Mapping Forest Height and Aboveground Biomass by Integrating ICESat‐2, Sentinel‐1 and Sentinel‐2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India

The present study aims to map forest canopy height by integrating ICESat‐2 and Sentinel‐1 data and investigate the effect of integrating forest canopy height information with Sentinel‐2 data‐derived spectral variables on the prediction of spatial distribution of forest aboveground biomass (AGB). Random forest (RF) algorithm was used to develop forest canopy height and AGB models. It was observed that ICESat‐2 and Sentinel‐1 based model was able to predict forest canopy height with R2= 0.84 and %RMSE = 4.48%. Two forest AGB models were developed, with only spectral variables and by incorporating forest height information with spectral variables. The results reflected that incorporation of forest canopy height in the forest AGB model improved the accuracy of the AGB predictions (R2= 0.83, %RMSE = 4.64%). The study presents a comprehensive methodology for mapping forest canopy height and AGB. Estimation and mapping of forest biomass are vital to understand the role of forests in the global carbon budget and climate. Forest canopy height is an important indicator of biomass, carbon stock, and forest productivity and hence, incorporation of accurate forest canopy height information can result in improved estimation of forest biomass. The present study aims to map the forest canopy height and analyses the effect of including the canopy height information on aboveground biomass prediction by the synergistic use of multi‐sensor earth observation data using a machine learning algorithm. It presents a comprehensive methodology for forest canopy height and biomass estimation using active and passive remote sensing data. The developed approach can be implemented for mapping as well as monitoring the forest biomass/carbon stock and has huge relevance to carbon budgeting, climate conventions, and sustainable management of forests. The study was conducted using freely available satellite data, algorithm/software, and platform which makes it easily replicable in a time and cost‐effective manner. Forest canopy height was mapped by integrating ICESat‐2 canopy height & Sentinel‐1 backscatter & texture values using Random Forest (RF)Forest canopy height and Sentinel‐2 derived variables were used to map spatial distribution of forest aboveground biomass (AGB) using RFThe study presents a novel methodology for mapping AGB using ICESat‐2 data in combination with microwave and optical satellite data Forest canopy height was mapped by integrating ICESat‐2 canopy height & Sentinel‐1 backscatter & texture values using Random Forest (RF) Forest canopy height and Sentinel‐2 derived variables were used to map spatial distribution of forest aboveground biomass (AGB) using RF The study presents a novel methodology for mapping AGB using ICESat‐2 data in combination with microwave and optical satellite data