Relationships between the three components of air temperature and remotely sensed land surface temperature of agricultural areas in Morocco

摘要

In this article, correlations to derive minimum (T-min), mean (T-mean), and maximum (T-max) air temperatures from land surface temperature (T-s) in Morocco are studied. Land surface temperature was derived from 349 decadal cloud-free images acquired by the Advanced Very High Resolution Radiometer sensor, onboard the National Oceanic and Atmospheric Administration's satellites. T-s was first compared with T-min, T-mean, and T-max, at the level of nine meteorological stations between 1995 and 2012. This first step showed the existence of good linear correlations between T-s and air temperatures. The coefficient of determination, R-2, varied between 0.59 and 0.79 for T(s)versus T-min, between 0.60 and 0.76 for T(s)versus T-max, and between 0.67 and 0.79 for T(s)versus T-mean. The root mean square error (RMSE) varied between 2.4 degrees C and 3.9 degrees C for T(s)versus T-min, between 2.5 degrees C and 4.6 degrees C for T(s)versus T-max, and between 2.2 degrees C and 3.8 degrees C for T(s)versus T-mean. The mean absolute error (MAE) varied between 2 degrees C and 3.1 degrees C for T(s)versus T-min, between 1.9 degrees C and 3.6 degrees C for T(s)versus T-max, and between 1.8 degrees C and 3 degrees C for T(s)versus T-mean. Second, T-s was compared with T-min, T-max, and T-mean gridded at the level of agricultural areas of 46 provinces in Morocco. For 42 provinces, the mean value of R-2 was 0.71 for both T(min)versus T-s and T(max)versus T-s, and 0.76 for T(mean)versus T-s. The mean values of the RMSE and the MAE were 3.1 degrees C and 2.4 degrees C for T(s)versus T-min, 3.6 degrees C and 2.8 degrees C for T(max)versus T-s, and 3 degrees C and 2.3 degrees C for T(mean)versus T-s, respectively. Finally, the accuracy of the regression models between air and surface temperatures was tested using a k-fold cross-validation method that showed high stability of these models. The relationships obtained in this work could be very useful for further monitoring and modelling agriculture and meteorological parameters.