Timely and effective estimation and monitoring of soil moisture (SM) provides not only an understanding of regional SM status for agricultural management or potential drought but also a basis for characterizing water and energy exchange. The apparent thermal inertia (ATI) and Temperature Vegetation Dryness Index (TVDI) are two widely used indices to reflect SM from remote sensing data. While the ATI-based model is routinely used to estimate the SM of bare soil and sparsely vegetated areas, the TVDI-based model is more suitable for areas with dense vegetation coverage. In this study, we present an iteration procedure that allows us to identify optimal Normalized Difference Vegetation Index (NDVI) thresholds for subregions and estimate their relative soil moisture (RSM) using three models (the ATI-based model, the TVDI-based model, and the ATI/TVDI joint model) from 1 January to 31 December 2017, in the Chinese Loess Plateau. The initial NDVI (NDVI₀) was first introduced to obtain TVDI value and two other thresholds of NDVI_ATI and NDVI_TVDI were designed for dividing the whole area into three subregions (the ATI subregion, the TVDI subregion, and the ATI/TVDI subregion). The NDVI values corresponding to maximum R-values (correlation coefficient) between estimated RSM and in situ RSM measurements were chosen as optimal NDVI thresholds after performing as high as 48,620 iterations with 10 rounds of 10-fold cross-calibration and validation for each period. An RSM map of the whole study area was produced by merging the RSM of each of the three subregions. The spatiotemporal and comparative analysis further indicated that the ATI/TVDI joint model has higher applicability (accounting for 36/38 periods) and accuracy than the ATI-based and TVDI-based models. The highest average R-value between the estimated RSM and in situ RSM measurements was 0.73 ± 0.011 (RMSE—root mean square error, 3.43 ± 0.071% and MAE—mean absolute error, 0.05 ± 0.025) on the 137th day of 2017 (DOY—day of the year, 137). Although there is potential for improved mapping of RSM for the entire Chinese Loess Plateau, the iteration procedure of identifying optimal thresholds determination offers a promising method for achieving finer-resolution and robust RSM estimation in large heterogeneous areas.

Urbanization is a key determinant of fine particulate matter (PM_2.5) pollution variability. However, there is a limited understanding of different urbanization factors’ roles in PM_2.5 pollution. Using satellite-derived PM_2.5 data from 2002 to 2017, we investigated the spatiotemporal evolution and the spatial autocorrelation of PM_2.5 pollution in the Yangtze River Delta (YRD) region. Afterwards, the impacts of three urbanization factors (population urbanization, land urbanization and economic urbanization) on PM_2.5 pollution were estimated by a spatial Durbin panel data model (SDM). Obtained results showed that: (i) PM_2.5 pollution was larger in the north than in the south of YRD; (ii) Lianyungang and Yancheng cities had significant increasing trends in PM_2.5 pollution from 2002 to 2017; (iii) the regional median center of PM_2.5 pollution was observed in the Nanjing city, with gradual shifting to the northwest during the 16-year period; (iv) PM_2.5 pollution showed significant and positive spatial autocorrelation and spillover effect; (v) population urbanization contributed more to the increase in PM_2.5 pollution than land urbanization, while economic urbanization had no significant impact. The present study highlights the impacts of three urbanization factors on PM_2.5 pollution which represent valuable and relevant information for air pollution control and urban planning.

The land ecosystem provides essential natural resources for the survival and development of human beings. Therefore, land ecological security (LES) acts as a vital part of the sustainable development of human society and economy. This study included a dynamic analysis of land use change in Chaohu Lake Basin (CLB) in China from 1998 to 2018, evaluating the spatiotemporal patterns of LES at both the administrative district scale and grid scale (200 m × 200 m). Then, geographic detector was applied to analyze the influence of the assessment index on LES. The results show that in the 2008–2018 period, land use changed more significantly compared to the 1998–2008 period. The continuous extension of urban land led to a decrease in the areas of other land use types. In the CLB (administrative district scale), the LES levels varied throughout the study period. In Changfeng, Feixi, and the other three regions, the LES has been significantly improved. However, the LES in six other regions showed different degrees of decline, particularly in Hexian and Urban Hefei. Simultaneously, the LES showed a gradual improvement at a 200 m × 200 m grid scale level. The influence of anthropogenic factors on the LES was stronger than natural factors. Findings from this study provide reliable guidance for improving the ecosystem environment in ecologically fragile areas.

Carbon emissions are critical to climate change, and land-use change is an essential source of growth in carbon emissions. Research on land-use carbon emissions has become one of the hotspots in academic research. To explore the research hotspots and development trends of land-use carbon emissions in the last 20 years, CiteSpace software was used to conduct a quantitative analysis of relevant literature. This paper was based on the China National Knowledge Infrastructure (CNKI) and Web of Science (WoS) database literature on land-use carbon emissions from 2001 to 2020. The scientific research cooperation network CiteSpace software, with keyword co-occurrence, clustering, and burst word detection, was used to systematically analyze the main research strengths, hotspots and frontiers and clarify the research progress. The research results are as follows: (1) the amount of literature and the depth of research on land-use carbon emissions have increased yearly. However, there is little cooperation between research institutions and scholars, and there is still a lack of large-scale and stable research teams. (2) At the research hotspot level, the English literature focuses on building models and theoretical frameworks to study the internal mechanisms and driving factors of carbon emissions and climate change. The Chinese literature focuses on achieving regional carbon emissions reductions and carbon cycle goals and optimizing a low-carbon economy, transportation and land-use structure. (3) Research frontiers and trends show that the English literature first explored carbon sequestration, organic carbon, and carbon accounting. In China, the research frontiers are gradually becoming focused on influencing factors, decoupling analysis, and the built environment. The study will strengthen the intensity and depth of global carbon emission research and provide a reference for improving global climate change, protecting ecology and balancing economic development.

This study aims to integrate multisource data to model the relative soil moisture (RSM) over the Chinese Loess Plateau in 2017 by stepwise multilinear regression (SMLR) in order to improve the spatial coverage of our previously published RSM. First, 34 candidate variables (12 quantitative and 22 dummy variables) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and topographic, soil properties, and meteorological data were preprocessed. Then, SMLR was applied to variables without multicollinearity to select statistically significant (p-value < 0.05) variables. After the accuracy assessment, monthly, seasonal, and annual spatial patterns of RSM were mapped at 500 m resolution and evaluated. The results indicate that there was a high potential of SMLR to model RSM with the desired accuracy (best fit of the model with Pearson’s r = 0.969, root mean square error = 0.761%, and mean absolute error = 0.576%) over the Chinese Loess Plateau. The variables of elevation (0–500 m and 2000–2500 m), precipitation, soil texture of loam, and nighttime land surface temperature can continuously be used in the regression models for all seasons. Including dummy variables improved the model fit both in calibration and validation. Moreover, the SMLR-modeled RSM achieved better spatial coverage than that of the reference RSM for almost all periods. This is a significant finding as the SMLR method supports the use of multisource data to complement and/or replace coarse resolution satellite imagery in the estimation of RSM.

Land-use change and regional carbon emissions are closely related. In-depth research on the mechanism of land-use carbon emissions is conducive for clarifying the direction and focus of future low-carbon construction work. Carbon emissions calculation models were used to calculate total carbon emissions in Guizhou Province and reveal their spatiotemporal distribution characteristics. The results were as follows. (1) The land-use structure of Guizhou Province underwent a significant change from 2009 to 2019, in particular with regard to cultivated and construction land. (2) Land-use carbon emissions in Guizhou Province were increasing annually, and this was related to urbanization, population density, and energy consumption. (3) There were spatial differences in the regional distribution of carbon emissions, their intensity, and carbon emissions per capita in Guizhou Province. Additionally, there were spatial clusters of cities with high or low emissions intensity. Therefore, there is scope for constructive proposals concerning sustainable land use and development, such as controlling the total amount and intensity of construction land, optimizing the structure of land use, managing the rational distribution of the population, constantly advancing the optimization of industrial structure, and improving the relevant policies.