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Air temperature is an important meteorological variable in many fields of our life. However, the availability of air temperature measurements over large geographic areas is often limited by the weather stations spatial distribution inadequacy, their low density and difficulties of data quality and collection. In this context, this study consists to develop four simple models to estimate the three components of air temperature (Tmin, Tmax and Tmean) from remotely sensed land surface temperature (Ts) derived from NOAA-AVHRR images, and based on the international Koppen-Geiger climate classification of Morocco. The results confirmed the existence of good relationships between the three components of measured air temperatures and land surface temperature derived from NOAA-AVHRR images for the main four climate classes of Morocco. The coefficient of determination, R2, varied between 0.69 and 0.80 for Tmin versus Ts, between 0.62 and 0.74 for Tmax versus Ts, and between 0.69 and 0.79 for Tmean versus Ts. The root mean square error varied between 3.1 °C and 3.3 °C for Tmin versus Ts, between 3.2 and 4.1 °C for Tmax versus Ts and between 2.7 and 3.4 °C for Tmean versus Ts. K-fold cross validation method was performed to assess the accuracy and the stability of proposed models. The limited number of proposed models is a great advantage to carry further studies requiring air temperature’s components at larger scale.

In arid and semi-arid countries, drought monitoring is a difficult issue to deal with, mainly due to the low density of precipitation measurement stations. The present study aims to evaluate the ability of the Tropical Rainfall Monitoring Mission (TRMM) 3B42 (v7) to monitor annual precipitation in Morocco. The accuracy of TRMM data to estimate annual rainfall was evaluated. Annual precipitations derived from 5113 daily TRMM 3B42 V7 data were compared to the corresponding in situ rainfall measurements from 23 rain gauges, between 1998 and 2012. The results showed a general, good linear relationship between TRMM and rain gauges data. When considering annual records, the Pearson correlation coefficient, R2, was equal to 0.73 and the Root Mean Square Error, RMSE, was equal to 159.8 mm/year. The correlation between rain gauge measurements and TRMM rainfall has been clearly improved when working with long-term annual average precipitation. The R2 increased to 0.79 and the RMSE decreased to 115.2 mm.

The monitoring of drought statewide is a difficult issue especially when the national network of meteorological stations is sparse or do not cover the entire country. In this paper, rainfall satellite estimates derived from Tropical Rainfall Measuring Mission (TRMM) product have been used to evaluate the ability of remote sensing data to study the trends of annual precipitation in Morocco between 1998 and 2012. The standardized precipitation index, SPI, has been chosen to monitor meteorological drought in Morocco. Firstly, the accuracy of TRMM product to estimate annual rainfall was evaluated. Annual precipitations derived from 5113 daily TRMM data were compared to the corresponding rainfall measurements from 23 rain gauges. The results showed a general good linear relationship between TRMM and rain gauges data. When considering annual record, the Pearson correlation coefficient, R², was equal to 0.73 and the root mean square error, RMSE, was equal to 159.8mm/year. The correlation between rain gauge measurements and TRMM rainfall had been clearly improved when working with long-term annual average precipitation. The R² increased to 0.79 and the RMSE decreased to 115,2mm. Secondly, the Mann-kendall tau coefficient, the Theil Sen slope and the contextual Mann-Kendall significance were used to analyze the SPI trends over Morocco. This analysis showed that mainly two regions appeared to be subject of significant trends during the studied period: The extreme north eastern of Morocco manifests a positive SPI trends and is more and more subject of extreme rainfall while the extreme south of the country is suffering from a decrease of annual precipitation which could represent significant socio-economic risks in these areas.