Accurate information about the spatiotemporal patterns of rice paddies is essential for the assessment of food security, management of agricultural resources, and sustainability of ecosystems. However, accurate spatial datasets of rice paddy fields and multi-cropping at fine resolution are still lacking. Landsat observation is the primary source of remote sensing data that has continuously mapped regional rice paddy fields at a 30-m spatial resolution since the 1980s. However, Landsat data used for rice paddy studies reveals some challenges, especially data quality issues (e.g., cloud cover). Here, we present an algorithm that integrates time-series Landsat and MODIS (Moderate-resolution Imaging Spectroradiometer) images with a phenology-based approach (ILMP) to map rice paddy planting fields and multi-cropping patterns. First, a fusion of MODIS and Landsat data was used to reduce the cloud contamination, which added more information to the Landsat time series data. Second, the unique biophysical features of rice paddies during the flooding and open-canopy periods (which can be captured by the dynamics of the vegetation indices) were used to identify rice paddy regions as well as those of multi-cropping. This algorithm was tested for 2015 in Nanchang County, which is located on the Poyang Lake plain in southern China. We evaluated the resultant map of the rice paddy and multi-cropping systems using ground-truth data and Google Earth images. The overall accuracy and kappa coefficient of the rice paddy planting areas were 93.66% and 0.85, respectively. The overall accuracy and kappa coefficient of the multi-cropping regions were 92.95% and 0.89, respectively. In addition, our algorithm was more capable of capturing detailed information about areas with fragmented cropland than that of the National Land Cover Dataset (NLCD) from 2015. These results demonstrated the great potential of our algorithm for mapping rice paddy fields and using the multi-cropping index in complex landscapes in southern China.

Trace metals in street dust originate from many natural and anthropogenic sources and may directly or indirectly affect daily life and health. Here, the enrichment factor (EF), an efficient parameter to assess the contamination by trace metals, was evaluated and compared in four different zones of the urban area of Nanchang city during different seasons. Results showed that EFs of trace metals have regional seasonal variations. The EF range of Cr, Ni, Cu, Zn, As, Cd and Pb were found to be (0.76-16.38), (0.66-9.06), (1.09-169.58), (1.52-36.16), (0.44-7.17), (2.07-78.38), (0.95-20.94), respectively. These values indicate minimal to significant pollution. The EF values of Zn, Cd and Pb in street dust show very similar variations as their median and mean values of EF are higher in certain zones, and their seasonal variations are similar. The Ni and As results suggest that anthropogenic influences for these elements are not significant. The Ni may be influenced by land use type rather than point sources and As may be attributed to natural sources. The Principal Component Absolute Scores - Multivariate Linear Regression (PCAS-MLR) model was used to identify the types and contributions of trace metal sources. The main sources of trace metals in dust were found to be industrial discharges and traffic emissions. However, the percentages of their contributions have significant regional differences. The relationship between the EF of trace metals and the ecological risk index (ERI) was also investigated, and indicates that seriously polluted areas correspond to high ERI regions. The land use types and characteristics of the source jointly affects the relationship between EFs and ERI.

The Qinghai-Tibetan Plateau (QTP) is a global center of cold and dry, where the most extensive fragile alpine vegetation exists. Quantitative analysis of drought event characteristics and vegetation response to drought on the QTP is indispensable for understanding the increasing drought events in a warming climate which exacerbate adverse influence on extremely alpine ecosystems. Here, using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) from 2000 to 2018, this study analyzed the characteristics of drought events, their temporal impacts, and the stability of vegetation response to drought on the QTP. Results showed that: the characteristics of drought events on the QTP have clear spatial heterogeneity. When compared to the east monsoon region, most of the western regions have higher frequency and lower intensity of drought events. Drought has significant temporal effects on vegetation in grassland areas of the QTP during the growing season, which reach their peak in July and August. The 0–1-month and 3-month time scales were the optimal lagged and accumulated time during the growing season, respectively. The stability of vegetation response to drought showed significant spatial heterogeneity and varied with eco-geographical regions and vegetation types. Generally, forest areas showed high resistance (74.09) and resilience (2.26), followed by crop and grassland areas.

Accurately tracking the changes in rice cropping intensity is a critical requirement for policymakers to formulate reasonable land-use policies. Southern China is a traditional region for rice multi-cropping, yet less is known about its spatial–temporal changes under the background of rapid urbanization in recent decades. Based on images from Landsat and MODIS and multiple land cover products, the gap-filling and Savitzky–Golay filter method (GF-SG), the enhanced pixel-based phenological features composite approach (Eppf-CM), random forest (RF), and the difference in NDVI approach (DNDVI) were combined to map the rice cropping pattern with a spatial resolution of 30 × 30 m over Southern China in 2000 and 2020 through Google Earth Engine (GEE). Subsequently, the spatial–temporal changes in rice cropping intensity and their driving factors were examined by Getis-Ord Gi* and geographical detector. The results showed that the produced rice cropping pattern maps exhibited high accuracy, with kappa coefficients and overall accuracies exceeding 0.81 and 90%, respectively. Over the past two decades, the planting areas of double-season rice in Southern China decreased by 54.49%, and a reduction was observed across eight provinces, while only half of the provinces exhibited an increase in the planting areas of single-season rice. Compared to the year 2000, the planting area of the conversion from double- to single-season rice cropping systems in 2020 was 2.71 times larger than that of the conversion from single- to double-season rice cropping systems. The hotspots of the change in rice cropping intensity were mainly located in the central part of Southern China (excluding the Poyang Lake Plain). The decline in the rural labor force, coupled with ≥10 °C accumulated temperature and topographical factors, plays a crucial role in the decreased intensity of rice cropping. Our findings can be beneficial for realizing regional agricultural sustainability and food security.

Advanced developments have been achieved in urban human population estimation, however, there is still a considerable research gap for the mapping of remote rural populations. In this study, based on demographic data at the town-level, multi-temporal high-resolution remote sensing data, and local population-sensitive point-of-interest (POI) data, we tailored a random forest-based dasymetric approach to map population distribution on the Qinghai–Tibet Plateau (QTP) for 2000, 2010, and 2016 with a spatial resolution of 1000 m. We then analyzed the temporal and spatial change of this distribution. The results showed that the QTP has a sparse population distribution overall; in large areas of the northern QTP, the population density is zero, accounting for about 14% of the total area of the QTP. About half of the QTP showed a rapid increase in population density between 2000 and 2016, mainly located in the eastern and southern parts of Qinghai Province and the central-eastern parts of the Tibet Autonomous Region. Regarding the relative importance of variables in explaining population density, the variables “Distance to Temples” is the most important, followed by “Density of Villages” and “Elevation”. Furthermore, our new products exhibited higher accuracy compared with five recently released gridded population density datasets, namely WorldPop, Gridded Population of the World version 4, and three national gridded population datasets for China. Both the root-mean-square error (RMSE) and mean absolute error (MAE) for our products were about half of those of the compared products except for WorldPop. This study provides a reference for using fine-scale demographic count and local population-sensitive POIs to model changing population distribution in remote rural areas.