Forest Canopy Water Content Monitoring Using Radiative Transfer Models and Machine Learning

Forests are facing various threats, such as drought, in the context of global climate change. Canopy water content (CWC) is a crucial indicator of forest water stress, mortality, and fire monitoring. However, previous studies on CWC have not adequately simulated forests with heterogeneous and discontinuous canopy structures. At the same time, there is a lack of field validation. This study retrieved the forest CWC across the contiguous U.S. (CONUS) with coupled radiative transfer models (RTMs) and the random forest (RF) algorithm. A Gaussian copula and prior knowledge were used for model parameterization. The results indicated that more accurate simulations of leaf trait dependencies and canopy structure characteristics lead to better CWC inversion. In addition, GeoSail, coupled with PROSPECT-5B, showed good performance (R-2 = 0.68, RMSE = 0.15 kg m(-2), MAE = 0.12 kg m(-2), rRMSE = 12.78%, Bias = -0.036 kg m(-2)) for forest CWC retrieval. Large variation existed in forest CWC, spatiotemporally, and evergreen needle forest (ENF) showed strong CWC capacity. This study underscores the suitability of 3D RTMs for inversing the parameters of forest canopies.

Automated summary

Forests face threats like drought in the context of global climate change. Canopy water content (CWC) serves as an important indicator for forest water stress, mortality, and fire monitoring. This study used radiative transfer models and the random forest algorithm to retrieve forest CWC in the contiguous U.S. The results demonstrated that accurate simulation of leaf traits and canopy structure leads to improved CWC inversion. The study highlights the suitability of 3D radiative transfer models for canopy parameter inversion.