Downscaling of MODIS thermal imagery
In this paper, integration of two models TsHARP (Tsharp) and Thin plate spline (TPS) has been performed for spatial sharpening of 1 km (coarse) resolution of MODIS thermal imagery to 250 m resolution. Afterwards it was validated with LANDSAT-7 thermal data (after resampled to 250 m pixel). The resul...
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doaj-01b13f5dd75e44d4a6d515e079b031132020-11-25T02:56:09ZengElsevierEgyptian Journal of Remote Sensing and Space Sciences1110-98232019-04-012214958Downscaling of MODIS thermal imageryKishan Singh Rawat0V.K. Sehgal1S.S. Ray2Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110012, India; Sathyabama Institute of Science and Technology (SIST), (Deemed to be University), Chennai-600119 (T.N.), India; Corresponding author.Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110012, IndiaMahalanobis National Crop Forecast Centre (MNCFC), Pusa Campus, New Delhi 110012, IndiaIn this paper, integration of two models TsHARP (Tsharp) and Thin plate spline (TPS) has been performed for spatial sharpening of 1 km (coarse) resolution of MODIS thermal imagery to 250 m resolution. Afterwards it was validated with LANDSAT-7 thermal data (after resampled to 250 m pixel). The results showed that LST based on integration of two (TsHARP and TPS) models is consistent with true data (LANDSAT-7 ETM+, thermal data). We have observed R2 at pure cropped area, cropped area with low settlement and cropped area with high settlement is showing, 0.74 (Multi R = 0.80, Adju R = 0.75 and p = .001), 0.72 (Multi R = 0.78, Adju R = 72 and p = .001) and 0.71 (Multi R = 0.78, Adju R = 0.71 and p = .001) respectively. While overall R2 of 0.69 (Multi R = 0.76, Adju R = 0.71 and p = .000) for all categories of classes (cropped area + cropped area with low settlement + cropped area with high settlement). LST shows root mean square error (RMSE) = 0.307 °C, Relative-RMSE (R-RMSE) = 0.167 °C, mean absolute error (MAE) = 0.033 °C, normalized RMSE (NRMSE) = 0.018 °C, index of agreement (d) = 0.99, RMSE-observations standard deviation ratio (RSR) = 0.39 and RMSE% = 0.02 for merging process based LST. We conclude that combination of TsHARP and TPS model has a great potential to estimate LST at 250 m with high temporal resolution. This LST can be used as an input in various models to estimate other components which are LST dependent. Keywords: Downscaling, TsHARP, TPS, MODIS, LANDSAT-7http://www.sciencedirect.com/science/article/pii/S1110982317300169 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kishan Singh Rawat V.K. Sehgal S.S. Ray |
spellingShingle |
Kishan Singh Rawat V.K. Sehgal S.S. Ray Downscaling of MODIS thermal imagery Egyptian Journal of Remote Sensing and Space Sciences |
author_facet |
Kishan Singh Rawat V.K. Sehgal S.S. Ray |
author_sort |
Kishan Singh Rawat |
title |
Downscaling of MODIS thermal imagery |
title_short |
Downscaling of MODIS thermal imagery |
title_full |
Downscaling of MODIS thermal imagery |
title_fullStr |
Downscaling of MODIS thermal imagery |
title_full_unstemmed |
Downscaling of MODIS thermal imagery |
title_sort |
downscaling of modis thermal imagery |
publisher |
Elsevier |
series |
Egyptian Journal of Remote Sensing and Space Sciences |
issn |
1110-9823 |
publishDate |
2019-04-01 |
description |
In this paper, integration of two models TsHARP (Tsharp) and Thin plate spline (TPS) has been performed for spatial sharpening of 1 km (coarse) resolution of MODIS thermal imagery to 250 m resolution. Afterwards it was validated with LANDSAT-7 thermal data (after resampled to 250 m pixel). The results showed that LST based on integration of two (TsHARP and TPS) models is consistent with true data (LANDSAT-7 ETM+, thermal data). We have observed R2 at pure cropped area, cropped area with low settlement and cropped area with high settlement is showing, 0.74 (Multi R = 0.80, Adju R = 0.75 and p = .001), 0.72 (Multi R = 0.78, Adju R = 72 and p = .001) and 0.71 (Multi R = 0.78, Adju R = 0.71 and p = .001) respectively. While overall R2 of 0.69 (Multi R = 0.76, Adju R = 0.71 and p = .000) for all categories of classes (cropped area + cropped area with low settlement + cropped area with high settlement). LST shows root mean square error (RMSE) = 0.307 °C, Relative-RMSE (R-RMSE) = 0.167 °C, mean absolute error (MAE) = 0.033 °C, normalized RMSE (NRMSE) = 0.018 °C, index of agreement (d) = 0.99, RMSE-observations standard deviation ratio (RSR) = 0.39 and RMSE% = 0.02 for merging process based LST. We conclude that combination of TsHARP and TPS model has a great potential to estimate LST at 250 m with high temporal resolution. This LST can be used as an input in various models to estimate other components which are LST dependent. Keywords: Downscaling, TsHARP, TPS, MODIS, LANDSAT-7 |
url |
http://www.sciencedirect.com/science/article/pii/S1110982317300169 |
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