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SAR and LiDAR remote sensing have already shown the potential of active sensors for forest parameter retrieval. SAR sensor in its fully polarimetric mode has an advantage to retrieve scattering property of different component of forest structure and LiDAR has the capability to measure structural information with very high accuracy. This study was focused on retrieval of forest aboveground biomass (AGB) using Terrestrial Laser Scanner (TLS) based point clouds and scattering property of forest vegetation obtained from decomposition modelling of RISAT-1 fully polarimetric SAR data. TLS data was acquired for 14 plots of Timli forest range, Uttarakhand, India. The forest area is dominated by Sal trees and random sampling with plot size of 0.1 ha (31.62m*31.62m) was adopted for TLS and field data collection. RISAT-1 data was processed to retrieve SAR data based variables and TLS point clouds based 3D imaging was done to retrieve LiDAR based variables. Surface scattering, double-bounce scattering, volume scattering, helix and wire scattering were the SAR based variables retrieved from polarimetric decomposition. Tree heights and stem diameters were used as LiDAR based variables retrieved from single tree vertical height and least square circle fit methods respectively. All the variables obtained for forest plots were used as an input in a machine learning based Random Forest Regression Model, which was developed in this study for forest AGB estimation. Modelled output for forest AGB showed reliable accuracy (RMSE = 27.68 t/ha) and a good coefficient of determination (0.63) was obtained through the linear regression between modelled AGB and field-estimated AGB. The sensitivity analysis showed that the model was more sensitive for the major contributed variables (stem diameter and volume scattering) and these variables were measured from two different remote sensing techniques. This study strongly recommends the integration of SAR and LiDAR data for forest AGB estimation.

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Abstract. This study evaluates the all-sky GPM/GMI radiances towards assimilation in regional mesoscale model at 183 ± 7 GHz. The radiative transfer model (RTM) namely RTTOV-SCATT is used for the simulation of three tropical cyclones (hudhud, vardah and kyant respectively). Within the RTM, the performance of non-spherical Discrete Dipole Approximation (DDA) shapes (sector snowflake, 6-bullet rosette, block-column and thinplate) are evaluated. The input data used in RTTOV-SCATT includes vertical hydrometeor profiles, humidity and surface fluxes. In addition, the first guess simulations from Weather Research Forecast (WRF) model were executed at 15 km resolution using ERA-Interim reanalysis datasets. Results indicate that observed minus first guess (FG departures) are symmetric with DDA shapes. The normalized probability density function of FG departures shows large number of spatially correlated samples between clear-sky and poorly forecasted region. Quality control (QC) method was performed to eliminate large FG departures due to instrumental anomalies or poor forecast of clouds and precipitation. The goodness of fit test, h-statistics and skewness of observed and simulated distribution show optimum results for thinplate shape in all the convective events. We also tested the high resolution ERA-5 reanalysis datasets for the simulation of all-sky radiances using thinplate shape. Results illustrate a potential to integrate the GMI sensor data within a WRF data assimilation system.