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

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Evaluating a thermal image sharpening model over a mixed agricultural landscape in India. / Jeganathan, C.; Hamm, N. A. S.; Mukherjee, S.; Atkinson, Peter M.; Raju, P. L. N.; Dadhwal, V. K.

In: International Journal of Applied Earth Observation and Geoinformation, Vol. 13, No. 2, 04.2011, p. 178-191.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Jeganathan, C, Hamm, NAS, Mukherjee, S, Atkinson, PM, Raju, PLN & Dadhwal, VK 2011, 'Evaluating a thermal image sharpening model over a mixed agricultural landscape in India', International Journal of Applied Earth Observation and Geoinformation, vol. 13, no. 2, pp. 178-191. https://doi.org/10.1016/j.jag.2010.11.001

APA

Jeganathan, C., Hamm, N. A. S., Mukherjee, S., Atkinson, P. M., Raju, P. L. N., & 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. https://doi.org/10.1016/j.jag.2010.11.001

Vancouver

Jeganathan C, Hamm NAS, Mukherjee S, Atkinson PM, Raju PLN, Dadhwal VK. Evaluating a thermal image sharpening model over a mixed agricultural landscape in India. International Journal of Applied Earth Observation and Geoinformation. 2011 Apr;13(2):178-191. https://doi.org/10.1016/j.jag.2010.11.001

Author

Jeganathan, C. ; Hamm, N. A. S. ; Mukherjee, S. ; Atkinson, Peter M. ; Raju, P. L. N. ; Dadhwal, V. K. / Evaluating a thermal image sharpening model over a mixed agricultural landscape in India. In: International Journal of Applied Earth Observation and Geoinformation. 2011 ; Vol. 13, No. 2. pp. 178-191.

Bibtex

@article{99c967e579d84abdb3910fe651de8896,
title = "Evaluating a thermal image sharpening model over a mixed agricultural landscape in India",
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.",
keywords = "Sharpening, Dis-aggregation, Land surface temperature, ASTER, MODIS",
author = "C. Jeganathan and Hamm, {N. A. S.} and S. Mukherjee and Atkinson, {Peter M.} and Raju, {P. L. N.} and Dadhwal, {V. K.}",
note = "M1 - 2",
year = "2011",
month = apr,
doi = "10.1016/j.jag.2010.11.001",
language = "English",
volume = "13",
pages = "178--191",
journal = "International Journal of Applied Earth Observation and Geoinformation",
issn = "0303-2434",
publisher = "International Institute for Aerial Survey and Earth Sciences",
number = "2",

}

RIS

TY - JOUR

T1 - Evaluating a thermal image sharpening model over a mixed agricultural landscape in India

AU - Jeganathan, C.

AU - Hamm, N. A. S.

AU - Mukherjee, S.

AU - Atkinson, Peter M.

AU - Raju, P. L. N.

AU - Dadhwal, V. K.

N1 - M1 - 2

PY - 2011/4

Y1 - 2011/4

N2 - 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.

AB - 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.

KW - Sharpening

KW - Dis-aggregation

KW - Land surface temperature

KW - ASTER

KW - MODIS

U2 - 10.1016/j.jag.2010.11.001

DO - 10.1016/j.jag.2010.11.001

M3 - Journal article

VL - 13

SP - 178

EP - 191

JO - International Journal of Applied Earth Observation and Geoinformation

JF - International Journal of Applied Earth Observation and Geoinformation

SN - 0303-2434

IS - 2

ER -