6533b86efe1ef96bd12cc682

RESEARCH PRODUCT

Improved land surface emissivities over agricultural areas using ASTER NDVI

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Abstract Land surface emissivity retrieval over agricultural regions is important for energy balance estimations, land cover assessment and other related environmental studies. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) produces images of sufficient spatial resolution (from 15 m to 90 m) to be of use in agricultural studies, in which fields of crops are too small to be well-resolved by low resolution sensors. The ASTER project generates land surface emissivity images as a Standard Product (AST05) using the Temperature/Emissivity Separation (TES) algorithm. However, the TES algorithm is prone to scaling errors in estimating emissivities for surfaces with low spectral contrast if the atmospheric correction is inaccurate. This paper shows a comparison between the land surface emissivity estimated with the TES algorithm and from a simple approach using the Normalized Difference Vegetation Index (NDVI) for five ASTER images (28 June 2000, 15 August 2000, 31 August 2000, 28 April 2001 and 02 August 2001) of the agricultural area of Barrax (Albacete, Spain). The results indicate that differences are  2% for bands 10 (8.3 μm), 11 (8.6 μm) and 12 (9.1 μm). The emissivities for the five ASTER bands were tested against in situ measurements carried out with the CIMEL CE 312-2 field radiometer, the NDVI method giving root mean square errors (RMSE)  0.03 over bare soil. The errors and inconsistencies for ASTER bands 13 and 14 are within those anticipated for TES, but the greater errors for bands 10–12 suggest the presence of problems related to atmospheric compensation and model assumptions about soil spectra. The NDVI method uses visible/near-infrared data co-acquired with the thermal images to estimate vegetation cover and, hence, provides an independent constraint on emissivity. The success of this approach suggests that it may be useful for daytime images of agricultural or other heavily vegetated areas, in which the TES algorithm has occasional failures.