Статті в журналах: "Water Remote sensing"

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 terrestrial water storage variations) are now observable at varying spatial and temporal resolutions and accuracy via remote sensing. We evaluate the current status of estimates of each of these variables, as well as river discharge, the direct estimation of which is not yet possible. Although most of the constituent variables are observable by remote sensing, attempts to close the surface water budget from remote sensing alone have generally been unsuccessful, suggesting that current generation sensors and platforms are not yet able to provide hydrologically consistent observations of the land surface water budget at any spatial scale.

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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 area in Libya. The use of theoretical and experimental research in remote sensing, remote sensing applications in the Netherlands and remote sensing applications in developing countries are discussed. (Abstract retrieved from CAB Abstracts by CABI’s permission)

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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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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 meteorological data. A water balance in the root soil layer enables us to calculate irrigation water requirements at appropriate scale for monitoring water management near- real time. This approach could be coupled to the remote sensing-based surface energy balance which uses surface temperature as primary input. But what we could call «remote sensing-driven crop water management» requires at least two steps more to be placed into the day-to-day routine on farming irrigation: On the one hand, for planning irrigation the users require the forecasting of crop water requirements for the week ahead; it can be achieved by extrapolating crop coefficient trend and by using weather forecasting for ETo estimation. On the other hand, decision makers in charge of irrigation require access to this information in an easy-to-use way on real time. It can be achieved through leading edge webGIS tools, which facilitates co-creation and collaboration with stakeholders.

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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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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 highlights novel methods for evaluating crop water stress and its correlation with certain measurable parameters, investigated using remote-sensing systems. Through an examination of previous literature, technologies, and data, we review the application of remote-sensing systems in the analysis of crop water stress. Initially, the study presents the relationship of relative water content (RWC) with equivalent water thickness (EWT) and soil moisture crop water stress. Evapotranspiration and sun-induced chlorophyll fluorescence are then analyzed in relation to crop water stress using remote sensing. Finally, the study presents various remote-sensing technologies used to detect crop water stress, including optical sensing systems, thermometric sensing systems, land-surface temperature-sensing systems, multispectral (spaceborne and airborne) sensing systems, hyperspectral sensing systems, and the LiDAR sensing system. The study also presents the future prospects of remote-sensing systems in analyzing crop water stress and how they could be further improved.

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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 remote sensing information extraction, this paper analyzes the water information extraction of remote sensing images from shallow to deep. Threshold method and normalized water index method are respectively used for water extraction, and comparative analysis is conducted. The results show that both threshold method and normalized water index method can effectively extract water, but the threshold method requires several experiments to determine the threshold value. It is impossible to determine the most suitable threshold for water extraction. Normalized water index can extract water well, and it is realized by the operation between bands, and the operation is simple and convenient.

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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 of the water environment. The results show that the method is practical and effective, and can achieve high recognition accuracy for water blooms.

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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 are independent processes. Various empirical models based on meteorological variables and remote sensed vegetation indices to estimate WUE proved the ability of remotely sensed data for WUE estimating. The analytical model provides a mechanistic opportunity for WUE estimation on an ecosystem scale, while the hypothesis has yet to be validated and applied for the shorter time scales. An optimized response of canopy conductance to atmospheric vapor pressure deficit (VPD) in an analytical model inverted from the conductance model has been also challenged. Partitioning transpiration (T) and evaporation (E) is a more complex phenomenon than that stated in the analytic model and needs a more precise remote sensing retrieval algorithm as well as ground validation, which is an opportunity for remote sensing to extrapolate WUE estimation from sites to a regional scale. Although studies on controlling the mechanism of environmental factors have provided an opportunity to improve WUE remote sensing, the mismatch in the spatial and temporal resolution of meteorological products and remote sensing data, as well as the uncertainty of meteorological reanalysis data, add further challenges. Therefore, improving the remote sensing-based methods of GPP and ET, developing high-quality meteorological forcing datasets and building mechanistic remote sensing models directly acting on carbon–water cycle coupling are possible ways to improve WUE remote sensing. Improvement in direct WUE remote sensing methods or remote sensing-driven ecosystem analysis methods can promote a better understanding of the global ecosystem carbon–water coupling mechanisms and vegetation functions–climate feedbacks to serve for the future global carbon neutrality.

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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 remains a challenging task to investigate and resolve the problem for public health authority. Intensification of agricultural activities, change in climatic conditions, coastal area quick urban development, and resultant freshwater source declining have contributed considerably to the surface water contamination risk and the augmentation of waterborne disease incidences. The quality of surface water monitoring needs frequent problem detection to reduce any negative effect on public health. The epidemiology study applies geospatial and remote sensing technologies to distinguish the temporal and spatial environmental variability determinants to assess the epidemiology of certain diseases. By providing an integrated and systematic approach to risky water management for the public health and safety, a proper epidemiology method can be used and proved to be an efficient device to evaluate the quality of surface water and any related health risks. SWRMS- Spatial water resource monitoring system provides important and beneficial information to support water management. Requisite innovative features involve the explicit water redistribution description and use of river water and groundwater systems, to achieve more spatial details like key irrigated area features and wetlands, to improve hydrometer observation accuracy and assimilating the observations. A review of research and operational applications reveals that satellite view can enhance spatial detail and accuracy in estimating hydrological model. Every operating system uses land cover classification, dynamic forcing, and a parameterization priory of vegetation dynamics, which is partially or completely based on remote sensing, while satellite observations are utilized in varying stages for data assimilation and model evaluation. The satellite observation, utility by data assimilation varies as a dominant hydrological function. This review paper identifies the spatial and temporal precipitation products, including the application of a higher remote sensing product range, along with operational challenges while research satellite mission continuity with data services, finding computationally-efficient data assimilation techniques. The entire observations critically relies on the detailed information availability and understanding the remotely-sensed spatial and temporal scaling.

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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 digital technology is the important basis for water information.

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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 were estimated with accuracy by the water balance, but the same was not possible for the discharge. However, high uncertainties in the estimates were verified, mainly during the rainy season. The remote sensing data allowed the identification of the seasonality of hydrological variables in the Pantanal system and in the different regions of the basin: Chaco, Pantanal and Planalto. Water deficit in the basin was observed from March/April to September as well as a positive water balance due to precipitation during the rest of the year. The spatial analysis of the basin showed that in the northern region, the precipitation, the evapotranspiration, and the water storage variation are higher than in the southern region. Results demonstrated that remote sensing data can help in the comprehension of hydrological systems operation, especially in large wetland regions.

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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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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 this basis, a morphology-based segmentation method was designed for remote sensing images on water bodies. Experimental results show that our method achieved a high segmentation accuracy, controlling the mean segmentation error at below 1.32%.

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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 retrieves the water-leaving spectra by the standard atmospheric correction algorithm. The submarine is detected by analyzing the water-leaving spectrum information. Results of comparisons with conventional IR imaging remote sensing antisubmarine detection, the modified approach is available to estimate the spectrum properties and effective to antisubmarine detection in sea water

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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 management in farms. The review is organised into four main sections: (i) remote sensing platforms; (ii) the remote sensor suite; (iii) techniques adopted for horticultural applications and indicators of water status; and, (iv) case studies of the use of remote sensing in horticultural crops. Finally, the authors’ view is presented with regard to future prospects and research gaps in the estimation of the crop water status for precision irrigation.

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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 water quality indicators. assessment, providing a basis for future multispectral remote sensing monitoring of water quality indicators.

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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 elevations the spacecraft would use more fuel. Higher spatial resolution requires larger diameter antennas that in turn require more fuel to maintain in space. Given these competing issues most passive radiometers have spatial resolutions in 10s of kilometers that are too coarse for catchment hydrology applications. Most local applications require higher-spatial-resolution soil moisture data. Downscaling of the data requires ancillary data and model products, all of which are used here to develop high-spatial-resolution soil moisture for catchment applications in hydrology. In this paper the author will outline and explain the methodology for downscaling passive microwave estimation of soil moisture.

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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 UAV Remote Sensing Technology in water pollution monitoring. In order to make full use of the advantages of remote sensing monitoring technology in water ecological environment monitoring, including wide coverage, fast analysis speed, accurate and reliable monitoring results, and large amount of information, according to the spectral effect of polluted water, a remote sensing inversion model of river and lake parameters is established to determine the water quality of rivers and lakes. Concentrations of pollutants in the body are inverted and analyzed. The analysis results show that the fitting effect of the inversion model is accurate and the accuracy errors reflected by the degree of dispersion and deviation are also acceptable. The inversion results of the concentration of river and lake characteristic parameters based on the Gaofen-1 remote sensing image data are in line with the actual situation. The research results can greatly strengthen the monitoring capacity of the sewage outlet and provide reference for related research on water pollution caused by the sewage outlet.

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