AUTHOR=Lo Adama , Diouf Abdoul Aziz , Diedhiou Ibrahima , Bassène Cyrille Djitamagne Edouard , Leroux Louise , Tagesson Torbern , Fensholt Rasmus , Hiernaux Pierre , Mottet Anne , Taugourdeau Simon , Ngom Daouda , Touré Ibra , Ndao Babacar , Sarr Mamadou Adama 

TITLE=Dry season forage assessment across senegalese rangelands using earth observation data

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.931299

DOI=10.3389/fenvs.2022.931299

ISSN=2296-665X

ABSTRACT=Strengthening of feed security in the Sahel is urgently needed given climate change and growing human population. A prerequisite to this is sustainable use of rangeland forage resources for livestock. Many studies have focused on the assessment of rangeland resources during the rainy season, while only a few have focused on the dry season which is the longest and demanding period for livestock in Sahelian rangelands. The objective of this study is to develop remote sensing-based models for estimating dry season forage vegetation mass. To that end, 29 vegetation indices calculated from each of the MODIS-MCD43A4 (500m), Landsat-8 (30m) and Sentinel-2 (10m) satellite products were used and tested against in-situ data collected during three field-measurement campaigns in 2021 at eleven monitoring sites across Senegalese rangelands. Four statistical models were tested including: Random Forest, Gradient Boosting Machines, simple linear and multiple linear regressions. The two main vegetation mass variables modelled from remote sensing imagery were the standing herbaceous and litter mass (BH), and total foliage mass (BT) with mass of woody plants added to BH. Overall, Sentinel-2 data provided the best performance for the assessment of BH with a multiple linear regression (R² = 0.74; RMSE = 378 kg‧DM/ha) using NDI5 (Normalized Difference Index5), GRCI (Green Residue Cover Index), SRI (Simple Ratio Index), TCARI (Transformed Chlorophyll Absorption in Reflectance Index), DFI (Dead Fuel Index) indices. For BT, the best model performance was obtained from same sensor/statistical model/indices including also RVI3 (Ratio Vegetation Index3) (R² = 0.78; RMSE = 496 kg‧DM/ha). Results showed the suitability of combining the Red, Green, Blue, NIR, SWIR1 and SWIR2 bands in monitoring forage availability during the dry season. Our study revealed that the spectral richness of the optical sensor systems Sentinel-2, Landsat-8 and MODIS-MCD43A4 allowed for accurate assessments of dry-season forage mass of semi-arid rangelands. Adding to this the high spatial and temporal resolution of Sentinel-2 satellite imagery makes this a promising data source for timely monitoring. These findings can support the monitoring of the animal feed balance in Sahelian countries, and contribute to enhance the resilience of pastoralism towards feed shortage through early warning systems.