Hyperspectral constraints reduce bias in ECOSTRESS evapotranspiration and drought indicators

Droughts in Africa's drylands threaten regional food security and global agricultural markets. Early-warning systems increasingly rely on Earth Observation (EO), yet precipitation-based indicators often fail to detect emerging vegetation water stress. With new low-Earth-orbit missions, evapotranspiration (ET), which represents actual land-surface water flux, and ET-derived metrics such as the Evaporative Stress Index (ESI) have become essential. ECOSTRESS provides ~70 m sub-daily land surface temperature observations for ET estimation via the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model. However, PT-JPL often exhibits positive ET bias in drylands, increasing the risk of drought omission errors. We evaluated whether hyperspectral vegetation indices (HVIs) can reduce these biases using multi-year field spectrometry, eddy covariance fluxes, and EnMAP/PRISMA imagery in a Kenyan dryland experiment. On the independent validation subset, the standard PT-JPL over- estimated ET by 21.8% (mean observed latent heat = 4.86 MJ m − 2 d − 1 ). Incorporating HVIs reduced bias to 3.5% when constraining soil evaporation and to − 6.4% when applied to both canopy and soil components, while also improving other goodness-of-fit metrics. Bias reduction occurred through two mechanisms: (i) alleviating NDVI saturation, which strengthened canopy constraints under wetter conditions, and (ii) reformulating the soil- moisture constraint using hyperspectral reflectance, thereby limiting soil-evaporation inflation under humid and transitional conditions. These improvements were consistent across hydrological periods and sensor plat- forms. The findings demonstrate that narrowband spectral information enhances ET partitioning and directly support upcoming narrowband–thermal missions (e.g., CHIME, Landsat Next, LSTM, S2NG, SBG) by improving ET-based drought early-warning in moisture-limited environments.