Spatial Disaggregation of Latent Heat Flux Using Contextual Models over India
AbstractEstimation of latent heat flux at the agricultural field scale is required for proper water management. The current generation thermal sensors except Landsat-8 provide data on the order of 1000 m. The aim of this study is to test three approaches based on contextual models using only remote sensing datasets for the disaggregation of latent heat flux over India. The first two approaches are, respectively, based on the estimation of the evaporative fraction (EF) and solar radiation ratio at coarser resolution and disaggregating them to yield the latent heat flux at a finer resolution. The third approach is based on disaggregation of the thermal data and estimating a finer resolution latent heat flux. The three approaches were tested using MODIS datasets and the validation was done using the Bowen Ratio energy balance observations at five sites across India. From the validation, it was observed that the first two approaches performed similarly and better than the third approach at all five sites. The third approach, based on the disaggregation of the thermal data, yielded larger errors. In addition to better performance, the second approach based on the disaggregation of solar radiation ratio was simpler and required lesser data processing than the other approaches. In addition, the first two approaches captured the spatial pattern of latent heat flux without introducing any artefacts in the final output. View Full-Text
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Eswar, R.; Sekhar, M.; Bhattacharya, B.K.; Bandyopadhyay, S. Spatial Disaggregation of Latent Heat Flux Using Contextual Models over India. Remote Sens. 2017, 9, 949.
Eswar R, Sekhar M, Bhattacharya BK, Bandyopadhyay S. Spatial Disaggregation of Latent Heat Flux Using Contextual Models over India. Remote Sensing. 2017; 9(9):949.Chicago/Turabian Style
Eswar, Rajasekaran; Sekhar, Muddu; Bhattacharya, Bimal K.; Bandyopadhyay, Soumya. 2017. "Spatial Disaggregation of Latent Heat Flux Using Contextual Models over India." Remote Sens. 9, no. 9: 949.
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