Merlin, O, Duchemin, B, Hagolle, O, Jacob, F, Coudert, B, Chehbouni, G, Dedieu, G, Garatuza, J, Kerr, Y (2010). Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images. REMOTE SENSING OF ENVIRONMENT, 114(11), 2500-2512.

Abstract
The temporal frequency of the thermal data provided by current spaceborne high-resolution imagery systems is inadequate for agricultural applications. As an alternative to the lack of high-resolution observations, kilometric thermal data can be disaggregated using a green (photosynthetically active) vegetation index e.g. NDVI (Normalized Difference Vegetation Index) collected at high resolution. Nevertheless, this approach is only valid in the conditions where vegetation temperature is approximately uniform. To extend the validity domain of the classical approach, a new methodology is developed by representing the temperature difference between photosynthetically and non-photosynthetically active vegetation. In practice, both photosynthetically and non-photosynthetically active vegetation fractions are derived from a time series of Formosat-2 shortwave data, and then included in the disaggregation procedure. The approach is tested over a 16 km by 10 km irrigated cropping area in Mexico during a whole agricultural season. Kilometric MODIS (MODerate resolution Imaging Spectroradiometer) surface temperature is disaggregated at 100 m resolution, and disaggregated temperature is subsequently compared against concurrent ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data. Statistical results indicate that the new methodology is more robust than the classical one, and is always more accurate when fractional non-photosynthetically active vegetation cover is larger than 0.10. The mean correlation coefficient and slope between disaggregated and ASTER temperature is increased from 0.75 to 0.81 and from 0.60 to 0.77, respectively. The approach is also tested using the MODIS data re-sampled at 2 km resolution. Aggregation reduces errors in MODIS data and consequently increases the disaggregation accuracy. (C) 2010 Elsevier Inc. All rights reserved.

DOI:
10.1016/j.rse.2010.05.025

ISSN:
0034-4257