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Climate has changed throughout the 4.5 million year history of the globe. At the same time, natural and human activities are affected directly by the global change of Earth system attributes, such as increase in temperature. Land surface temperature (LST) is an important but highly variable climate parameter. Its spatial distribution and the characteristic of its diurnal change over wide areas can only be determined with remote-sensing methods. The power of wireless sensor network (WSN) technology has provided the capability of developing large-scale systems for remote-sensing algorithm and sensor validation.This article presents a new method (sensitivity ∼0.1 • C) for in situ LST measurements. The results of wide in situ LST campaigns carried out during 2009 in four distant and different sites located in northern Morocco are shown: (1) Kasr-Seghir, (2) Targha, (3) Tangier, and (4) Chefchaouen. For this purpose, we used two calibrated radiometers with thermal infrared bands, OSM101 and TESTO845. Finally, during these campaigns, a total of 28,531 measurements were made with the proposed wireless-LST (Wi-LST) system. The preliminary results show a wide variability of the measurements, which is in total accordance with the heterogeneity of the targets' nature. This is encouraging for we are interested in building up a reliable and consistent standard in situ LST measurements database for LST algorithm validations.

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The Split-Window (SW) algorithm has been developed in order to retrieve Land Surface Temperatures (LST) from Thermal InfraRed (TIR) remote sensing data. In this paper, a study has been carried out using MODTRAN 4.0 radiative transfer code simulations using the TIR channels of the Infrared Imager Radiometer Suite (VIIRS) and The Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) Satellites to obtain numerical coefficients of the proposed algorithms. Results from validation, using the standard atmospheric simulation for various situations and the ground truth data sets demonstrate the applicability of the algorithm. A detailed analysis of the estimated total error in LST-SW,  Total(Ts) , shows that the algorithms are able to estimate accurate LST with mean value of about 1.31 K, a minimum of 1.25 K and a maximum of 1.38 K (with an amplitude of 0.13 K), a standard deviation of about 0.04 K and a root mean square error (rmse) of about 1.31 K.