Determining Optimal New Generation Satellite Derived Metrics for Accurate C3 and C4 Grass Species Aboveground Biomass Estimation in South Africa

Abstract

While satellite data has proved to be a powerful tool in estimating C3 and C4 grass species Aboveground Biomass (AGB), finding an appropriate sensor that can accurately characterize the inherent variations remains a challenge. This limitation has hampered the remote sensing community from continuously and precisely monitoring their productivity. This study assessed the potential of a Sentinel 2 MultiSpectral Instrument, Landsat 8 Operational Land Imager, and WorldView-2 sensors, with improved earth imaging characteristics, in estimating C3 and C4 grasses AGB in the Cathedral Peak, South Africa. Overall, all sensors have shown considerable potential in estimating species AGB; with the use of different combinations of the derived spectral bands and vegetation indices producing better accuracies. However, WorldView-2 derived variables yielded better predictive accuracies (R 2 ranging between 0.71 and 0.83; RMSEs between 6.92% and 9.84%), followed by Sentinel 2, with R 2 between 0.60 and 0.79; and an RMSE 7.66% and 14.66%. Comparatively, Landsat 8 yielded weaker estimates, with R 2 ranging between 0.52 and 0.71 and high RMSEs ranging between 9.07% and 19.88%. In addition, spectral bands located within the red edge (e.g., centered at 0.705 and 0.745 µm for Sentinel 2), SWIR, and NIR, as well as the derived indices, were found to be very important in predicting C3 and C4 AGB from the three sensors. The competence of these bands, especially of the free-available Landsat 8 and Sentinel 2 dataset, was also confirmed from the fusion of the datasets. Most importantly, the three sensors managed to capture and show the spatial variations in AGB for the target C3 and C4 grassland area. This work therefore provides a new horizon and a fundamental step towards C3 and C4 grass productivity monitoring for carbon accounting, forage mapping, and modelling the influence of environmental changes on their productivity.

Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques

“…However, according to Wang et al, SWIR bands centered at 1649 nm and 1722 nm were highly correlated with leaf dry matter content [78]. A similar study by Shoko et al also showed that SWIR centered at 2190 nm contributed more to biomass estimation over time [79]. In addition, the green and blue bands were found to be sensitive to corn biomass.…”

The Effects on Greenhouse Gas Emissions of Ecological Intensification of Meat Production with Rainfed Sown Biodiverse Pastures

“…The key parameter missing to perform such an evaluation is a regionalized estimation of the yield of SBP. A remote sensing approach is a viable alternative to estimate regionalized aboveground biomass productivity [55,56]. This approach also has limitations, such as, for example, the influence of tree cover.…”

Characterizing Livestock Production in Portuguese Sown Rainfed Grasslands: Applying the Inverse Approach to a Process-Based Model

“…AGP is usually measured through destructive methods of biomass estimation and is limited to small area due to the destructive nature, time, expense and labor involved [66][67][68]. Other non-destructive methods have been proposed, e.g., using remote sensing imaging [69,70]. Nevertheless, the influence of tree cover in "Montado" agri-forestry regions is a relevant limitation to the use of remote sensing methods as well as our approach, as high tree cover density also influences AGP [71].…”