The distribution of the population is an essential aspect of addressing social, economic, and environmental problems. Gridded population data can provide more detailed information than census data, and multisource data from remote sensing and geographic information systems have been widely used for population estimation studies. However, due to spatial heterogeneity, the population has different distribution characteristics and variation patterns at different scales, while the relationships between multiple variables also vary with scale. This article presents a stepwise downscaling approach in that the random forest regression kriging technique is used to downscale census data to multi-resolution gridded population datasets. Using Nanjing, China, as the experimental case, population distribution maps were generated at 100 m, 500 m, and 1 km spatial resolution, and compared with the other three downscaling methods and three population products. The results demonstrated the produced gridded population maps by the proposed approach have higher accuracy and more accurate details of population distribution with the smallest mean absolute error (MAE) and root mean squared error (RMSE) values of 1.590 and 2.189 ten thousand people (over 40% reduction). The artificial land and road data are the two most important indicators of population distribution for the regional random forest modeling in Nanjing. Our proposed method can be a valuable tool for population mapping and has the potential to monitor sustainable development goals.

Accurate spatial distribution of gridded gross domestic product (GDP) data is crucial for revealing regional disparities within administrative units, thus facilitating a deeper understanding of regional economic dynamics, industrial distribution, and urbanization trends. The existing GDP spatial models often rely on prediction residuals for model evaluation or utilize residual distribution to improve the final accuracy, frequently overlooking the modifiable areal unit problem within residual distribution. This paper introduces a hybrid downscaling model that combines random forest and area-to-area kriging to map gridded GDP. Employing Thailand as a case study, GDP distribution maps were generated at a 1 km spatial resolution for the year 2015 and compared with five alternative downscaling methods and an existing GDP product. The results demonstrate that the proposed approach yields higher accuracy and greater precision in detailing GDP distribution, as evidenced by the smallest mean absolute error and root mean squared error values, which stand at USD 256.458 and 699.348 ten million, respectively. Among the four different sets of auxiliary variables considered, one consistently exhibited a higher prediction accuracy. This particular set of auxiliary variables integrated classification-based variables, illustrating the advantages of incorporating such integrated variables into modeling while accounting for classification characteristics.

Satellite-based solar-induced chlorophyll fluorescence (SIF) has emerged as a valuable tool for monitoring the photosynthetic activity of vegetation at both regional and global scales. Downscaling techniques offer the opportunity to utilize coarse-spatial-resolution SIF products for investigating carbon cycles and ecological processes at finer resolutions. However, the lack of pre-2000 downscaled products and the limited utilization of residual information in existing models pose challenges in fully harnessing the potential of SIF downscaling. In this study, we generated a new monthly SIF product, DSIF _RFK* _EA0.05, at a resolution of 0.05° in East Asia from July 1995 to June 2003. Random forest kriging (RFK) was employed, incorporating GOME SIF, AVHRR data, ERA5 climate data, and using the optimal explanatory variables. Compared with other downscaled results, the produced DSIF _RFK* _EA0.05 has higher accuracy and more accurate details of SIF distribution with the highest mean R ² value of 0.83 and smallest root mean squared error (RMSE) value of 0.08 mW/m ² /nm/sr. The DSIF _RFK* _EA0.05 estimates showed a strong correlation with gross primary productivity (GPP) data from eight flux sites (R ² = 0.73), as well as high correlation coefficient values of 0.73, 0.88, and 0.89 with three other fine-resolution products. This study addresses the gap in statistical downscaling of SIF before 2000 and demonstrates the feasibility of utilizing AVHRR data for fine-resolution SIF prediction. Our developed product offers improved detail compared to the original GOME SIF, thereby enhancing the application of satellite SIF for understanding early carbon cycles and ecological processes at a finer resolution.

Surface soil moisture (SSM) is crucial for understanding terrestrial hydrological processes. Despite its widespread use since 2015, the Soil Moisture Active and Passive (SMAP) SSM dataset faces challenges due to its inherent low spatial resolution and data gaps. This study addresses these limitations through a deep learning approach aimed at interpolating missing values and downscaling soil moisture data. The result is a seamless, daily 1 km resolution SSM dataset for China, spanning from 1 January 2016 to 31 December 2022. For the original 9 km daily SMAP products, a convolutional neural network (CNN) with residual connections was employed to achieve the spatially seamless 9 km SSM data, integrating multi-source remote sensing data. Subsequently, auxiliary data including land cover, land surface temperatures, vegetation indices, vegetation temperature drought indices, elevation, and soil texture were integrated into the CNN-based downscaling model to generate the spatially seamless 1 km SSM. Comparative analysis of the spatially seamless 9 km and 1 km SSM datasets with ground observations yielded unbiased root mean square error values of 0.09 cm³/cm³ for both, demonstrating the effectiveness of the downscaling method. This approach provides a promising solution for generating high-resolution, spatially seamless soil moisture data to meet the needs of hydrological, meteorological, and agricultural applications.

The Yangtze River Economic Belt (YREB), spanning nine provinces and cities in eastern, central, and western China, is a key region for China’s urbanization. This study utilizes the Google Earth Engine (GEE) platform to integrate four land cover and impervious surface datasets, constructing built-up area datasets for the YREB at five-year intervals from 1985 to 2020. The employed random forest model achieved an overall accuracy (OA) and kappa coefficient both exceeding 90%, demonstrating high reliability and precision in the generated datasets. Using this dataset, we then calculated the United Nations Sustainable Development Goal 11.3.1 (SDG11.3.1) index for the YREB and its nine constituent provinces, which includes the land consumption rate (LCR), population growth rate (PGR), and ratio of land consumption rate to population growth rate (LCRPGR). The results show that the LCRPGR index for the entire region over the 35-year period is −0.006, 4.84, 0.44, 0.77, 5.15, 0.09, and 2.13, respectively. These values suggest that the land consumption rate significantly outpaced the population growth rate during 1990–1995, 2005–2010, and 2015–2020, reflecting periods of rapid urban development. This study offers important insights into urban expansion in the YREB, offering valuable data to inform sustainable urbanization practices.

Satellite-based surface soil moisture (SSM) products often contain spatial gaps and reduced reliability due to variations in vegetation cover and type, complex surface conditions such as heterogeneous topography and soil texture, or inherent limitations of satellite microwave sensors. This study presents a residual autoencoder model named TsSMNet, which combines multi-source remote sensing inputs with statistical features derived from SSM time series, including central tendency, dispersion and variability, extremes and distribution, temporal dynamics, magnitude and energy, and count-based features, to reconstruct gap-free SSM estimates. The model incorporates one-dimensional convolutional layers to efficiently capture local continuity patterns within the flattened SSM representations while reducing parameter complexity. TsSMNet was used to generate seamless 9 km SSM data over China from 2016 to 2022, based on the SMAP product, and was evaluated using in situ observations from six networks in the International Soil Moisture Network. The results show that TsSMNet outperforms AutoResNet, Transformer, Random Forest and XGBoost models, reducing the root mean square error (RMSE) by an average of 17.1 percent and achieving a mean RMSE of 0.09 cm³/cm³. Feature importance analysis highlights the strong contribution of temporal predictors to model accuracy. Compared to its variant without time-series features, TsSMNet provides better spatial representation, improved consistency with in situ temporal observations, and enhanced evaluation metrics. The reconstructed product offers improved spatial coverage and continuity relative to the original SMAP data, supporting broader applications in regional-scale hydrological analysis and large-scale climate, ecological, and agricultural studies.