Influence of the number of included sites in the model development.

<p>Average root mean squared error (RMSE) of the model with respect to the number of included sites. Statistics are based on developing the model 1000 times for each included sites size.</p

Model evaluation.

<p>a) Model evaluation at each site showing the root mean squared error (RMSE) to MODIS-NDVI. The dashed line shows the average value for all sites. Numbers to the right indicate SOS differences between model NDVI and MODIS-NDVI in 8 day time steps (SOS global model–SOS MODIS). b) MODIS-NDVI vs Model NDVI for all sites. Dashed line represents the 1:1 line. SOS: start of season.</p

Site information.

<p>Site information.</p

Map showing site locations of study.

<p>Sites used for study are located in Africa (10), Australia (2), North America (2), and Europe (1).</p

Visual representation of the steps taken in model selection.

<p>The five steps shown are repeated until all combinations are found. DayL is day length in hours, log(sm) is the natural logarithm of soil moisture, and smmap is soil moisture multiplied with mean annual precipitation (m/year). BIC is the Bayesian information Criterion used to evaluate.</p

Variable importance analysis.

<p>a) Soil moisture variable importance related to tree cover. b) Soil moisture variable importance related to mean annual precipitation. Correlation coefficients (r) are for both panels shown next to the linear regression line. Day length importance (in %) can be calculated by taking 100-(soil moisture importance).</p

Image3_Dry season forage assessment across senegalese rangelands using earth observation data.jpg

Strengthening of feed security in the Sahel is urgently needed given the 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 most 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 (500 m), Landsat-8 (30 m), and Sentinel-2 (10 m) 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, namely, random forest, gradient boosting machines, and simple linear and multiple linear regressions. The two main vegetation mass variables modeled from remote sensing imagery were the standing herbaceous and litter dry mass (BH) and total forage dry mass (BT) with a dry mass of woody plant leaves added to BH. Overall, Sentinel-2 data provided the best performance for the assessment of BH with 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), and DFI (Dead Fuel Index) indices. For BT, the best model was also obtained from Sentinel-2 data, including 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 enhancing the resilience of pastoralism toward feed shortage through early warning systems.</p

Image4_Dry season forage assessment across senegalese rangelands using earth observation data.jpg

Strengthening of feed security in the Sahel is urgently needed given the 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 most 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 (500 m), Landsat-8 (30 m), and Sentinel-2 (10 m) 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, namely, random forest, gradient boosting machines, and simple linear and multiple linear regressions. The two main vegetation mass variables modeled from remote sensing imagery were the standing herbaceous and litter dry mass (BH) and total forage dry mass (BT) with a dry mass of woody plant leaves added to BH. Overall, Sentinel-2 data provided the best performance for the assessment of BH with 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), and DFI (Dead Fuel Index) indices. For BT, the best model was also obtained from Sentinel-2 data, including 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 enhancing the resilience of pastoralism toward feed shortage through early warning systems.</p

Image5_Dry season forage assessment across senegalese rangelands using earth observation data.jpg

Strengthening of feed security in the Sahel is urgently needed given the 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 most 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 (500 m), Landsat-8 (30 m), and Sentinel-2 (10 m) 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, namely, random forest, gradient boosting machines, and simple linear and multiple linear regressions. The two main vegetation mass variables modeled from remote sensing imagery were the standing herbaceous and litter dry mass (BH) and total forage dry mass (BT) with a dry mass of woody plant leaves added to BH. Overall, Sentinel-2 data provided the best performance for the assessment of BH with 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), and DFI (Dead Fuel Index) indices. For BT, the best model was also obtained from Sentinel-2 data, including 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 enhancing the resilience of pastoralism toward feed shortage through early warning systems.</p

The African Regional Greenhouse Gases Budget (2010–2019)

As part of the REgional Carbon Cycle Assessment and Processes Phase 2 (RECCAP2) project, we developed a comprehensive African Greenhouse gases (GHG) budget covering 2000 to 2019 (RECCAP1 and RECCAP2 time periods), and assessed uncertainties and trends over time. We compared bottom‐up process‐based models, data‐driven remotely sensed products, and national GHG inventories with top‐down atmospheric inversions, accounting also for lateral fluxes. We incorporated emission estimates derived from novel methodologies for termites, herbivores, and fire, which are particularly important in Africa. We further constrained global woody biomass change products with high‐quality regional observations. During the RECCAP2 period, Africa's carbon sink capacity is decreasing, with net ecosystem exchange switching from a small sink of −0.61 ± 0.58 PgC yr−1 in RECCAP1 to a small source in RECCAP2 at 0.16 (−0.52/1.36) PgC yr−1. Net CO2 emissions estimated from bottom‐up approaches were 1.6 (−0.9/5.8) PgCO2 yr−1, net CH4 were 77 (56.4/93.9) TgCH4 yr−1 and net N2O were 2.9 (1.4/4.9) TgN2O yr−1. Top‐down atmospheric inversions showed similar trends. Land Use Change emissions increased, representing one of the largest contributions at 1.7 (0.8/2.7) PgCO2eq yr−1 to the African GHG budget and almost similar to emissions from fossil fuels at 1.74 (1.53/1.96) PgCO2eq yr−1, which also increased from RECCAP1. Additionally, wildfire emissions decreased, while fuelwood burning increased. For most component fluxes, uncertainty is large, highlighting the need for increased efforts to address Africa‐specific data gaps. However, for RECCAP2, we improved our overall understanding of many of the important components of the African GHG budget that will assist to inform climate policy and action.</p