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ABSTRACTUrban vertical features are crucial for understanding urban morphology. However, long-term information on three-dimensional buildings, which are important fundamental data for studying on the historical urbanization processes, remains scarce in China. In this study, we proposed a Random Forest model to generate an annual 1-km resolution building volume dataset covering mainland China from 2001 to 2019, by integrating the nighttime light data, population demographics, electricity consumption records, carbon dioxide emissions data, and various optical and statistical datasets. This new building volume data are highly consistent with that derived from Baidu Maps on 1-km scale, with Pearson’s correlation coefficient (R) of 0.847, root mean square error (RMSE) of 9.17 × 105 m3/km2 and mean absolute error (MAE) of 5.86 × 105 m3/km2. Notably, cross-validation indicate that the blooming problem was greatly improved when compared with previous model-based building three-dimensional data. The proposed method holds significant advantages, benefiting form low-cost implementation based on free open-source data and providing extendable algorithm to estimate the 3D shape of cities in the future. The time-series historical building volume data offer comprehensive insights into the historical development of urban structures, and provide valuable fundmental data for future urban planning, urban climate models and land use projections.

Digitizing the land surface temperature (Ts) and surface soil moisture (mv) is essential for developing the intelligent Digital Earth. Here, we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving Ts and mv using the dual-polarized Tb of Aqua satellite advanced microwave scanning radiometer (AMSR-E) C-band (6.9 GHz) based on the simplified radiative transfer equation. Validation using in situ Ts and mv in southern China showed the average root mean square errors (RMSE) of Ts and mv retrievals reach 2.42 K (R2 = 0.61, n = 351) and 0.025 g cm−3 (R2 = 0.68, n = 663), respectively. The results were also validated using global in situ Ts (n = 2362) and mv (n = 1657) of International Soil Moisture Network. The corresponding RMSE are 3.44 k (R2 = 0.86) and 0.039 g cm−3 (R2 = 0.83), respectively. The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer Ts (R2 = 0.57; RMSE = 2.91 k) and ECV_SM mv (R2 = 0.51; RMSE = 0.045 g cm−3), respectively. Overall, this paper indicates an effective way to jointly modeling Ts and mv using passive microwave remote sensing.