi.org/10.15373/2249555x/aug2012/17.

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5

Trochim, E. D., A. Prakash, D. L. Kane, and V. E. Romanovsky. "Remote sensing of water tracks." Earth and Space Science 3, no. 3 (March 2016): 106–22. http://dx.doi.org/10.1002/2015ea000112.

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6

Zhang, Yunlin, Claudia Giardino, and Linhai Li. "Water Optics and Water Colour Remote Sensing." Remote Sensing 9, no. 8 (August 9, 2017): 818. http://dx.doi.org/10.3390/rs9080818.

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7

Menenti, M., and G. J. A. Nieuwenhuis. "Remote sensing in the water management practice." Netherlands Journal of Agricultural Science 34, no. 3 (August 1, 1986): 317–28. http://dx.doi.org/10.18174/njas.v34i3.16785.

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In this examination of the use of remote sensing in water management 3 case studies are presented. The first concerned water management in the eastern Netherlands and remote sensing was used to provide evapotranspiration values. The description of the hydrological conditions was markedly improved by combining remote sensing and hydrological model calculations. A case study in Argentina using Greenness Vegetation Index showed how remote sensing can be used to give data on irrigated area and crop type. In the third case study, remote sensing was used to investigate groundwater losses in a desert

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8

He, Ping, Xueya Chen, Yuanxing Cai, Yue Zhou, and Yan Chen. "Research Progress of Remote Sensing Technology in Lake Water Environment Monitoring in China." International Journal of Engineering and Technology 14, no. 2 (May 2022): 15–18. http://dx.doi.org/10.7763/ijet.2022.v14.1195.

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This paper analyzes the research progress of remote sensing technology in lake water environment monitoring in China in recent years, including the research progress of suspended matter concentration in water, the research progress of bloom characteristics and the research status of chlorophyll concentration A.Although great progress has been made in lake water environment monitoring, the use of remote sensing to capture the spectral characteristics of water remains to be strengthened. It is necessary to improve the lake remote sensing algorithm for long time series and large range.

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9

Sriharsha, Mr K. V., and Dr N. V. Rao. "Water Management Using Remote Sensing Techniques." CVR Journal of Science & Technology 4, no. 1 (June 1, 2013): 87–92. http://dx.doi.org/10.32377/cvrjst0417.

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10

Mishra, Deepak, Eurico D’Sa, and Sachidananda Mishra. "Preface: Remote Sensing of Water Resources." Remote Sensing 8, no. 2 (February 4, 2016): 115. http://dx.doi.org/10.3390/rs8020115.

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11

Jackson, Thomas. "Soil Water Modeling and Remote Sensing." IEEE Transactions on Geoscience and Remote Sensing GE-24, no. 1 (January 1986): 37–46. http://dx.doi.org/10.1109/tgrs.1986.289586.

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12

Schuster, Gregory L., Bing Lin, and Oleg Dubovik. "Remote sensing of aerosol water uptake." Geophysical Research Letters 36, no. 3 (February 2009): n/a. http://dx.doi.org/10.1029/2008gl036576.

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13

Karstens, U., C. Simmer, and E. Ruprecht. "Remote sensing of cloud liquid water." Meteorology and Atmospheric Physics 54, no. 1-4 (1994): 157–71. http://dx.doi.org/10.1007/bf01030057.

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14

van der Piepen, Heinz, and Roland Doerffer. "Remote sensing of substances in water." GeoJournal 24, no. 1 (May 1991): 27–48. http://dx.doi.org/10.1007/bf00213054.

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15

Li, Chengcai, Jietai Mao, Jianguo Li, and Qing Xia. "Remote sensing precipitable water with GPS." Chinese Science Bulletin 44, no. 11 (June 1999): 1041–45. http://dx.doi.org/10.1007/bf02886027.

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16

Calera, A. "Remote Sensing for Crop Water Management." Agrociencia 19, no. 3 (December 2015): 77. http://dx.doi.org/10.31285/agro.19.289.

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Abstract (sommario):

Advancements on Earth Observation science and technology in the last decades have made possible the operative use of dense time series of multispectral imagery at high spatial resolution [5-30 m] to monitor crop development across its growing season at a suitable scale. These time series of images, jointly with meteorological data are able to provide accurate maps of daily evapotranspiration and so crop water requirements by using the remote sensing-based approach crop coefficient, Kc, and reference evapotranspiration, ETo, where Kc is derived from spectral reflectances and ETo from meteorolog

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17

Schmugge, Thomas J., William P. Kustas, Jerry C. Ritchie, Thomas J. Jackson, and Al Rango. "Remote sensing in hydrology." Advances in Water Resources 25, no. 8-12 (August 2002): 1367–85. http://dx.doi.org/10.1016/s0309-1708(02)00065-9.

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18

Schultz, Gert A. "Remote sensing in hydrology." Journal of Hydrology 100, no. 1-3 (July 1988): 239–65. http://dx.doi.org/10.1016/0022-1694(88)90187-4.

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19

Drury, S. A., and D. A. Rothery. "Remote sensing(PS670)." Geocarto International 5, no. 4 (December 1990): 40. http://dx.doi.org/10.1080/10106049009354285.

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20

Lo, C. P. "Applied remote sensing." Geocarto International 1, no. 4 (January 1986): 60. http://dx.doi.org/10.1080/10106048609354071.

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21

Akhavi, M. S. "Remote sensing research." Geocarto International 3, no. 4 (December 1988): 66. http://dx.doi.org/10.1080/10106048809354185.

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22

Ahmad, Uzair, Arturo Alvino, and Stefano Marino. "A Review of Crop Water Stress Assessment Using Remote Sensing." Remote Sensing 13, no. 20 (October 17, 2021): 4155. http://dx.doi.org/10.3390/rs13204155.

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Abstract (sommario):

Currently, the world is facing high competition and market risks in improving yield, crop illness, and crop water stress. This could potentially be addressed by technological advancements in the form of precision systems, improvements in production, and through ensuring the sustainability of development. In this context, remote-sensing systems are fully equipped to address the complex and technical assessment of crop production, security, and crop water stress in an easy and efficient way. They provide simple and timely solutions for a diverse set of ecological zones. This critical review high

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23

Wu, Dan, and Liangyan Yang. "Water Extraction Based on Landsat Remote Sensing Image." International Journal of Education and Humanities 6, no. 1 (November 27, 2022): 155–57. http://dx.doi.org/10.54097/ijeh.v6i1.3082.

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With the rapid development of remote sensing information technology, the types of Earth observation products are increasingly diverse, and the spatial and temporal resolution of remote sensing images are greatly improved. In recent years, how to extract useful information from massive remote sensing data products is a hot issue in remote sensing geoscience research, among which the extraction of water information can be widely used in agricultural production, water resources protection and monitoring, disaster prevention and reduction and other applications. Based on the characteristics of rem

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24

Foresman, T. W., and T. B. Serpi. "Mandate for Remote Sensing Education and the Remote Sensing Core Curriculum." Geocarto International 14, no. 2 (June 1999): 81–85. http://dx.doi.org/10.1080/10106049908542109.

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25

Lei, Feng, You Yu, Daijun Zhang, Li Feng, Jinsong Guo, Yong Zhang, and Fang Fang. "Water remote sensing eutrophication inversion algorithm based on multilayer convolutional neural network." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5319–27. http://dx.doi.org/10.3233/jifs-189017.

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In recent years, with the rapid development of satellite technology, remote sensing inversion has been used as an important part of environmental monitoring. Remote sensing inversion has been prepared for large-scale water environment monitoring in the watershed that is difficult for the traditional water environment monitoring methods. This paper will discuss some shortcomings of traditional remote sensing inversion methods, and proposes a remote sensing inversion method based on convolutional neural network, which realizes large-scale remote sensing smart and automatic inversion monitoring o

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26

Cai, Wanyuan, Sana Ullah, Lei Yan, and Yi Lin. "Remote Sensing of Ecosystem Water Use Efficiency: A Review of Direct and Indirect Estimation Methods." Remote Sensing 13, no. 12 (June 18, 2021): 2393. http://dx.doi.org/10.3390/rs13122393.

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Water use efficiency (WUE) is a key index for understanding the ecosystem of carbon–water coupling. The undistinguishable carbon–water coupling mechanism and uncertainties of indirect methods by remote sensing products and process models render challenges for WUE remote sensing. In this paper, current progress in direct and indirect methods of WUE estimation by remote sensing is reviewed. Indirect methods based on gross primary production (GPP)/evapotranspiration (ET) from ground observation, processed models and remote sensing are the main ways to estimate WUE in which carbon and water cycles

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27

Saeid, Ahmed Ayad Alfaytouri. "Remote Sensing in Water Quality and Water Resources Management." International Journal for Research in Applied Sciences and Biotechnology 9, no. 1 (February 21, 2022): 163–70. http://dx.doi.org/10.31033/ijrasb.9.1.19.

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The quality of water ascertains the ‘integrity’ of water for specific purposes. Tests and quality of examination of water can provide sufficient information about the waterway health. If tests are conducted over a span of time period, the water quality changes can be realized. There are several testing parameters like pH value, temperature, salinity, turbidity, phosphates and nitrates, which can help assess the water quality. Also, aquatic macro-invertebrates can give a proper water quality indication. Surface water contaminated can pose a high risk to the entire human population and it

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28

Hu, Yu. "Water Status and Prospects for Remote Sensing." Advanced Materials Research 853 (December 2013): 363–66. http://dx.doi.org/10.4028/www.scientific.net/amr.853.363.

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Water Information mainly using digital photogrammetric, remote sensing , telemetry and other digital technologies and equipment acquisition variety of water infrastructure data can also be used digitizer or scanning technology to non-digital information digital, this thesis summarizes the use of databases, spatial database and data warehouse technology to store and organize data , high-speed data communications network to transfer data , data mining and artificial intelligence technologies such as processing, use and dissemination of data . These results show that the effective application of

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29

Moreira, Adriana Aparecida, Alice César Fassoni-Andrade, Anderson Luis Ruhoff, and Rodrigo Cauduro Dias de Paiva. "REMOTE SENSING OF WATER BALANCE IN PANTANAL." Raega - O Espaço Geográfico em Análise 46, no. 3 (August 28, 2019): 20. http://dx.doi.org/10.5380/raega.v46i3.67096.

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Pantanal, located in the Upper Paraguay basin, is the world’s largest tropical wetland. The maintenance of this ecosystem depends on the water balance since precipitation is seasonal and high losses of water occur due to the high evapotranspiration. Water balance assessment using in situ data is still a challenge due to the large extension of the area and the complexity to be represented. In this study, the water balance in the Upper Paraguay basin was investigated based on hydrological variables derived from remote sensing data. Precipitation, evapotranspiration, and water storage change data

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30

Tátošová, Lucia, Karol Šinka, Beáta Novotná, and Dušan Húska. "Water in the City and Remote Sensing." Environment, Earth and Ecology 5, no. 1 (December 30, 2021): 26–38. http://dx.doi.org/10.24051/eee/145518.

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31

Ronghua, MA, TANG Junwu, DUAN Hongtao, and PAN Delu. "Progress in lake water color remote sensing." Journal of Lake Sciences 21, no. 2 (2009): 143–58. http://dx.doi.org/10.18307/2009.0201.

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32

Thenkabail, Prasad S. "Water productivity mapping methods using remote sensing." Journal of Applied Remote Sensing 2, no. 1 (November 1, 2008): 023544. http://dx.doi.org/10.1117/1.3033753.

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33

Ritchie, Jerry C., Paul V. Zimba, and James H. Everitt. "Remote Sensing Techniques to Assess Water Quality." Photogrammetric Engineering & Remote Sensing 69, no. 6 (June 1, 2003): 695–704. http://dx.doi.org/10.14358/pers.69.6.695.

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34

DALU, G. "Satellite remote sensing of atmospheric water vapour." International Journal of Remote Sensing 7, no. 9 (September 1986): 1089–97. http://dx.doi.org/10.1080/01431168608948911.

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35

BRAUDE, C., N. BEN YOSEF, and I. DaR. "Satellite remote sensing of waste water reservoirs." International Journal of Remote Sensing 16, no. 16 (November 10, 1995): 3087–114. http://dx.doi.org/10.1080/01431169508954610.

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36

Ahmad, S. R. "Remote sensing of water pollution by lasers." Transactions of the Institute of Measurement and Control 13, no. 2 (April 1991): 104–12. http://dx.doi.org/10.1177/014233129101300207.

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37

Zhang, Jie, Minquan Feng, and Yu Wang. "Automatic Segmentation of Remote Sensing Images on Water Bodies Based on Image Enhancement." Traitement du Signal 37, no. 6 (December 31, 2020): 1037–43. http://dx.doi.org/10.18280/ts.370616.

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Abstract (sommario):

By virtue of high-resolution remote sensing satellites, there is a possibility to analyze remote sensing images on water bodies through digital image processing (DIP). In many remote sensing images, however, the water bodies have similar gray values as other ground objects. To effectively distinguish water bodies from other ground objects in these images, this paper proposes a logarithmic enhancement method for remote sensing images on water bodies based on adaptive morphology. The proposed method can filter the noise of non-target area, and enhance the water body in the original image. On thi

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38

Chandrasekhar, M. G., K. Radhakrishnan, V. Jayaraman, and B. Manikiam. "Indian remote sensing programme." Geocarto International 6, no. 3 (September 1991): 59–62. http://dx.doi.org/10.1080/10106049109354322.

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39

Ryerson, Bob. "Remote sensing in Canada." Geocarto International 6, no. 3 (September 1991): 79–83. http://dx.doi.org/10.1080/10106049109354327.

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40

Cracknell, Arthur, and Ladson Hayes. "Introduction to remote sensing." Geocarto International 7, no. 2 (June 1992): 40. http://dx.doi.org/10.1080/10106049209354370.

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41

Cracknell, Arthur, and Ladson Hayes. "Remote sensing yearbook 1986." Geocarto International 1, no. 3 (January 1986): 58. http://dx.doi.org/10.1080/10106048609354061.

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42

Carter, D. J. "The remote sensing sourcebook." Geocarto International 1, no. 3 (January 1986): 60. http://dx.doi.org/10.1080/10106048609354062.

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43

Deekshatulu, B. L., and S. Adiga. "Indian remote sensing programme." Geocarto International 1, no. 4 (January 1986): 49–59. http://dx.doi.org/10.1080/10106048609354069.

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44

Cracknell, Arthur, and Ladson Hayes. "Remote Sensing Yearbook 1987." Geocarto International 2, no. 2 (June 1987): 48. http://dx.doi.org/10.1080/10106048709354096.

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45

Campbell, James B. "Introduction to remote sensing." Geocarto International 2, no. 4 (December 1987): 64. http://dx.doi.org/10.1080/10106048709354126.

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46

Ke, Zhi Wu, Rui Yu, Rui Xiang, Ke Long Zhang, and Yong Ma. "An Antisubmarine Detection Method Using IR Spectrometer in Ocean Remote Sensing." Advanced Materials Research 490-495 (March 2012): 1337–41. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1337.

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According to the reduction of submarine noise level, Non-acoustics antisubmarine detection method becomes more important for the ocean remote sensing, especially infrared (IR) imaging remote sensing detection method. Conventional IR imaging remote sensing antisubmarine detection is more difficult because modern advanced submarine IR thermal radiance is not obvious. In this paper, our main purpose is to develop the advanced IR imaging remote sensing antisubmarine detection approach by using infrared spectrometer. The IR spectrum information derived from IR spectrometer in sea water and then ret

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47

Gautam, Deepak, and Vinay Pagay. "A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops." Agronomy 10, no. 1 (January 17, 2020): 140. http://dx.doi.org/10.3390/agronomy10010140.

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With increasingly advanced remote sensing systems, more accurate retrievals of crop water status are being made at the individual crop level to aid in precision irrigation. This paper summarises the use of remote sensing for the estimation of water status in horticultural crops. The remote measurements of the water potential, soil moisture, evapotranspiration, canopy 3D structure, and vigour for water status estimation are presented in this comprehensive review. These parameters directly or indirectly provide estimates of crop water status, which is critically important for irrigation manageme

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48

Wei, YE, and SONG Wei. "Quantitative remote sensing monitoring of water quality in Bohai Bay based on Landsat multispectral data." E3S Web of Conferences 206 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202020603007.

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In this paper, through the principal component analysis of water quality survey data of Bohai Bay in 2006, 2009 and 2013, the main pollutant was selected, and the quasi-simultaneous Landsat multispectral remote sensing data are regressed to establish the quantitative inversion model of the sensitive band and the main pollutants in seawater. The accuracy of the model is determined to meet the requirements of quantitative inversion of water quality remote sensing through the significance test method of accuracy assessment, providing a basis for future multispectral remote sensing monitoring of w

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49

Lakshmi, Venkat. "Remote Sensing of Soil Moisture." ISRN Soil Science 2013 (March 7, 2013): 1–33. http://dx.doi.org/10.1155/2013/424178.

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Soil moisture is an important variable in land surface hydrology as it controls the amount of water that infiltrates into the soil and replenishes the water table versus the amount that contributes to surface runoff and to channel flow. However observations of soil moisture at a point scale are very sparse and observing networks are expensive to maintain. Satellite sensors can observe large areas but the spatial resolution of these is dependent on microwave frequency, antenna dimensions, and height above the earth’s surface. The higher the sensor, the lower the spatial resolution and at low el

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50

Ling, Min, Qun Cheng, Jun Peng, Ling Jiang, and Ruifeng Wang. "Retrieval Algorithm of Water Pollutant Concentration Based on UAV Remote Sensing Technology." Mobile Information Systems 2022 (April 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/5017000.

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With the development of the society and economy, traditional water pollution monitoring methods can no longer meet the normal needs of work. Unmanned aerial vehicle remote sensing technology has gradually emerged, and it has shown a development trend of multimodel and multifunction. However, the application of UAV remote sensing technology in water pollution monitoring is in its infancy and has not formed a unified method and standard. This paper introduces the disadvantages of UAV Remote Sensing Technology in water pollution monitoring and provides a way to improve the application level of UA

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