Vegetation canopy water estimation from optical satellite observations

Vegetation canopy water (VCW) plays one connecting role in the coupling of terrestrial carbon-water cycles, and together with soil moisture, identifying the main changes of the terrestrial ecosystem. With regard to the remote sensing technologies, microwave-based VOD (vegetation optical depth) has been widely used as the VCW proxy. The feature of coarse resolution especially for microwave passive as well as mixing of vegetation water and biomass together would limit its more precise application. In spite of some efforts for the hyperspectral thermal and Global Navigation Satellite Systems (GNSS) limited in regional areas, as well as optical indices and initial efforts for AVHRR and SNAP from optical remote sensing, there are still no global operational and mature VCW product in the science community.

To bridge the research gap, this study proposed the unified VCW retrieval algorithm for optical satellites, by improving the methodology developed with some first attempts (e.g., machine learning trained on PROSAIL radiative transfer model simulations). The improvements were implemented by comprehensively parametrizing the VCW related variables (i.e., leaf traits and soil background) in PROSAIL model, based on the largest open integrated global plants (TRY) and soil spectral (OSSL) databases, respectively. In PROSAIL, VCW is expressed as the product of green/leaf area per horizontal ground area (LAI, cm²/cm²) and leaf water content per green area (Cw, g/cm²). In the proposed algorithm, we bridge the quantitative relationship between VCW (LAI *Cw) and simulated TOC reflectance using the machine learning model.

The algorithm was assessed for Landsat8 and Sentinel-2, using the ground measurements distributed over diverse climate and biome types worldwide. The results indicate that the developed VCW exhibits satisfactory performance, with R of 0.731 and unbiased RMSE (ubRMSE) of 0.055 g/cm². Moreover, the proposed VCW achieves reasonable spatial patterns and seasonal changes over diverse vegetation types. The developed VCW product in this study is expected to provide new insights for monitoring global or regional vegetation water variations from optical satellites. With the strength of high spatial resolution compared to the microwave ones in the remote sensing community, the developed VCW would further facilitate the better hydro-ecological applications, especially for the terrestrial carbon-water couplings through vegetation, drought monitoring etc.