Vincent Simonneaux

Identifying main crop classes in an irrigated area using high resolution image time series

We present a robust and reproducible method to map the main land cover classes in an irrigated area near Marrakech (bare soil, herbaceous crops, trees on bare soil, trees on herbaceous understory), using a time series of 9 SPOT high resolution images. The images were first radiometrically corrected in reflectance using a physical model (SMAC), and then applying an adjustment based on invariant objects located on the scene. The 9 images were then used to generate a NDVI profile for each pixel, which was analyzed using simple decision rules to assign it to a landcover class, considering simple criteria like the minimum, maximum, and dynamics of the NDVI.

Estimation of Irrigation Water Pumping by Remote Sensing: Application of the SAMIR Model to Citrus under Mediterranean Climate Conditions

Agricultural intensification has led to overexploitation of water resources, in particular of the underground resource. Estimating groundwater exploitation remains a serious problem in arid and semi-arid countries such as Algeria. The objective of this work is to estimate volumes of pumped irrigation water using the SAtellite Monitoring of Irrigation (SAMIR) model from satellite imagery. Eleven (11) Landsat-TM images covering an area of 20 hectares of citrus orchards with a drip irrigation system were used. Irrigation volumes simulated by SAMIR were compared with volumes of pumped irrigation water. The latter were recorded digitally by specific programmers over a one-year observation period. The results shows a slight discrepancy between total simulated and observed volumes, 138,782 m and 135,688.5 m, respectively. The index of agreement d and RMSE were 80.94 and 0.11, respectively, while the ratio of observed to simulated volumes varied from 0.76 to 1.27. 65% of the values compared differed by less than 10%. A significant correlation was found between the two methods. This confirms the feasibility of estimating pumping by remote sensing without calculating theoretical crop water requirements.

Evaporation-based disaggregation of surface soil moisture data: The dispatch method, the CATDS product and on-going research

The soil evaporation is under atmospheric conditions non-limited in energy-strongly linked to the near-surface soil moisture sensed by microwave radiometers. This has been the rationale for developing the DisPATCh (Disaggregation based on Physical And Theoretical scale Change) method, which relies on thermal-derived evaporation to improve the spatial resolution of SMOS (Soil Moisture and Ocean Salinity) like data. In practice, the disaggregation scheme estimates the 0–5 cm soil moisture at 1 km resolution by combining 40 km SMOS soil moisture, 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) data, and a multi-scale soil evaporation model. This paper provides an overview of 1) the current status and main assumptions of DisPATCh, 2) the DisPATCh-based processor implemented in the Centre Aval de Traitement des Données SMOS (CATDS), and 3) related ongoing research including advanced modeling of soil evaporation and the prospect of coupling thermal- and radar-based soil moisture downscaling approaches.

Monitoring irrigation volumes using high-resolution NDVI image time series: calibration and validation in the Kairouan plain (Tunisia)

The increasing availability of high resolution high repetitively VIS-NIR remote sensing, like the forthcoming Sentinel-2 mission to be launched in 2015, offers unprecedented opportunity to improve agricultural monitoring. In this study, regional evapotranspiration and crop water consumption were estimated over an irrigated area located in the Kairouan plain (central Tunisia) using the FAO-56 dual crop coefficient water balance model combined with NDVI image time series providing estimates of the actual basal crop coefficient (Kcb) and vegetation fraction cover. Three time series of high-resolution SPOT5 images have been acquired for the 2008-2009, 2011-2012 and 2012-2013 hydrological years. We also benefited from a SPOT4 time series acquired in the frame of the SPOT4-Take5 experiment. The SPOT5 images were radiometrically corrected, first, using the SMAC6s Algorithm, and then improved using invariant objects located on the scene. The method was first calibrated using ground measurements of evapotranspiration achieved using eddy-correlation devices installed on irrigated wheat and barley plots. For other crops for which no calibration data was available, parameters were taken from bibliography. Then, the model was run to spatialize irrigation over the whole area and a validation was done using cumulated seasonal water volumes obtained from ground survey for three irrigated perimeters. In a subsequent step, evapotranspiration estimates were obtained using a large aperture scintillometer and were used for an additional validation of the model outputs.