The AMSL LST algorithm validated for the Xinjiang Autonomous Region in China

Abstract

The split-window land surface brightness temperature (LST_b) algorithm of Coll and Caselles (1994) is one of the first approaches to estimate LST_b applied for large surface areas. In this article, we describe a calibrated and validated version of the Coll and Caselles (1994) algorithm applied for the retrieval of land surface air temperature (LST_a) – equivalent to standard WMO (World Meteorological Organization) temperature measurements – for the province of Xinjiang (PR of China). Locally received MODIS (Moderate Resolution Imaging Spectroradiometer) imagery (Fukang receiving station) is used as the input data stream for the so-called AMSL (Aqua MODIS SWA LST_a) algorithm. The objective to develop this algorithm is that it is an input for a distributed hydrological model as well as a soil moisture content retrieval algorithm. In the Xinjiang province with an abundance of arid to semi-arid regions, a highly continental climate, irrigated crop fields and mountain ranges of 6000 m and higher, one typically deals with the spatio-temporally complex conditions, making a high-accuracy retrieval of LST_a quite a challenge. The calibration and validation of the AMSL LST_a product (LST_a,amsl) – using the Jackknife method – is performed using LST_a measurements (LST_a,tmb) from 49 meteorological stations managed by the Tarim Meteorological Bureau (TMB). These stations are distributed relatively homogeneously over the province. The TMB stations’ temperature data are split into 40 calibration LST_a,tmb data sets and 9 validation LST_a,tmb data sets. We can observe that when validated, the LST_a,amsl versus LST_a,tmb validation relationship elicits a high correlation, a slope very close to 1 and an intercept very close to 0. The validated LST_a,amsl estimates demonstrate an estimation accuracy of 0.5 K. The results presented in this article suggest that the LST_a,amsl product is suitable to estimate the land surface air temperature spatio-temporal fields for the arid and semi-arid regions of the Xinjiang province accurately.

Acknowledgements

Thanks are due to the Ministry of Science and Technology (MOST) of China as well as BELSPO, the Belgian Science Policy Office, for financial support. We also acknowledge the work performed by the anonymous reviewers and co-authors, to enhance the scientific quality of this research article. The research presented here was supported by the ‘973 Programme’ (No. 2009CB421301), the National Key Technology Programme (No. 2007BAH12B03) and the National Natural Science Foundation of China (No. 40730633 and No. 0571030).