Combining a Two Source Energy Balance Model Driven by MODIS and MSG-SEVIRI Products with an Aggregation Approach to Estimate Turbulent Fluxes over Sparse and Heterogeneous Vegetation in Sahel Region (Niger)

Bouchra Hssaine,Bernard Cappelaere,Ghani Chehbouni,Olivier Merlin,Salah Er-Raki,Jamal Elfarkh,Aurore Brut,Jamal Ezzahar,Lionel Jarlan,Said Khabba

doi:10.3390/rs10060974

Abstract

Estimates of turbulent fluxes (i.e., sensible and latent heat fluxes H and LE) over heterogeneous surfaces is not an easy task. The heterogeneity caused by the contrast in vegetation, hydric and soil conditions can generate a large spatial variability in terms of surface–atmosphere interactions. This study considered the issue of using a thermal-based two-source energy model (TSEB) driven by MODIS (Moderate resolution Imaging Spectroradiometer) and MSG (Meteosat Second Generation) observations in conjunction with an aggregation scheme to derive area-averaged H and LE over a heterogeneous watershed in Niamey, Niger (Wankama catchment). Data collected in the context of the African Monsoon Multidisciplinary Analysis (AMMA) program, including a scintillometry campaign, were used to test the proposed approach. The model predictions of area-averaged turbulent fluxes were compared to data acquired by a Large Aperture Scintillometer (LAS) set up over a transect about 3.2 km-long and spanning three vegetation types (millet, fallow and degraded shrubs). First, H and LE fluxes were estimated at the MSG-SEVIRI grid scale by neglecting explicitly the subpixel heterogeneity. Moreover, the impact of upscaling the model’s inputs was investigated using in-situ input data and three aggregation schemes of increasing complexity based on MODIS products: a simple averaging of inputs at the MODIS resolution scale, another simple averaging scheme that considers scintillometer footprint extent, and the weighted average of inputs based on the footprint weighting function. The H and LE simulated using the footprint weighted method were more accurate than for the two other aggregation rules despite the heterogeneity of the landscape. The statistical values are: correlation coefficient (R) = 0.71, root mean square error (RMSE) = 63 W/m2 and mean bias error (MBE) = −23 W/m2 for H and an R = 0.82, RMSE = 88 W/m2 and MBE = 45 W/m2 for LE. This study opens perspectives for the monitoring of convective and evaporative fluxes over heterogeneous landscape based on medium resolution satellite products.

Highlights

The Sahelian region is one of the most vulnerable areas to climate variability and change due to the scarcity of water resources, associated with a high evaporative demand and low and irregular rainfall
For the degraded shrubs site, eddy covariance (EC) system consisted of a CSAT3 and a Krypton hygrometer (KH20, Campbell Scientific Ltd.) and the fluxes were calculated using the ECpack software after performing planar fit corrections [51], correcting the sonic temperature for the presence of humidity [52], frequency response corrections for slow apparatus and path length integration [53], the inclusion of the mean vertical velocity according to Webb et al [54] and oxygen correction for the Krypton hygrometer which is sensitive to O2 [55]
The two-source energy balance (TSEB) predictions of the convective fluxes were evaluated at the scale of the scintillometer footprint: (1) by aggregating the station scale predictions using the in-situ observations of albedo, Ts and leaf area index (LAI), which constitutes the ideal case; (2) by using 3 km Ts observations from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument directly at the scale of the scintillometer measurements; and (3) by testing three aggregation schemes of MODIS products to assess if a better representation of heterogeneity based on the 1-km products improves our large-scale estimates

Summary

Introduction

The Sahelian region is one of the most vulnerable areas to climate variability and change due to the scarcity of water resources, associated with a high evaporative demand and low and irregular rainfall. Several land surface models have been developed to estimate the evapotranspiration (ET) These models are classified into three different categories: (i) conceptual models that are diagnostic and do not incorporate temporal information about the surface state such as the FAO-56 approach [6] which provides only a daily estimation of the evapotranspiration by modeling the evaporative function based on an empirical coefficient; (ii) surface energy balance (SEB) models which calculate ET as a residual term of the surface energy balance (TSEB [7], Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) [8], and SEBS [9]); and (iii) the mechanistic models which resolve both water and energy budget equations in coupled manner and are often called soil–vegetation–atmosphere transfer models (SVAT models) such as Suivi de l’Etat Hydrique des Sols (SEtHyS) [10], Interaction Soil Biosphere Atmosphere (ISBA) [11], a Simple Soil-Plant-Atmosphere Transfer model (SiSPAT) [12] and the Interactive Canopy Radiation Exchange (ICARE) [13]. This represents a significant advantage in semi-arid areas characterized by sparse meteorological networks and high spatio-temporal variability of precipitation

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