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The area, distribution, and temporal dynamics of anthropogenic impervious surface (AIS) at large scale are significant for environmental, ecological and socio-economic studies. Remote sensing has become an important tool for monitoring large scale AIS, while it remains challenging for accurate extraction of AIS using optical datasets alone due to the high diversity of land covers over large scale. Previous studies indicated the complementary use of synthetic aperture radar (SAR) to improve the AIS estimation, while most of them were limited to local and small scales. The potential of SAR for large scale AIS mapping is still uncertain and underexplored. In this study, first, a machine learning framework incorporating both optical and SAR data based on Google Earth Engine platform was developed for mapping and analyzing the annual dynamics of AIS in China. Feature-level fusion for SAR and optical data across large scale was tested applicable considering the backscattering coefficients, texture measures and spectral characteristics. Improved accuracy (averaged 2% increased overall accuracy and averaged 4% increased Kappa coefficient) and better delineation between the bright impervious surface and bare land was observed comparing with using optical data alone. Second, comprehensive assessment was conducted using high-resolution samples from Google Earth, census data from China Statistic Yearbook and benchmark datasets from the GlobeLand30 and GHSL, demonstrating the feasibility and reliability of the proposed method and results. Last but not the least, we analyzed the spatial and temporal patterns of AIS in China from national, regional and provincial levels. • We incorporated SAR and optical images for large scale impervious surfaces mapping. • Results were validated with high resolution samples, census and benchmark data. • SAR generally improved the overall accuracy by 2%. • SAR better delineated the impervious surface from bare land. [ABSTRACT FROM AUTHOR]

The Soil Moisture Experiment in the Luan River (SMELR) was conducted from 2017 to 2018 in the semi-arid Luan River watershed located in the North of China. One of the objectives of SMELR is to serve as an assessment tool and demonstration for a new Terrestrial Water Resources Satellite (TWRS) concept with one-dimensional synthetic aperture microwave techniques, for which soil moisture retrieval under variable satellite observing configurations (mainly in terms of incidence angels) is the greatest challenge. This proposed mission is targeted to provide continuity for the current satellite L-band microwave observations, and further improve the accuracy and spatial resolution of soil moisture mapping through the synergistic use of active, passive and optical remote sensing data. Multi-resolution, multi-angle and multi-spectral airborne data were obtained four times over a 70 km by 12 km area in the Shandian River basin, and one time over a 165 km by 5 km area that includes the Xiaoluan River basin. The near surface soil moisture (0 cm–5 cm) was measured extensively on the ground in fifty 1 km by 2 km quadrats (targeted to compare with the airborne radiometer), and two hundred and fifty 200 m by 200 m quadrats corresponding to radar observations. Two networks were established for continuous measurement of the soil moisture and temperature profile (3 cm, 5 cm, 10 cm, 20 cm, 50 cm) and precipitation in the Shandian and Xiaoluan River basin, respectively. Supporting ground measurements also included ground temperature, vegetation water content, surface roughness, continuous measurement of microwave emission and backscatter at a pasture site, reflectance of various land cover types, evapotranspiration and aerosol observations. Preliminary results within the experimental area indicate that (1) the near surface soil moisture spatial variability at a 200 m scale was up to ~0.1 cm3/cm3 at an intermediate value of ~0.35 cm3/cm3. (2) The difference of soil and vegetation temperature in grass and croplands reach its maximum of 11 K around solar noon time, and the soil temperature gradient is largest at around 15 P.M. (3) Both the airborne and ground measurements cover a wide range of conditions. The L-band active and passive observations exhibit a large variation of ~30 dB and ~80 K, respectively, corresponding to soil moisture range from 0.1 cm3/cm3 to 0.5 cm3/cm3. The sensitivity of both active and passive data to soil moisture is compared at corresponding spatial resolutions and show high information complementarity for better accuracy and resolution soil moisture retrieval. • First airborne optical and active-passive microwave experiment conducted in China • Demonstration of the active-passive microwave synergy at varying incidence angles • Addressing prelaunch science questions of the terrestrial water resources mission [ABSTRACT FROM AUTHOR]