Tarik Benabdelouahab

Monitoring surface water content using visible and short-wave infrared SPOT-5 data of wheat plots in irrigated semi-arid regions

Irrigated agriculture is an important strategic sector in arid and semi-arid regions. Given the large spatial coverage of irrigated areas, operational tools based on satellite remote sensing can contribute to their optimal management. The aim of this study was to evaluate the potential of two spectral indices, calculated from SPOT-5 high-resolution visible (HRV) data, to retrieve the surface water content values (from bare soil to completely covered soil) over wheat fields and detect irrigation supplies in an irrigated area. These indices are the normalized difference water index (NDWI) and the moisture stress index (MSI), covering the main growth stages of wheat. These indices were compared to corresponding in situ measurements of soil moisture and vegetation water content in 30 wheat fields in an irrigated area of Morocco, during the 2012–2013 and 2013–2014 cropping seasons. NDWI and MSI were highly correlated with in situ measurements at both the beginning of the growing season (sowing) and at full vegetation cover (grain filling). From sowing to grain filling, the best correlation (R2 = 0.86; p < 0.01) was found for the relationship between NDWI values and observed soil moisture values. These results were validated using a k-fold cross-validation methodology; they indicated that NDWI can be used to estimate and map surface water content changes at the main crop growth stages (from sowing to grain filling). NDWI is an operative index for monitoring irrigation, such as detecting irrigation supplies and mitigating wheat water stress at field and regional levels in semi-arid areas.

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

ABSTRACT In this article, correlations to derive minimum (Tmin), mean (Tmean), and maximum (Tmax) air temperatures from land surface temperature (Ts) 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. Ts was first compared with Tmin, Tmean, and Tmax, at the level of nine meteorological stations between 1995 and 2012. This first step showed the existence of good linear correlations between Ts and air temperatures. The coefficient of determination, R2, varied between 0.59 and 0.79 for Ts versus Tmin, between 0.60 and 0.76 for Ts versus Tmax, and between 0.67 and 0.79 for Ts versus Tmean. The root mean square error (RMSE) varied between 2.4°C and 3.9°C for Ts versus Tmin, between 2.5°C and 4.6°C for Ts versus Tmax, and between 2.2°C and 3.8°C for Ts versus Tmean. The mean absolute error (MAE) varied between 2°C and 3.1°C for Ts versus Tmin, between 1.9°C and 3.6°C for Ts versus Tmax, and between 1.8°C and 3°C for Ts versus Tmean. Second, Ts was compared with Tmin, Tmax, and Tmean gridded at the level of agricultural areas of 46 provinces in Morocco. For 42 provinces, the mean value of R2 was 0.71 for both Tmin versus Ts and Tmax versus Ts, and 0.76 for Tmean versus Ts. The mean values of the RMSE and the MAE were 3.1°C and 2.4°C for Ts versus Tmin, 3.6°C and 2.8°C for Tmax versus Ts, and 3°C and 2.3°C for Tmean versus Ts, 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.

Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area

ABSTRACT Surface soil moisture content (SSMC) monitoring constitutes an important parameter to estimate crop water requirements, especially in arid and semi-arid areas. Remote sensing became a useful tool for estimating SSMC. Two approaches were applied for monitoring the SSMC during the 2013/14 cropping season in the irrigated perimeter of Tadla (Morocco) using multispectral bands of Landsat-8 OLI images. The first approach examined the potential of visible and short-wave infrared drought index (VSDI), normalized multi-band drought index (NMDI) and short-wave infrared water stress index (SIWSI), to retrieve SSMC. The second approach attempted to develop a new SSMC model based on evaluation of the correlations between multispectral bands and measured SSMC using a stepwise multiple regression analysis. Results showed that the established model is highly correlated with the measured SSMC at all crop growth stages with R 2 of 0.87, 0.85 and 0.89, for bare soil, partially covered and entirely covered by vegetation, respectively.