Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km², with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.

Karst rocky desertification (KRD) has become the primary ecoenvironmental problem in the karst regions of southwest China. The rapid and efficient acquisition of exposed bedrock fractions (EBF) is crucial for the monitoring and assessment of KRD degree and distribution within the highly heterogeneous landscapes. Remote-sensing indices provide a useful method for the quick mapping of the EBF at large scales. The currently available rock indices, however, are faced with insensitivity to bedrock change characteristics, which greatly limits their performances and suitability. To address this problem, we proposed a novel karst bare-rock index (KBRI) that applies shortwave-infrared (SWIR) and near-infrared (NIR) bands from Landsat-8 OLI imagery to maximally distinguish between exposed bedrock and other land cover types in southwest China. A linear regression model was thus established between KBRI and the EBF derived from in situ measurements. The model developed here was then validated with an independent experiment and applied over a large geographic area to produce regional maps of EBF in southwest China. Experimental results showed good performance on root mean square error (5.59%), mean absolute error (4.63%), root mean absolute percentage error (13.59%), and coefficient of determination (0.72), respectively. The advantages of the proposed method are reflected in its simplicity and minimal requirements for auxiliary data while still achieving comparatively better accuracy than existing related indices. Thus, the KBRI has the great potential for the application in other regions around the world with the similar geological backgrounds, thereby helping to address the similar or other related environmental issues. Results of this study provide baseline data for the KRD assessment and karst-ecosystem management in southwest China.

Since the implementation of China’s afforestation and conservation projects during recent decades, an increasing number of studies have reported greening trends in the karst regions of southwest China using coarse-resolution satellite imagery, but small-scale changes in the heterogenous landscapes remain largely unknown. Focusing on two typical karst regions in the Nandong and Xiaojiang watersheds in Yunnan province, we processed 2,497 Landsat scenes from 1988 to 2016 using the Google Earth Engine cloud platform and analyzed vegetation trends and associated drivers. We found that both watersheds experienced significant increasing trends in annual fractional vegetation cover, at a rate of 0.0027 year⁻¹ and 0.0020 year⁻¹, respectively. Notably, the greening trends have been intensifying during the conservation period (2001–2016) even under unfavorable climate conditions. Human-induced ecological engineering was the primary factor for the increased greenness. Moreover, vegetation change responded differently to variations in topographic gradients and lithological types. Relatively more vegetation recovery was found in regions with moderate slopes and elevation, and pure limestone, limestone and dolomite interbedded layer as well as impure carbonate rocks than non-karst rocks. Partial correlation analysis of vegetation trends and temperature and precipitation trends suggested that climate change played a minor role in vegetation recovery. Our findings contribute to an improved understanding of the mechanisms behind vegetation changes in karst areas and may provide scientific supports for local afforestation and conservation policies.

Key Findings: Combining physical fractionation and pyrolysis–gas chromatography/mass spectrometry (py-GC/MS) technique can help better understand the dynamics of soil organic matter (SOM). Background and Objectives: SOM plays a critical role in the global carbon (C) cycle. However, its complexity remains a challenge in characterizing chemical molecular composition within SOM and under nitrogen (N) deposition. Materials and Methods: Three particulate organic matter (POM) fractions within SOM and under N treatments were studied from perspectives of distributions, C contents and chemical signatures in a subtropical forest. N addition experiment was conducted with two inorganic N forms (NH₄Cl and NaNO₃) applied at three rates of 0, 40, 120 kg N ha⁻¹ yr⁻¹. Three particle-size fractions (>250 μm, 53–250 μm and <53 μm) were separated by a wet-sieving method. Py-GC/MS technique was used to differentiate between chemical composition. Results: A progressive proportion transfer of mineral-associated organic matter (MAOM) to fine POM under N treatment was found. Only C content in fine POM was sensitive to N addition. Principal component analyses (PCA) showed that the coarse POM had the largest plant-derived markers (lignins, phenols, long-chain n-alkanes, and n-alkenes). Short-chain n-alkanes and n-alkenes, benzofurans, aromatics and polycyclic aromatic hydrocarbons mainly from black carbon prevailed in the fine POM. N compounds and polysaccharides from microbial products dominated in the MAOM. Factor analysis revealed that the degradation extent of three fractions was largely distinct. The difference in chemical structure among three particulate fractions within SOM was larger than treatments between control and N addition. In terms of N treatment impact, the MAOM fraction had fewer benzofurans compounds and was enriched in polysaccharides, indicating comparatively weaker mineralization and stronger stabilization of these substances. Conclusions: Our findings highlight the importance of chemical structure in SOM pools and help to understand the influence of N deposition on SOM transformation.

China has been a leader in global carbon emissions since 2006. The question of how to reduce emissions while maintaining stable economic growth is a serious challenge for the country. To achieve this, it is of great significance to track the spatial and temporal evolution of carbon emissions in China during recent decades, which can provide evidence-based scientific guidance for developing mitigation policies. In this study, we calculated the carbon emissions of land use in 1999–2015 using the carbon emissions factor method proposed by the Intergovernmental Panel on Climate Change (IPCC). The Kuznets curve model was used to explore the influence of economic growth and urbanization on carbon emissions at the national and provincial levels. The results indicated that (1) China’s emissions increased from 927.88 million tons (Mt) in 1999 to 2833.91 Mt in 2015 at an average annual growth rate of 12.94%, while carbon sinks grew slightly, from 187.58 Mt to 207.19 Mt. Both emissions and sinks presented significant regional differences, with the Central and Southwest regions acting as the biggest emissions and sink contributors, respectively. (2) Built-up land was the largest land carrier for carbon emissions in China, contributing over 85% to total emissions each year; and (3) at the national level, the relationships between economic growth, urbanization, and carbon emissions presented as inverted U-shaped Kuznets curves, which were also found in the majority of the 30 studied provinces. While carbon emissions may be reaching a peak in China, given the disproportionate role of built-up land in carbon emissions, efforts should be devoted to limiting urbanization and the production of associated carbon emissions.

Full-waveform hyperspectral LiDAR (FWHSL) is able to obtain spectral and spatial information by utilizing a single instrument, and it has become more and more commonly used in vertical distribution studies of structural and biochemical characteristics of vegetation. However, the pulse-echo arrival times of multiple spectral channels of the FWHSL are not consistent and this causes range ambiguity in spectral channels. In this paper, the pulse signal decay effect on range measurements was studied by measuring the varying trends of pulse signal decay between spectral channels with different material properties. The experiments were repeated at different distances. All of the spectral channels were compared for different materials. The results suggest that the channels in the red edge spectral region of vegetation have good stability and accuracy for range measurements of varied distance and materials properties. Finally, based on the geometric invariability in a specific red edge channel, a practical calibration approach for the pulse signal decay effect is also presented. The validation tests showed it could improve the pulse signal decay effect of full-waveform hyperspectral LiDAR.