Improvement of FAPAR Estimation Under the Presence of Non-Green Vegetation Considering Fractional Vegetation Coverage

The homogeneous turbid medium assumption inherent to the Beer-Lambert’s law can lead to a reduction in the shading effect between leaves when non-green vegetation canopies are present, resulting in an overestimation of the fraction of absorbed photosynthetically active radiation (FAPAR). This paper proposed a method to improve the FAPAR estimation (FAPARFVC) based on Beer-Lambert’s law by incorporating fractional vegetation coverage (FVC). Initially, the canopy-scale leaf area index (LAI) of the green canopy distribution area within the pixel (sample site) was determined based on the FVC. Subsequently, the canopy-scale FAPAR was calculated within the green canopy distribution area, adhering to the assumption of a homogeneous turbid medium in the Beer-Lambert’s law. Finally, the average FAPAR across the pixel (sample site) was calculated based on the FVC. This paper conducted a case study using measured data from the BigFoot Project and grass savanna in Senegal, West Africa, as well as Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR products. The results indicated that the FAPARFVC approach demonstrated superior accuracy compared to the FAPAR determined by MODIS LAI, according to the Beer-Lambert’s law (FAPARLAI) and MODIS FPAR products (FAPARMOD). The mean absolute percentage error of FAPARFVC was 48.2%, which is 25.6% and 52.1% lower than that of FAPARLAI and FAPARMOD, respectively. The mean percentage error of FAPARFVC was 16.8%, which was 71.6% and 73.4% lower than that of FAPARLAI and FAPARMOD, respectively. The improvements in accuracy and the decrease in overestimation for FAPARFVC became more pronounced with increasing FVC compared to FAPARLAI. The findings suggested that the FAPARFVC method enhanced the accuracy of FAPAR estimation under the presence of non-green vegetation canopies. The method can be extended to regional scale FAPAR and gross primary production (GPP) estimations, thereby providing more accurate inputs for understanding its tempo-spatial patterns and drivers.