International Journal of Remote Sensing vol:33 issue:12 pages:3886-3905
The split-window land surface brightness temperature (LSTb) algorithm of Coll and Caselles (1994) is one of the first approaches to estimate LSTb 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 (LSTa) – 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 LSTa) 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 LSTa quite a challenge. The calibration and validation of the AMSL LSTa product (LSTa,amsl) – using the Jackknife method – is performed using LSTa measurements (LSTa,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 LSTa,tmb data sets and 9 validation LSTa,tmb data sets.We can observe that when validated, the LSTa,amsl versus LSTa,tmb validation relationship elicits a high correlation, a slope very close to 1 and an intercept very close to 0. The validated LSTa,amsl estimates demonstrate an estimation accuracy of 0.5 K. The results presented in this article suggest that the LSTa,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.