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Accurate large-area Digital Terrain Models (DTMs) are crucial for many science applications. Spaceborne Synthetic Aperture Radar (SAR) platforms are often used to create these DTMs as they provide an effective tool to collect surface elevations across a wide extent. However, SAR-derived digital elevation models (DEMs) cannot accurately measure ground elevations in the presence of forests. This work demonstrates an approach to estimate terrain elevations from 12 m TanDEM-X by using a convolutional neural network (CNN) trained with ground elevations from ICESat-2 – a spaceborne laser altimeter. This approach demonstrated the ability to estimate terrain elevations from TanDEM-X DEMs for the greater North Carolina area. The CNN estimated terrain saw an improvement in RMSE from 11.28 m to 4.42 m within the entire area of interest, and a focused improvement in RMSE from 12.78 m to 4.95 m in forested areas when compared to ICESat-2. The CNN model outperformed linear, random forest, and gradient boosted regression models using comparable model inputs. This work combines 12-m TanDEM-X data with ICESat-2 profiles, resulting in a new DTM product with accuracy approaching that of reference elevations obtained from satellite laser altimetry in the southeastern United States. • Investigates use of CNN to create DTM with TanDEM-X and ICESat-2 in North Carolina. • CNN outperformed linear, random forest, and gradient boosted regression models tested. • CNN reduced RMSE from 11.28 m to 4.42 m with convolutional neural network. • Establishes possible framework for creating global DTMs. [ABSTRACT FROM AUTHOR]

Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m2]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 – December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g. , AMSR-E), current (e.g. , AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers. [Display omitted] • A multi-sensor semi-physical algorithm is applied to retrieve vegetation moisture. • The water component in vegetation optical depth is disentangled from dry biomass. • Vegetation moisture retrievals at X- and Ku-bands capture the in situ dynamics. • L-band has poorer performance due to lack of in situ samples in wood canopy layers. [ABSTRACT FROM AUTHOR]