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Evaluating a thermal image sharpening model over a mixed agricultural landscape in India

Evaluating a thermal image sharpening model over a mixed agricultural landscape in India
Evaluating a thermal image sharpening model over a mixed agricultural landscape in India
Fine spatial resolution (e.g., <300 m) thermal data are needed regularly to characterise the temporal pattern of surface moisture status, water stress, and to forecast agriculture drought and famine. However, current optical sensors do not provide frequent thermal data at a fine spatial resolution. The TsHARP model provides a possibility to generate fine spatial resolution thermal data from coarse spatial resolution (≥1 km) data on the basis of an anticipated inverse linear relationship between the normalised difference vegetation index (NDVI) at fine spatial resolution and land surface temperature at coarse spatial resolution. The current study utilised the TsHARP model over a mixed agricultural landscape in the northern part of India. Five variants of the model were analysed, including the original model, for their efficiency. Those five variants were the global model (original); the resolution-adjusted global model; the piecewise regression model; the stratified model; and the local model. The models were first evaluated using Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) thermal data (90 m) aggregated to the following spatial resolutions: 180 m, 270 m, 450 m, 630 m, 810 m and 990 m. Although sharpening was undertaken for spatial resolutions from 990 m to 90 m, root mean square error (RMSE) of <2 K could, on average, be achieved only for 990–270 m in the ASTER data. The RMSE of the sharpened images at 270 m, using ASTER data, from the global, resolution-adjusted global, piecewise regression, stratification and local models were 1.91, 1.89, 1.96, 1.91, 1.70 K, respectively. The global model, resolution-adjusted global model and local model yielded higher accuracy, and were applied to sharpen MODIS thermal data (1 km) to the target spatial resolutions. Aggregated ASTER thermal data were considered as a reference at the respective target spatial resolutions to assess the prediction results from MODIS data. The RMSE of the predicted sharpened image from MODIS using the global, resolution-adjusted global and local models at 250 m were 3.08, 2.92 and 1.98 K, respectively. The local model consistently led to more accurate sharpened predictions by comparison to other variants.
sharpening, dis-aggregation, land surface temperature, ASTER, MODIS
0303-2434
178-191
Jeganathan, C.
f859ef31-fe01-4623-9ebf-d78466c974ca
Hamm, N.A.S.
e6d796b7-1286-4a67-843e-296dd1c1ee82
Mukherjee, S.
d9278fe6-ec80-45e0-b3ab-137e668787e8
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Raju, P.L.N.
43b2541d-e15a-47b7-b4a9-8581dd90b997
Dadhwal, V.K.
0b9a922d-8d4c-418d-b352-fd7225ea4c50
Jeganathan, C.
f859ef31-fe01-4623-9ebf-d78466c974ca
Hamm, N.A.S.
e6d796b7-1286-4a67-843e-296dd1c1ee82
Mukherjee, S.
d9278fe6-ec80-45e0-b3ab-137e668787e8
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Raju, P.L.N.
43b2541d-e15a-47b7-b4a9-8581dd90b997
Dadhwal, V.K.
0b9a922d-8d4c-418d-b352-fd7225ea4c50

Jeganathan, C., Hamm, N.A.S., Mukherjee, S., Atkinson, P.M., Raju, P.L.N. and Dadhwal, V.K. (2011) Evaluating a thermal image sharpening model over a mixed agricultural landscape in India. International Journal of Applied Earth Observation and Geoinformation, 13 (2), 178-191. (doi:10.1016/j.jag.2010.11.001).

Record type: Article

Abstract

Fine spatial resolution (e.g., <300 m) thermal data are needed regularly to characterise the temporal pattern of surface moisture status, water stress, and to forecast agriculture drought and famine. However, current optical sensors do not provide frequent thermal data at a fine spatial resolution. The TsHARP model provides a possibility to generate fine spatial resolution thermal data from coarse spatial resolution (≥1 km) data on the basis of an anticipated inverse linear relationship between the normalised difference vegetation index (NDVI) at fine spatial resolution and land surface temperature at coarse spatial resolution. The current study utilised the TsHARP model over a mixed agricultural landscape in the northern part of India. Five variants of the model were analysed, including the original model, for their efficiency. Those five variants were the global model (original); the resolution-adjusted global model; the piecewise regression model; the stratified model; and the local model. The models were first evaluated using Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) thermal data (90 m) aggregated to the following spatial resolutions: 180 m, 270 m, 450 m, 630 m, 810 m and 990 m. Although sharpening was undertaken for spatial resolutions from 990 m to 90 m, root mean square error (RMSE) of <2 K could, on average, be achieved only for 990–270 m in the ASTER data. The RMSE of the sharpened images at 270 m, using ASTER data, from the global, resolution-adjusted global, piecewise regression, stratification and local models were 1.91, 1.89, 1.96, 1.91, 1.70 K, respectively. The global model, resolution-adjusted global model and local model yielded higher accuracy, and were applied to sharpen MODIS thermal data (1 km) to the target spatial resolutions. Aggregated ASTER thermal data were considered as a reference at the respective target spatial resolutions to assess the prediction results from MODIS data. The RMSE of the predicted sharpened image from MODIS using the global, resolution-adjusted global and local models at 250 m were 3.08, 2.92 and 1.98 K, respectively. The local model consistently led to more accurate sharpened predictions by comparison to other variants.

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More information

e-pub ahead of print date: 7 December 2010
Published date: April 2011
Keywords: sharpening, dis-aggregation, land surface temperature, ASTER, MODIS
Organisations: Global Env Change & Earth Observation

Identifiers

Local EPrints ID: 339880
URI: http://eprints.soton.ac.uk/id/eprint/339880
ISSN: 0303-2434
PURE UUID: ad9c612d-7f91-4ed0-8748-7ee9cacce32f
ORCID for P.M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

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Date deposited: 01 Jun 2012 08:45
Last modified: 15 Mar 2024 02:47

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Contributors

Author: C. Jeganathan
Author: N.A.S. Hamm
Author: S. Mukherjee
Author: P.M. Atkinson ORCID iD
Author: P.L.N. Raju
Author: V.K. Dadhwal

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