Comparison of Some Split-window Algorithms to Estimate Land Surface Temperature from AVHRR Data in Southeastern Tehran,

Land surface temperature (LST) is a significant parameter for many applications. Many studies have proposedvarious algorithms, such as the split-window method, for retrieving surface temperatures from two spectrallyadjacent thermal infrared bands of satellite data. Each algorithm is developed for a limited study area andapplication. In this paper, as part of developing an optimal split-window method in the southeast of Tehran province,Iran, four commonly applied algorithms to retrieve the LST from AVHRR were compared. This study was carriedout in a wheat farm site located in the Pakdasht Agricultural Region. Measurements of LST over the farm were madewith a manual infrared radiometer at the time of NOAA overpass for 18 days of May to June 2004. These days werecloud free over the study area. A total of 18 NOAA images were acquired for the days that LST measurements weremade. The temperatures derived by the different split-window algorithms were compared to ground truthmeasurements. The performance of the split window algorithms was checked with three statistical indices: root meansquare error (RMSE), mean bias error (MBE) and coefficient of determination (R2). The results showed that theUlivieri split-window algorithm produced the lowest value of RMSE and MBE (2.71 and 0.26 K, respectively) andits highest value of R2 (0.92) gave more accurate results than the other algorithms

A new approach for developing comprehensive agricultural drought index using satellite-derived biophysical parameters and factor analysis method

The accurate assessment of drought and its monitoring is highly depending on the selection of appropriate indices. Despite the availability of countless drought indices, due to variability in environmental properties, a single universally drought index has not been presented yet. In this study, a new approach for developing comprehensive agricultural drought index from satellite-derived biophysical parameters is presented. Therefore, the potential of satellite-derived biophysical parameters for improved understanding of the water status of pistachio (Pistachio vera L.) crop grown in a semiarid area is evaluated. Exploratory factor analysis with principal component extraction method is performed to select the most in?uential parameters from seven biophysical parameters including surface temperature (Ts), surface albedo (a), leaf area index (LAI), soil heat ?ux (Go), soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and net radiation (Rn). Ts and Gowere found as the most effective parameters by this method. However, Ts, LAI, a, and SAVI that accounts for 99.6 % of the total variance of seven inputs were selected to model a new biophysical water stress index (BPWSI). The values of BPWSI were stretched independently and compared with the range of actual evapotranspiration estimated through well-known METRIC (mapping evapotranspiration at high resolution with internal calibration) energy balance model. The results showed that BPWSI can be ef?ciently used for the prediction of the pistachio water status (RMSE of 0.52, 0.31, and 0.48 mm/day on three image dates of April 28, July 17, and August 2, 2010). The study con?rmed that crop water status is accounted by several satellite-based biophysical parameters rather than single parameter