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)

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/m 2 and mean bias error (MBE) = −23 W/m 2 for H and an R = 0.82, RMSE = 88 W/m 2 and MBE = 45 W/m 2 for LE. This study opens perspectives for the monitoring of convective and evaporative fluxes over heterogeneous landscape based on medium resolution satellite products.

An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

“…The recently developed TSEB-SM is fully described in Ait Hssaine et al (2018b). The equations and sub-equations used in TSEB-SM are provided in Table 2; the main equations are given below.…”

“…In Ait Hssaine et al (2018b), an innovative calibration approach of α PT , a rss and b rss is developed from in situ SM and LST data (Ait Hssaine et al, 2018b). The calibration methodology is briefly explained below.…”

“…The retrieved α PT is then smoothed as in Ait Hssaine et al (2018b) to remove outliers and to reduce uncertainties at the daily timescale. The smoothed values of α PT range from 0 to 1.54, 0 to 1.38 and 0.45 to 1.43 for S1, S2 and S3, respectively.…”

“…This assumption requires few input data and allows a precise estimate of potential ET (Fisher et al, 2008). Nevertheless, several studies (Ait Hssaine et al, 2018b;Fisher et al, 2008;Jin et al, 2011;Yang et al, 2015) have stressed that the PT coefficient cannot be considered a constant value, as it is influenced by several parameters. Other authors (Gonzalez-dugo et al, 2009;Long and Singh, 2012;Morillas et al, 2014) reported that the PT approach may overestimate the canopy ET, especially for low soil wetness, and/or sparse vegetation cover, because it does not include a reasonable reduction of the initial canopy ET under stress conditions.…”

“…Therefore, assumptions (about either soil or vegetation fluxes) are commonly required. To avoid such assumptions, an energy balance model, TSEB-SM, was recently developed by Ait Hssaine et al (2018b) in order to consider the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (f c ) normally used. While TSEB-SM has been successfully tested using in situ measurements, this paper represents its first evaluation in real life using 1 km resolution satellite data, comprised of MODIS (MODerate resolution Imaging Spectroradiometer) for LST and f c data and 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations.…”