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Estimating Evapotranspiration in Relation to Land-Use Change Using Satellite Remote Sensing
Evapotranspiration (ET) estimation at river basin scale with respect to various land use and land cover (LULC) provides useful conservation prescriptions. With the advancement of satellite remote sensing, ET estimation has gained tremendous attention. Satellite remote sensing-based methods can map spatially distributed ET over different land use and thus are helpful for inaccessible areas. In this chapter, a commonly used surface energy balance-based modified Priestley–Taylor algorithm was demonstrated to estimate LULC-wise ET in two eastern river basins of India, named Brahmani and Baitarani. The potential impact of cloud cover on the performance of the ET estimation was also assessed. The results showed that the forest accounted for the highest ET followed by water body/moist riverbed in both the river basins. The ET estimates were found reasonable during non-monsoon season; however, during monsoon season, an underestimation was observed due to cloud cover, revealing that a denser time-stack of satellite images is required for an accurate estimation of ET during monsoon season. The assessment of the effects of cloud cover on ET estimates revealed that the method used in the study require cloud-free satellite images for accurate estimates of ET. With the increased availability of data from different satellites from recent launches, a dense time-stack of data can be generated by fusing multisensor datasets. Such fusion may improve the accuracy of ET estimates considerably with better information about the spatio-temporal variability.
Estimating Evapotranspiration in Relation to Land-Use Change Using Satellite Remote Sensing
Evapotranspiration (ET) estimation at river basin scale with respect to various land use and land cover (LULC) provides useful conservation prescriptions. With the advancement of satellite remote sensing, ET estimation has gained tremendous attention. Satellite remote sensing-based methods can map spatially distributed ET over different land use and thus are helpful for inaccessible areas. In this chapter, a commonly used surface energy balance-based modified Priestley–Taylor algorithm was demonstrated to estimate LULC-wise ET in two eastern river basins of India, named Brahmani and Baitarani. The potential impact of cloud cover on the performance of the ET estimation was also assessed. The results showed that the forest accounted for the highest ET followed by water body/moist riverbed in both the river basins. The ET estimates were found reasonable during non-monsoon season; however, during monsoon season, an underestimation was observed due to cloud cover, revealing that a denser time-stack of satellite images is required for an accurate estimation of ET during monsoon season. The assessment of the effects of cloud cover on ET estimates revealed that the method used in the study require cloud-free satellite images for accurate estimates of ET. With the increased availability of data from different satellites from recent launches, a dense time-stack of data can be generated by fusing multisensor datasets. Such fusion may improve the accuracy of ET estimates considerably with better information about the spatio-temporal variability.
Estimating Evapotranspiration in Relation to Land-Use Change Using Satellite Remote Sensing
Water Sci.,Technol.Library
Pandey, Ashish (editor) / Chowdary, V. M. (editor) / Behera, Mukunda Dev (editor) / Singh, V. P. (editor) / Gupta, Dheeraj K. (author) / Patidar, Nitesh (author) / Behera, Mukunda Dev (author) / Panda, Sudhindra Nath (author) / Chowdary, V. M. (author)
Geospatial Technologies for Land and Water Resources Management ; Chapter: 12 ; 185-206
2021-12-07
22 pages
Article/Chapter (Book)
Electronic Resource
English
Moderate resolution imaging spectrometer (MODIS) , Brahmani and Baitarani , Evaporation fraction , Spatio-temporal cover fraction (STCF) , Energy balance Computer Science , Computer Applications , Geography, general , Natural Hazards , Landscape Ecology , Physical Geography , Earth and Environmental Science
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