Abstract:Water scarcity is one of the main factors limiting agricultural development in semi-arid areas. Remote sensing has long been used as an input for crop water balance monitoring. The increasing availability of high resolution high repetitivity remote sensing (forthcoming Sentinel-2 mission) offers an unprecedented opportunity to improve this monitoring. In this study, regional crop water consumption was estimated with the SAMIR software (SAtellite Monitoring of IRrigation) using the FAO-56 dual crop coefficient water balance model fed with high resolution NDVI image time series providing estimates of both the actual basal crop coefficient and the vegetation fraction cover. Three time series of SPOT5 images have been acquired over an irrigated area in central Tunisia along with a SPOT4 time series acquired in the frame of the SPOT4-Take5 experiment, which occurred during the first half of 2013. Using invariant objects located in the scene, normalization of the SPOT5 time series was realized based on the SPOT4-Take5 time series. Hence, a NDVI time profile was generated for each pixel. The operationality and accuracy of the SAMIR tool was assessed at both plot scale (calibration based on evapotranspiration ground measurements) and perimeter scale (irrigation volumes) when several land use types,
OPEN ACCESSRemote Sens. 2015, 7 13006 irrigation and agricultural practices are intertwined in a given landscape. Results at plot scale gave after calibration an average Nash efficiency of 0.57 between observed and modeled evapotranspiration for two plots (barley and wheat). When aggregated for the whole season, modeled irrigation volumes at perimeter scale for all campaigns were close to observed ones (resp. 135 and 121 mm, overestimation of 11.5%). However, spatialized evapotranspiration and irrigation volumes need to be improved at finer timescales.
Abstract. In semiarid areas, agricultural production is restricted by water
availability; hence, efficient agricultural water management is a major issue.
The design of tools providing regional estimates of evapotranspiration (ET),
one of the most relevant water balance fluxes, may help the sustainable
management of water resources. Remote sensing provides periodic data about actual vegetation temporal
dynamics (through the normalized difference vegetation index, NDVI) and water
availability under water stress (through the surface
temperature Tsurf),
which are crucial factors controlling ET. In this study, spatially distributed estimates of ET (or its energy
equivalent, the latent heat flux LE) in the Kairouan plain (central Tunisia)
were computed by applying the Soil Plant Atmosphere and Remote Sensing
Evapotranspiration (SPARSE) model fed by low-resolution remote sensing data
(Terra and Aqua MODIS). The work's goal was to assess the operational use of
the SPARSE model and the accuracy of the modeled (i) sensible heat flux (H)
and (ii) daily ET over a heterogeneous semiarid landscape with complex
land cover (i.e., trees, winter cereals, summer vegetables). SPARSE was run to compute instantaneous estimates of H and LE fluxes at the
satellite overpass times. The good correspondence (R2 = 0.60 and 0.63
and RMSE = 57.89 and 53.85 W m−2 for Terra and Aqua,
respectively) between instantaneous H estimates and large aperture
scintillometer (XLAS) H measurements along a path length of 4 km over the
study area showed that the SPARSE model presents satisfactory accuracy.
Results showed that, despite the fairly large scatter, the instantaneous LE
can be suitably estimated at large scales (RMSE = 47.20 and 43.20 W m−2
for Terra and Aqua, respectively, and R2 = 0.55 for both
satellites). Additionally, water stress was investigated by comparing
modeled (SPARSE) and observed (XLAS) water stress values; we found that most
points were located within a 0.2 confidence interval, thus the general
tendencies are well reproduced. Even though extrapolation of instantaneous
latent heat flux values to daily totals was less obvious, daily ET estimates
are deemed acceptable.
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