Mapping fractional vegetation cover in Sub-Saharan rangelands using phenological feature spaces

Abstract

This study introduces a novel approach for mapping annual fractional vegetation cover in Sub-Saharan rangelands. We used Sentinel-2 time series data from October 2022 to October 2023 to derive phenological metrics, including the dry season integral and rate of greenness decline after peak season. Phenological metrics effectively separate woody vegetation from herbaceous plants based on their distinct seasonal patterns, enabling knowledge-based identification of woody, herbaceous and bare surface endmembers by extending the traditional spectral feature space to a phenological feature space (PFS). Our method was robust across precipitation gradients, consistently producing a triangular-shaped PFS. The regression-based unmixing model, trained using simulated mixtures generated from pure endmember time series signals, showed promising predictive performance at 10-m resolution. Validation using unmanned aerial vehicle imagery revealed mean absolute errors of 11.87 %, 13.57 %, and 14.47 % for woody, herbaceous, and bare surface cover respectively, with the model explaining 68 %, 58 %, and 62 % of the variance in these respective cover types. The 10-m resolution maps provide a detailed representation of continuous transitions between vegetation types in semiarid rangelands, and detect distinct spatial patterns associated with rangeland management practices, such as woody vegetation removal. The complementary use of phenometrics for knowledge-based endmember selection and full spectral-temporal information as model input yielded better results than using phenometrics alone. Future applications of this method can potentially enable assessment of temporal trends in cover fractions across multiple years. This study represents a substantial advancement in monitoring capabilities for vegetation composition in African semi-arid environments, offering a foundation for more comprehensive understanding of human-environmental interactions in these ecosystems.