Global navigation satellite system-reflectometry (GNSS-R) can retrieve Earth's surface parameters, such as soil moisture (SM) using the reflected signals from GNSS constellations with advantages of noncontact, all-weather, real-time, and continuity, particularly the space-borne cyclone GNSS (CYGNSS) mission. However, the accuracy and efficiency of SM estimation from CYGNSS still need to improve. In this article, the global SM is estimated using machine learning (ML) regression aided by a preclassification strategy. The total observations are classified by land types and corresponding subsets are built for constructing ML regression submodels. Ten-fold cross-validation technique is adopted. The overall performance of SM estimation with/without preclassification is compared, and the results show that the SM estimations using different ML algorithms all have substantial improvement with the preclassification strategy. Then, the optimal XGBoost predicted model with root-mean-square error (RMSE) of 0.052 cm ³ /cm ³ is adopted. In addition, the satisfactory daily and seasonal SM prediction outcomes with an overall correlation coefficient value of 0.86 and an RMSE value of 0.056 cm ³ /cm ³ are achieved at a global scale, respectively. Furthermore, the extensive temporal and spatial variations of CYGNSS SM predictions are evaluated. It shows that the reflectivity plays a main role among the predictors in SM estimation, and the next is vegetation. In some extremely dry places, the roughness may become more important. The value of SM is positively correlated with RMSE and also another limit condition that will constrain the variation of predictors, thus affecting correlation coefficient R and RMSE. Also, we compare both SMAP and CYGNSS SM predictions against in situ SM measurements from 301 stations. Similar low-median unbiased RMSEs are obtained, and the daily averaged CYGNSS-based SM against the in situ networks is 0.049 cm ³ /cm ³ . The presented approach succeeds in providing SM estimation at a global scale with employing the least ancillary data with superior results and this article reveals the spatio-temporal heterogeneity for SM estimation using CYGNSS data.

The Surface Soil Moisture (SSM) products derived from microwave remote sensing have a coarse spatial resolution, therefore downscaling is required to obtain accurate SSM at high spatial resolution. An effective way to handle the stratified heterogeneity is to model for various stratifications, however the number of samples is often limited under each stratification, influencing the downscaling accuracy. In this study, a machine learning-based geostatistical model, which combines various ancillary information at fine spatial scale, is developed for spatial downscaling. The proposed support vector area-to-area regression kriging (SVATARK) model incorporates support vector regression and area-to-area kriging by considering the nonlinear relationships among variables for various stratifications. SVATARK also considers the change of support problem in the downscaling interpolation process as well as for solving the small sample size in trend prediction. The SVATARK method is evaluated in the Naqu region on the Tibetan Plateau, China to downscale the European Space Agency's (ESA) 25-km-resolution SSM product. The 1-km-resolution SSM predictions have been produced every 8 days over a six-year period (2010-2015). Compared with other five downscaling methods, the downscaled predictions from the SVATARK method performs the best with in-situ observations, resulting in a 24.4 percent reduction in root mean square error with 0.08 m3.m-3 and a 8.2 percent increase in correlation coefficient with 0.72, on average. Additionally, anomalously low SSM values, an indicator of drought, had a record low anomaly in mid-July for 2015, as noted by previous studies, indicating that SVATARK could be utilized for drought monitoring.

Global Navigation System-Reflectometry (GNSS-R) is a microwave remote sensing technology that can estimate soil moisture (SM) by receiving Earth's surface reflected signals from GNSS satellites. The Cyclone GNSS (CYGNSS) constellation, with its high temporal resolution Earth observation data, is a commonly used data source for soil moisture. However, due to the continual movement of GNSS transmitters and GNSS-R satellites, observations of the Earth's surface can be chaotic and random when compared to the regular paths followed by other remote sensing satellites. Moreover, there are numerous gaps in the observations that are unevenly distributed. In this paper, a gap-filling method based on spatial autocorrelation is proposed to interpolate the gaps within these uneven observation datasets, with SM being estimated post-interpolation. The sample set for the model comprises points surrounding the interpolation target, with modeling conducted considering factors of spatial weighting to estimate values at the interpolation target. Different autocorrelation-based gap-filling methods using CYGNSS data can achieve good estimation accuracy, and the data coverage after interpolation is on average 1.8 times greater than before interpolation. The gap-filling method using XGBoost achieves the best performance and offers the highest accuracy in SM estimation, with an average correlation coefficient of 0.8445, and an average RMSE of 0.0457 m³/m³. The gap-filling approach can significantly enhance data coverage and facilitate the filling of daily gaps in CYGNSS data with all maintaining high SM estimation accuracy. The estimation of daily missing values using CYGNSS data can fully exploit the embedded surface features in the data's fine resolution and can provide high-resolution SM retrieval, as well as for the warning and monitoring of floods, droughts, and changes in water bodies.

High-spatial-resolution (HSR) images and high-temporal-resolution (HTR) images have their unique advantages and can be replenished by each other effectively. For land cover classification, a series of spatiotemporal fusion algorithms were developed to acquire a high-resolution land cover map. The fusion processes focused on the single level, especially the pixel level, could ignore the different phenology changes and land cover changes. Based on Bayesian decision theory, this paper proposes a novel decision-level fusion for multisensor data to classify the land cover. The proposed Bayesian fusion (PBF) combines the classification accuracy of results and the class allocation uncertainty of classifiers in the estimation of conditional probability, which consider the detailed spectral information as well as the various phenology information. To deal with the scale inconsistency problem at the decision level, an object layer and an area factor are employed for unifying the spatial resolution of distinct images, which would be applied for evaluating the classification uncertainty related to the conditional probability inference. The approach was verified on two cases to obtain the HSR land cover maps, in comparison with the implementation of two single-source classification methods and the benchmark fusion methods. Analyses and comparisons of the different classification results showed that PBF outperformed the best performance. The overall accuracy of PBF for two cases rose by an average of 27.8% compared with two single-source classifications, and an average of 13.6% compared with two fusion classifications. This analysis indicated the validity of the proposed method for a large area of complex surfaces, demonstrating the high potential for land cover classification.

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.

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.