The land surface temperature (LST) downscaling is a valuable technique for obtaining high-spatiotemporal resolution LST data. By incorporating object-level or pixel-level residuals, the spectral information of the downscaled LST can be better restored, leading to improve the prediction accuracy. However, pixel-level residuals may introduce uncertainty in the residual results, whereas object-level residuals may overlook detailed land cover change information within objects, potentially resulting in discontinuities and abrupt changes in the predicted results. To address these challenges, this study proposed an integrated object- and pixel-based residual compensation framework (IOPRCF) to effectively restore the land cover change information within fine-resolution LST, thereby generating more accurate high-temporal and spatial resolution LST data. The IOPRCF was rigorously tested in 12 geographical diverse regions in China using three methods: thermal sharpening algorithm (TsHARP), random forest (RF), and simple and effective downscaling (SED). These tests encompass multiple time intervals and scaling scales to comprehensively validate the effectiveness and applicability of IOPRCF in the field of LST downscaling. The results demonstrate that, when compared with the original benchmark algorithms, TsHARP, RF, and SED with IOPRCF achieved average increases in$R^{2}$of 0.035, 0.035, and 0.028, respectively, average decreases in root mean square error (RMSE) of 0.110, 0.120, and 0.133 K, respectively, and average increases in SSIM of 0.017, 0.014, and 0.016, respectively. Moreover, the downscaled LST data produced by these algorithms with IOPRCF exhibited more similar spatial structures and captured more detailed information. Furthermore, the proposed IOPRCF demonstrated strong robustness and transferability across different LST downscaling algorithms and segmentation models. Finally, IOPRCF is expected to enhance the capabilities of LST downscaling algorithms for recovering land cover information, effectively supporting the generation of high-quality, high-spatiotemporal resolution LST globally.

Accurately estimating sensible heat (H) and latent heat (LE) in mountainous areas is a significant challenge due to the influence of complex topography factors. Currently, most models have been developed to estimate surface heat flux for flat surfaces without considering the effect of complex geometric terrain structures. In this study, the Surface Energy Balance Algorithm for Land coupled with a mountainous net surface radiation (Rn) calculation method (MSEBAL) was proposed to accurately estimate H and LE in the upstream catchment regions of the Heihe River Basin (HRB). The Rnwas estimated by correcting the solar incoming radiation components using topographic factors, including slope, aspect, sky view factor (SVF), and terrain configuration factor (TCF). The SEBAL and MSEBAL models were applied to satellite remote sensing data from Landsat 8 images and ground-observed datasets. In situ measurements from the eddy covariance (EC) system of the A’rou superstation were used to validate the estimation accuracy of Rn, LE, and H by MSEBAL. The results show that Rn, LE, and H estimated by the MSEBAL exhibit good consistency with the validation of in situ measurements. The Rnestimated by MSEBAL showed a decrease in RMSE from 164.32 to 51.31 W/m2and a reduction in absolute bias from 154.50 to 10.50 W/m2compared to SEBAL. The H and LE estimated by MSEBAL exhibit low RMSE and bias, with values of 31.96 and -17.93 W/m2for H, and 35.67 and -6.18 W/m2 for LE, respectively, compared to SEBAL. The spatial pattern of surface heat fluxes exhibited variations with complex terrain changes. H and LE were found to be higher at mountain peaks, while lower values were observed in valleys. Additionally, H and LE were greater on east and south-facing slopes that receive more solar radiation compared to west and north-facing slopes. This study provides an effective tool for estimating surface heat fluxes over mountainous regions.

Accurate spatial quantification of cropland soil organic carbon (SOC) in plateau lake basins is crucial for assessing the carbon sequestration potential in ecologically fragile regions. This study developed a machine learning (ML) framework that integrates multi-source satellite-derived environmental covariates (topography, climate, vegetation, soil properties, and parent materials) to estimate SOC distribution in the Erhai Lake basin. Using 432 topsoil samples (0–20 cm), we systematically compared 15 models, including conventional ML approaches (e.g., random forest, support vector machine, and light gradient boosting machine) and deep learning (DL) models (e.g., long short-term memory, recurrent neural network, and multilayer perceptron). The results showed that DL models achieved higher predictive accuracy than conventional ML models, reducing RMSE by 0.1680 g kg⁻¹ and increasing R², RPIQ, and CCC by averages of 0.0225, 0.1143, and 0.0253, respectively, although conventional ML models exhibited greater robustness. Elevation and temperature were identified as dominant factors controlling SOC spatial patterns, with higher concentrations clustered in the western and northern subbasins. Greater prediction uncertainty in the northwestern and eastern margins was associated with complex terrain heterogeneity. Spatially explicit SOC mapping derived from the integration of multi-source satellite data and ML models offers innovative approaches for carbon management in ecologically fragile lacustrine agroecosystems.

Land surface temperature (LST) is a key factor in monitoring and improving thermal environments. However, conventional LST retrieval algorithms have not sufficiently accounted for the cavity and adjacency effects caused by the three-dimensional (3D) structures in urban settings. In this study, we propose an urban multiple scattering radiative transfer model (UMS-RTM). Based on this model, we develop an urban nonlinear split-window (UNSW) algorithm to retrieve urban land surface temperature (ULST) from Landsat-9 satellite data. The UMS-RTM optimizes the thermal radiation transfer process by correcting the cavity and adjacency effects. Analysis shows that land surface emissivity (LSE) and sky view factor (SVF) are the primary factors influencing these effects. The cavity effect increases the effective LSE by 0.01 to 0.08, while the adjacency effect raises the ground-leaving brightness temperature (BT) by 0.82 K to 3.51 K. The UNSW algorithm’s coefficients were calibrated across various LST, water vapor content (WVC), and SVF groupings to eliminate atmospheric effects and correct for cavity and adjacency effects. Sensitivity analyses of instrument noise, WVC, effective LSE, and SVF uncertainties demonstrated the reliability of the UNSW algorithm. Validation using simulated data showed that the ULST retrieved by the UNSW algorithm had a root-mean-square error (RMSE) of 0.35 K. When applied to Landsat-9 satellite data, the UNSW algorithm revealed that conventional algorithms and LST products overestimate ULST by 0 K to 2 K, with overestimations exceeding 1 K in areas with low SVF. The UNSW algorithm provides more accurate ULST retrieval and finer spatial distribution details.

The wide range acquisition of all-weather land surface temperatures (LSTs) from passive microwave (PMW) remotely sensed data contributes to understanding the land-atmosphere interactions, surface energy balance, and the global water cycle. Although significant progress has been made in PMW-based LST retrieval using statistical models, physical models, and machine learning methods, there remains a need to propose a model with high accuracy alongside strong physical interpretability and good generalization ability. This article aims to develop a physics-constrained deep learning (DL) hybrid model to obtain accurate LSTs under nonprecipitation clouds and then compare it with the pure physical and DL models. The hybrid model is developed by incorporating the physical loss function into the convolutional neural network, inheriting the advantages of the physical and DL models. Results show that the constructed model achieved good performance with a root mean square error (RMSE) of 1.60 K and a mean absolute error (MAE) of 1.26 K in the simulated data. Sensitivity analysis revealed that the hybrid model is less sensitive to input parameters than the pure physical and pure DL models and exhibits robustness across varying land surface and atmospheric conditions. Furthermore, during the evaluation using U.S. Surface Radiation Budget (SURFRAD) site data, the hybrid model yielded RMSEs of 4.37 and 3.48 K for day and night, respectively, with Advanced Microwave Scanning Radiometer 2 (AMSR2) and ERA5 data from 2012 to 2024, while outperforming the other two models at each SURFRAD site. The spatiotemporal applicability of the hybrid model further highlighted its superior generalization ability in mapping LSTs. We believe that the evident strength of the developed model over the traditional pure physical and DL models is attributed to its good accuracy, robustness to uncertainties in input parameters, and physical interpretability, which will benefit the other parameter estimates.

Nighttime light remote sensing provides an intuitive and timely method for surveying the dynamic socio-economic development of a region. In this study, we utilized the LJ-1 01 remote sensing image, which boasts high spatial resolution, to investigate its potential in estimating socioeconomic development at the township level in Yunnan Province, China, during the year 2018. Subsequently, Zipf's law was applied to assess the balance and distribution of socio-economic indicators in Yunnan Province. The results reveal that the R2value for socio-economic estimation at the township-level using the LJ-1 01 remote sensing image reaches 0.762. Yunnan Province demonstrates a relatively imbalanced development at the township-level, characterized by a notably high Zipf's value. This research contributes to an enhanced understanding of regional socioeconomic development and aids in the identification of measures to narrow regional disparities for future growth.

This study presents a framework for predicting the total carbon storage of wetlands based on machine learning models that integrate Sentinel-1 (S1), Sentinel-2 (S2), the Digital Elevation Model (DEM), and climatic data. The results indicated that the Random Forest (RF) model outperformed the Support Vector Machine (SVM) and Extreme Gradient Boosting (XGB) models in prediction accuracy. With an R-squared value of 0.67 for aboveground biomass carbon density, 0.75 for belowground biomass carbon density, and 0.65 for soil organic carbon density. The predictive performance utilizing multi-source data is significantly superior to that of single indicators, and the inclusion of climate data enhances the model’s predictive capabilities. The total carbon storage of wetlands in the Yunnan-Kweichow Plateau is estimated at 55.5 MtC, comprising 13.8 MtC in aboveground biomass carbon, 7.6 MtC in belowground biomass carbon, and 34.1 MtC in soil organic carbon.

Local incidence angle (LIA) normalization is an important method to improve the accuracy of active microwave remote sensing-based soil moisture retrieval in mountainous areas. In this study, the differences between two commonly used synthetic aperture radar (SAR) backscatter LIA normalization methods, cosine-based and liner-based, were compared using simulated and Sentinel-1 SAR data. The influence of two backscatter normalization methods on soil moisture retrieval in the dual-temporal dual-channel (DTDC) algorithm is analyzed. The results show that the difference between the normalized backscatter by the two methods is less than 0.3 dB in most cases. Despite the simplicity of the methods, both angle normalization techniques can rectify variations in backscattering induced by the LIA effect and improve the accuracy of soil moisture retrieval.

Drought risk assessment is one of the most important basic research topics on the quantitative understanding of the mechanism of drought risk and scientifically reducing the adverse effects of drought, which is of great significance in the theory and practice of developing coping strategies and drought management plans. In this paper, the drought risk on a global scale was quantified according to the hazard, exposure, and vulnerability of drought from 2020 to 2099. In addition, the trends of drought risk variation under two different representative concentration pathways (RCP45 and RCP85) scenarios are analyzed and compared. According to the variation character of drought risk in different scenarios, it is divided into 7 types, and the specific differences of each type are discussed. The results show that (1) the areas with high drought risk are primarily concentrated in populated and high precipitation variability places, such as Pakistan, western India, and central North America. (2) When the greenhouse gas concentration rises from RCP45 to RCP85, the drought risk in about 36.88% of the world will worsen, which is primarily concentrated in southern North America, southeastern South America, southern Africa, southern Oceania, southern Asia, and western Europe.

Due to the spatial heterogeneity within a relatively small distance of urban areas, it is necessary to consider the complex land cover types and three-dimensional geometric structure of urban surface. This study introduces the sky view factor (SVF) to calculate the equivalent emissivity of urban surface. In addition, the thermal radiation of adjacent pixels to target pixels is also considered to establish the urban radiative transfer model. The Landsat-8 collection-2 level-2 science product was taken to validate the proposed urban radiative transfer model. The area within the Fourth Ring Road of Beijing was regarded as the study area, then the land surface temperature retrieval algorithm was applied to estimate urban surface temperature (UST). The results of the UST retrieval algorithm were evaluated by comparing brightness temperature (BT) at the top of atmosphere (TOA) simulated by the Discrete Anisotropic Radiative Transfer (DART) model. The root mean squared error (RMSE) between brightness temperatures estimated by the urban radiative transfer model and those simulated by DART model was less than 0.21 K.

In this letter, we propose a dual-temporal dual-channel (DTDC) algorithm for soil moisture retrieval by using time-series observations from the Sentinel-1 C-band synthetic aperture radar. This algorithm utilizes the ancillary information of vegetation water content derived from optical images and assumes no variation on the surface roughness during the two consecutive radar measurements. Therefore, with the DTDC backscatter observations, four equations could be established using forward models, while three unknowns (the two consecutive soil moisture values and one roughness parameter) could be solved simultaneously by minimizing a cost function. The algorithm was tested with a series of Sentinel-1 dual-channel (VV + VH) data over croplands (sugarcane and cassava) of Northeast Thailand with an upscaling resolution of 1 km. Results show that the proposed algorithm could well capture the temporal change of soil moisture with root-mean-square errors within 0.06 m3/m3when ignoring days with precipitation, and could achieve a similar spatial pattern of soil moisture as detected from the Soil Moisture Active Passive mission, indicating the Sentinel-1 might be a proper tool for agricultural water management.

Accurate spatialized daily precipitation data plays an important role in meteorology, hydrology and ecology. Tropical Rainfall Measuring Mission (TRMM) precipitation data has been widely used in recent years for the relatively high resolution and large spatial coverage. Among them, two TRMM precipitation products are most commonly used: 3-hour scale (4B42) and monthly scale (3B43). The 3B42 product with a high temporal resolution but low accuracy, while the 3B43 product is the opposite. For hydrological modeling and water resource analysis, the acquisition of daily precipitation data is very important. In most cases, daily precipitation data is obtained by accumulating 3B42 product directly. However, this method ignores the change of precipitation rate. In the case of heavy rainfall, the daily precipitation data from 3B42 data shows a large deviation compared with the daily rainfall observed from rain gauges. Based on the analysis of ground measured daily precipitation and soil moisture data, this paper proposes a temporal disaggregation algorithm of TRMM monthly precipitation products using AMSR2 daily soil moisture data. The results show that this method is simple and feasible, which provide a new reference for the study of temporal downscaling of satellite-based rainfall dataset.

In general, the drought index is usually used for drought monitoring. It is necessary to distinguish different climates in large-scale drought studies because of different climates respond differently to drought. Based on ERA5 reanalysis datasets and the world Map of Koppen-Geiger Climate Classification, this paper evaluates the spatial and temporal distribution of drought under different climate areas along the Belt and Road (B&R) during 2000-2017 from four aspects: precipitation, runoff, evaporation and soil moisture. Results are as follows: except for parts of North Africa and West Asia, the annual variation of precipitation in the other places are not significant, but the runoff is the opposite. However, the amount of evaporation increased significantly in 2017, which may be caused by global warming or El Niño. Overall, the frequency of droughts may not increase in the near future, but if they do, they may occur faster and more dramatically.

Bus rapid transit (BRT) has been proved to be an effective tool to improve mass transit services. However, BRT's adaptive operations like management and scheduling under different scenarios are too complicated to implement using traditional methods. The ACP approach, which is based on holism and complex system theory and consists of artificial systems (A), computational experiments (C) and parallel execution (P), offers an efficient new method to cope with these complex systems, including BRT. In this paper, the parallel transportation management and control system for BRT (PTMS-BRT) is presented, which is designed and implemented using the ACP approach. PTMS-BRT integrates such functions as BRT's monitoring, warning, forecasting, incident management, and real-time scheduling, to provide its operations smoother, safer, more efficient, and reliable. It has been piloted successfully in Guangzhou BRT to demonstrate it as another successful example of parallel transportation systems.

The subway's daily management and control are too complicated to be handled by using traditional methods. Based on the artificial systems, computational experiments, and parallel execution (ACP) approach, the Parallel Transportation Management and Control System for Subways (PTMS -Subway) is proposed. First, the dynamic status perception and management platform for subways (SPMP-Subway) is constructed, and artificial subway systems (ASS) are designed and constructed, and then they are validated by the real-time data from SPMP-Subway. Then, the design content and construction process of computational experiments platform are performed. Finally, through the interactions of parallel execution system between actual subway and its ASS, a set of practical management and control algorithms can be validated and improved. PTMS-Subway can implement those advanced functions, such as real-time monitoring, warning, forecasting, scheduling optimization, incidence management, and so on, to improve its reliability, efficiency, safety, and service level. SPMP-Subway and PTMS-Subway have been piloted in Subway Lines 1 and 2 in Suzhou, China, and achieved the expected results and benefits successfully.

Field data are important for convenient daily travel of urban residents, reducing traffic congestion and accidents, pursuing a low-carbon environment-friendly sustainable development strategy, and meeting the extra peak traffic demand of large sporting events or large business activities, etc. To meet the field data demand during the 2010 Asian (Para) Games held in Guangzhou, China, based on the novel Artificial systems, Computational experiments, and Parallel execution (ACP) approach, the Parallel Traffic Management System (PtMS) was developed. It successfully helps to achieve smoothness, safety, efficiency, and reliability of public transport management during the two games, supports public traffic management and decision making, and helps enhance the public traffic management level from experience-based policy formulation and manual implementation to scientific computing-based policy formulation and implementation. The PtMS represents another new milestone in solving the management difficulty of real-world complex systems.