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Forests play a pivotal role in carbon and water cycles by governing the exchanges of CO2and H2O between the terrestrial biosphere and the atmosphere. The evapotranspiration (ET) is the variable, which links these cycles. The eddy covariance (EC) method provides direct, high-frequency observations of ET of an ecosystem. The present study was carried out in a moist deciduous plant functional type (PFT) of northwest Himalayan foothills of India to estimate ET using the EC flux tower measurements and to study its biophysical controls from 2016 to 2018. The variability of sensible (H) and latent (LE) heat fluxes was also studied. The mean diurnal variation in H was from − 1.31 to 109.35 Wm−2whereas LE ranged from 4.47 to 186.89 Wm−2. The mean annual ET for 2016–2018 was found to be 693.67 ± 46.70 mm year−1. The highest diurnal variability in ET was witnessed during the post monsoon season followed by the monsoon, winter, and dry summer seasons. A relative weight analysis with multiple regression model was implemented to understand the control of biophysical variables on ET at an 8-day time scale. A combination of incoming solar radiation (Rg), leaf area index (LAI), vapour pressure deficit (VPD), air temperature (Tair), soil water content (SWC), and precipitation was able to explain 73% of the variability of ET at 8-day time scale. The analysis revealed that in the moist deciduous PFT the ET was limited by the availability of energy. The present study is the first-ever attempt to report the direct estimates of ET for an Indian forest.

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