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Water resources are the premise of sustainable agricultural development in Northeast China (NEC), the country’s largest granary. Revealing the long-term evolution of water resources from multiple perspectives, such as surface water area (SWA) and groundwater storage (GWS), is crucial for understanding their current status and predicting future trends to formulate adaptive strategies to promote sustainable water management. Here, firstly, we annually mapped surface water in 1987–2023 using the Google Earth Engine (GEE) cloud service, all these historical 30-m Landsat records, and a mature surface water detection algorithm; then, we calculated annual GWS using Gravity Recovery and Climate Experiment (GRACE) satellite data and compared the trends of SWA and GWS to analyze the current status and evolution direction of water resources. We found continuous expansion of SWA (169.3 km2/yr) since 2002 after a 15-year consecutive decline, while GWS significantly declined (2.37 mm/yr). Quantitative attribution analyses identified the increasing artificial lakes and aquaculture ponds as the major drivers of surface water expansion, with a contribution of 85.0 %, suggesting that the expanding surface water bodies seen from space were just an illusion of increasingly abundant water resources. In addition, we revealed the increasing water evaporation (0.2 km3/yr) and the ratio (0.005–0.2 %/yr) of water evaporation to evapotranspiration (ET) with surface water expansion, indicating that the contribution of water evaporation to ET is more prominent and evaporative water loss has become a new threat to local water security and deserves much more attention. Our research can not only bring new insights into the current status of water security in China’s largest granary but also provide a typical case for reference in analyzing water resource changes in other similar regions.

Abstract The amount of actively cultivated land in China is increasingly threatened by rapid urbanization and rural population aging. Quantifying the extent and changes of active cropland and cropping intensity is crucial to global food security. However, national-scale datasets for smallholder agriculture are limited in spatiotemporal continuity, resolution, and precision. In this paper, we present updated annual Cropland Use Intensity maps in China (China-CUI10m) with descriptions of the extent of fallow/abandoned, actively cropped fields and cropping intensity at a 10-m resolution in recent six years (2018–2023). The dataset is produced by robust algorithms with no requirements for regional adjustments or intensive training samples, which take full advantage of the Sentinel-1 (S1) SAR and Sentinel-2 (S2) MSI time series. The China-CUI10m maps have achieved high accuracy when compared to ground truth data (Overall accuracy = 90.88%) and statistical data (R2 > 0.94). This paper provides the recent trends in cropland abandonment and agricultural intensification in China, which contributes to facilitating geographic-targeted cropland use control policies towards sustainable intensification of smallholder agricultural systems in developing countries.

Abstract Accurate cropland map serves as the cornerstone of effective agricultural monitoring. Despite the continuous enrichment of remotely sensed cropland maps, pervasive inconsistencies have impeded their further application. This issue is particularly evident in areas with limited valid observations, such as southwestern China, which is characterized by its complex topography and fragmented parcels. In this study, we constructed multi-sourced samples independent of the data producers, taking advantage of open-source validation datasets and sampling to rectify the accuracy of ten contemporary cropland maps in southwestern China, decoded their inconsistencies, and generated a refined cropland map (CroplandSyn) by leveraging ten state-of-the-art remotely sensed cropland maps released from 2021 onwards using the self-adaptive threshold method. Validations, conducted at both prefecture and county scales, underscored the superiority of the refined cropland map, aligning more closely with national land survey data. The refined cropland map and samples are publicly available to users. Our study offers valuable insights for improving agricultural practices and land management in under-monitored areas by providing high-quality cropland maps and validation datasets.

Early identification of rubber plantations holds significant importance for both optimal plantation management and scientific studies. Even though remote sensing techniques for mapping rubber plantations have evolved considerably since the 2000s, current methods are highly effective in detecting mature rubber plantations (MRPs), which have distinctive forest characteristics, but often fail to identify immature rubber plantations (IRPs) in a timely manner. This leads to an estimated lag of at least five years in the resulting rubber plantation maps. This paper presents a novel algorithm aimed at promptly pinpointing IRPs in the early planting stage by harnessing a composite of multi-source time series satellite imagery on Google Earth Engine (GEE) platform. Twelve scenarios with different time intervals (3 months, 6 months, and 12 months) and datasets (Landsat 8, Sentinel-2, and Sentinel-1) were tested to determine the most efficient strategy for identifying IRPs using a random forest algorithm. The results demonstrate that the data cube constructed from Landsat 8, Sentinel-2, and Sentinel-1 with 3-month time intervals yields the most accurate identification accuracy. Specifically, it achieves a remarkable overall accuracy of 0.78, 0.87, and 0.93 for plantations established during the second, third, and fourth years, respectively. When implemented on Hainan Island, China’s second-largest natural rubber producing base, the algorithm unveiled a significant decline trend in rubber plantation areas since 2015. Additionally, the spatial distribution exhibited pronounced heterogeneity: while the western and northern regions saw dense immature plantation clusters, the eastern coastal regions hosted only sparse plantations. These up-to-date maps of IRPs are valuable in predicting rubber production, enhancing the monitoring and management practices, and promoting the sustainable development of natural rubber industry.

The widely distributed surface water bodies (e.g., lakes and reservoirs) are important components of water resources on Earth, their spatial–temporal dynamics can pose significant effects on regional water resources. Previous studies generally attributed variability in terrestrial water storage (TWS) to groundwater dynamics, hardly considering surface water changes, making the influence of the changing surface water storage on TWS unclear. Here, by taking Hulun Lake as an example, which occupies 94.0 % of the total water areas in the target basin, we quantitatively revealed the impacts of changes in lake water volume on TWS. Using cloud computing-based water body monitoring and altimetry satellite-based water level measurements, we found continuous lake shrinkage (25 km2/yr) before 2012 while rapid expansion after that. By coupling water areas with levels, annual lake volumes were estimated. We showed that lake volumes also significantly declined (−0.52 km3/yr) before 2012, and then rapidly restored (0.65 km3/yr) due to the increasing water recharge from rivers. The increasing rate of lake water volume was higher than that of TWS (0.15 km3/yr), indicating the great contribution of lake volume increases to TWS gains. Our study highlights the considerable impacts of the changing surface water bodies on regional water resources and it is essential to consider storage changes in surface water when attributing the drivers of TWS variability in regions experiencing dramatic lake changes. Our method proposed is also applicable to evaluating the impacts of surface water changes on TWS in other similar areas around the world.

Accurate cropland information is critical for agricultural planning and production, especially in food-stressed countries like China. Although widely used medium-to-high-resolution satellite-based cropland maps have been developed from various remotely sensed data sources over the past few decades, considerable discrepancies exist among these products both in total area and in spatial distribution of croplands, impeding further applications of these datasets. The factors influencing their inconsistency are also unknown. In this study, we evaluated the consistency and accuracy of six cropland maps widely used in China in circa 2020, including three state-of-the-art 10-m products (i.e., Google Dynamic World, ESRI Land Cover, and ESA WorldCover) and three 30-m ones (i.e., GLC_FCS30, GlobeLand 30, and CLCD). We also investigated the effects of landscape fragmentation, climate, and agricultural management. Validation using a ground-truth sample revealed that the 10-m-resolution WorldCover provided the highest accuracy (92.3%). These maps collectively overestimated Chinese cropland area by up to 56%. Up to 37% of the land showed spatial inconsistency among the maps, concentrated mainly in mountainous regions and attributed to the varying accuracy of cropland maps, cropland fragmentation and management practices such as irrigation. Our work shed light on the promotion of future cropland mapping efforts, especially in highly inconsistent regions.

Abstract Wind‐induced crop lodging can reduce agricultural production and impact food security. However, a systematic evaluation of large‐scale crop lodging and its drivers is lacking, mostly due to the limited observations available. Such knowledge gaps hinder the application of effective management practices to mitigate yield losses. Here, we quantify maize‐lodging induced by three consecutive typhoons in Northeast China in 2020 using direct crop canopy height observations from the Global Ecosystem Dynamics Investigations (GEDI) instrument and optical satellite imagery. We show that the canopy water‐related vegetation index provides a robust wall‐to‐wall mapping of crop lodging, supported by changes in GEDI canopy height. Our spatially contiguous lodging maps show that lodging intensity is mainly due to typhoon intensity (64.34%), with maize plantation fraction (11.94%) and phenology (7.94%) as secondary factors. The shelterbelt forest may partly alleviate the lodging risk, but its effect requires further investigation. Considering the recent expansion of maize cultivation and the poleward shift of landfalling typhoons alongside climate change, the lodging risk may increase for maize and further threaten China's food security.

Knowledge of forest management types is key to sustainable forest restoration practices, forest biomass assessment, and carbon accounting. However, there are no available global forest-management maps because of the spectral similarity of different forest management types. As such, we applied random forest and change detection algorithms to generate annual maps of 6 forest management types at a spatial resolution of 250 m from 2001 to 2020 including naturally regenerated forest (unmanaged and managed), planted forest (rotation of >15 years and ≤15 years), oil palm plantation, and agroforestry. In general, validation results on a point scale show that the overall accuracy is 86.82% ± 9.14%, indicating that our annual maps accurately represent global spatiotemporal variations in forest management types. Furthermore, we estimated the annual biomass carbon stock of different forest management types. The net expanded areas of planted forest, oil palm plantation, and agroforestry offset 59.56% of the loss of forest area and 77.13% of the loss of biomass carbon stock due to the decrease in the naturally regenerated forest. The decrease of managed natural regeneration forests, the expansion of planted forests with a rotation period of more than 15 years, and agroforestry resulted from reforestation practices, while the expansion of planted forests with a rotation period of less than 15 years and oil palm plantations resulted from the removal of part of agroforestry. Moreover, the expansion of planted forests with a rotation of less than 15 years (72.73%) dominates the global expansion of planted forests, and China has contributed 42.20% of this expansion. Our results are beneficial for nature solution-based climate change mitigation.

Grassland degradation (within-state change) and desertification (state conversion from grasslands to deserts) are different states in the complex dynamic process of grassland deterioration. However, the dynamics and associated drivers of the two states have been rarely examined separately, which is critical for early warning and conservation policy making. Here, the grassland degradation and desertification in Central Asia (CA) were studied due to their evident vulnerability to climate extremes and human activities. We performed a two-step approach to identify the critical state zones first (i.e., grasslands, deserts, and the desertification zones) and then assess the vegetation dynamics within each zone. Finally, we quantified the roles of CO2 fertilization effect, climate change, climate variability, land use (LU), and anthropogenic climate change (ACC) on the vegetation dynamics in different state zones. The results showed that the areas of grasslands and sparse vegetation regions (including deserts and desertification zones) were stable from 2000 to 2020, but the trends of vegetation greenness were divergent with a reduction in grasslands and an increase in sparse vegetation regions. Furthermore, climate change and climate variability were the main driving force affecting grassland degradation in CA. Yet the vegetation greening in the desertification and desert zones was mainly driven by the rising CO2 and LU. Moreover, ACC degraded 20.63% of grasslands and sparsely vegetated lands in CA on the baseline of 2000. The results highlighted the risks of grassland degradation in CA. This study proposed a methodology to examine grassland degradation and desertification in detail and quantify the associated driving factors (especially ACC) at the pixel scale, which provided some insights on developing region-precise strategies for grassland conservation in CA and other drylands.

Forage supply has been stressed due to the rapid increase in China's livestock consumption. However, the long-term dynamics of the relationships between forage demand and multi-sourced supply are not understood. Here, we examine the annual forage demand, or practical carrying capacity (PCC), and supply, or theoretical carrying capacity (TCC) from 2000 to 2019 in China. We construct a forage supply-demand index (FSDI) to represent the forage supply pressure using MODIS-derived net primary productivity products and provincial statistical datasets, and we consider two scenarios. First, natural grasslands are the sole source of forage. Second, natural grassland forage supply is supplemented with straw crops. We find an increase in PCC in northwestern China's major pastoral and agropastoral provincial regions, including Inner Mongolia, Gansu, Ningxia and Qinghai, at rate of 0.24-3.59 million sheep units (SU) a year. As the primary source of forage, the theoretical carrying capacity of natural grasslands (TCCgrass) expanded at a rate of 3 million SU/yr nationally. Crop straws fed 126.58 million SU nationally in 2019, which accounted for 11.3% of the total practical carrying capacity and alleviated the forage supply pressure by reducing FSDI by 26.56%. During 2000–2019, the theoretical carrying capacity of straw crops (TCCcrop) increased rapidly from 76.5 million SU to 126.6 million SU, which accounted for 10%-15% of the total forage supply at the national scale. We also discovered large carrying capacity gaps (TCCgap) in the northwestern pastoral provincial regions of Inner Mongolia, Xinjiang, Gansu, and some agricultural provinces such as Shandong and Henan, when we considered forage supply from both natural grasslands and straw crops. Our findings showed a large forage gap in the traditional pastoral regions, and we also discussed green fodder as a potential solution for balancing the supply of and demand for forage, which may shed light on crop and forage planning.

Summary: Floods occur more frequently in the context of climate change; however, flood monitoring capacity has not been well established. Here, we used a synergic mapping framework to characterize summer floods in the middle and lower reaches of the Yangtze River Plain and the effects on croplands in 2020, from both flood extent and intensity perspectives. We found that the total flood extent was 4936 km2 from July to August, and for flood intensity, 1658, 1382, and 1896 km2 of areas experienced triple, double, and single floods. A total of 2282 km2 croplands (46% of the flooded area) were inundated mainly from Poyang and Dongting Lake Basins, containing a high ratio of moderate damage croplands (47%). The newly increased flooding extent in 2020 was 29% larger than the maximum ever-flooded extent in 2015–2019. This study is expected to provide a reference for rapid regional flood disaster assessment and serving mitigation.

Measurement(s) irrigation area and distribution Technology Type(s) statistics and satellite remote sensing Factor Type(s) agricultural irrigation Sample Characteristic - Organism agricultural irrigation Sample Characteristic - Environment agricultural field Sample Characteristic - Location China

Eucalyptus plantations promote the economic development of forestry in southern China, but many studies have reported their negative environmental impacts, such as high water resource usage of certain species of Eucalyptus plants and losses in biodiversity. To date, annual maps of Eucalyptus plantations at large scales with high spatial resolutions are not yet available. Here, we investigated the spectral properties of Eucalyptus plantations and developed a knowledge-based Eucalyptus plantation mapping algorithm. We produced annual maps of Eucalyptus plantation at 10-m spatial resolution in the Guangxi Zhuang Autonomous Region (Guangxi), China, using our proposed algorithm and images of ALOS PALSAR-2, Sentinel-2, and Landsat (ETM+/OLI) in a single year. First, we generated annual evergreen forest maps using PALSAR-2 and Landsat/Sentinel-2-based vegetation index time series data. Second, we distinguished Eucalyptus plantations from the evergreen forest layer using the unique biophysical features of Eucalyptus plantations, the Sentinel-2 red edge bands, and Landsat/Sentinel-2-based enhanced vegetation index (EVI). Our resultant 2020 Eucalyptus plantation map had high producer's, user's, and overall accuracies of 0.85, 0.89, and 0.96, respectively. There were 3.10 × 106 ha of Eucalyptus plantation in Guangxi in 2020. Among the 14 administrative units, Wuzhou City had the largest Eucalyptus plantation area in Guangxi, followed by Nanning, Baise, and Chongzuo cities. We demonstrated the potential of knowledge-based mapping approaches for identifying evergreen forest and Eucalyptus plantations in complex and fragmented landscapes where cloud cover is frequent. Our 10-m Eucalyptus plantation map is the most current dataset available and can be used to assist the sustainable production of Eucalyptus, ecological assessments, and conservation.

Measurement(s) lake area and number Technology Type(s) satellite images and coding Sample Characteristic - Organism lake Sample Characteristic - Environment plateau-climate Sample Characteristic - Location Tibetan Plateau

It is imperative to rapidly and precisely acquire crop lodging area and severity for disaster prevention and yield prediction. However, estimation of crop lodging area at a large scale remains challenging due to the relatively low sensitivity of remote sensing signal to the lodging variation, limited availability of remote sensing images, and lodging statistical data. This study proposes a new method for lodging area estimation based on the optimal grid cell of Sentinel-2 and crop lodging percentage, overcoming the limitation of traditional pixel-based mapping approaches that fail to obtain quantitative lodging information. Basing the spatial aggregation method, we analyzed the optimal grid size of Sentinel-2 data for lodging percentage estimation. Then we investigated the spectral response for different lodging percentage levels and analyzed the potential of lodging percentage estimation for Sentinel-2 metrics (including selected spectral bands and their derived vegetation indexes (VIs)). A quantitative model was established between the training set and the Sentinel-2 metrics using the random forest (RF) algorithm. Finally, around 1462.62 ha fields from six counties or districts in Heilongjiang province in China were estimated for lodging percentage. Results indicate that the proposed method can estimate the crop lodging percentage on the testing set with an R2 and RMSE of 0.64 and 25.24, respectively, which can explain around 95 % spatial variation of lodging crop. Moreover, the overall magnitude of reflectance increased with the increase in lodging percentage. Among all Sentinel-2 optimal metrics, the Green, SWIR1, and Red edge 1 bands are the most crucial indicators for lodging percentage estimation. Our results on lodging percentage estimation in the study area indicate that there is more lodging maize in the Meilisidawoerzu district than in other areas. Although typhoons passed over Fuyu and Lindian counties, the lodging percentage in these areas is relatively low. The lodging percentage map has great value in agriculture management and insurance claim.

Agricultural irrigation is an important vehicle for increasing crop yield, but large-scale irrigation has posed great challenges to global and regional water availability and climate change via altering land–atmosphere interactions. The knowledge of irrigation distribution is essential to understand regional water cycles and guide agricultural management decision-making, but such information is scarce in China. We developed a remote sensing-dominated framework to map irrigated croplands in China at 500 m resolution using a synergetic training sample generating method, machine learning classifier, and a cloud computing platform (Google Earth Engine, GEE). To overcome the challenges of lacking nationwide training samples, we first produced two provisional irrigation maps by fusing statistics and MODIS-derived annual peak greenness indices. The two provisional irrigation maps were then spatially filtered with an existing irrigation product (GRIPC) to construct the initial training sample pool. Next, to enhance the robustness and cover more irrigated candidates, we screened and introduced the irrigated croplands in three land use/cover maps (CCI-LC, GLC_FCS, and NLCD) to supplement the training data pool. Afterward, we utilized locally adaptive random forest classifiers and data cubes (MODIS-derived spectral indices, climatic and topographic variables) to generate irrigation maps in each province of China on GEE. The resulting map outperformed other current irrigation maps with an overall accuracy of 79.2% . The map also showed a reasonable consistency with statistical data at the province and prefecture levels, with the determination coefficient (R2) of 0.89 and 0.77, respectively. In total, we identified 87.04 million hectares of irrigated croplands in mainland China in 2015. Using the resulting map and water use statistics, we found a high correlation between irrigation area and agricultural water use in Northwest, Northeast, and South China, and a low correlation in North China Plain. This map is expected to serve national water resource management and assist decision-making in improving agricultural adaption to climate change.

Wetlands are important CO2 sinks and methane sources, and their seasonality and phenological cycle play an essential role in understanding the carbon budget. However, given the spatial heterogeneity of wetland landscapes and the coarser spatial resolution of satellites, the phenological retrievals of wetlands are challenging. Here we examined the phenology of wetlands from 30 m harmonized Landsat/Sentinel-2 (LandSent30) and 500 m MODIS satellite observations using the ground phenology network PhenoCam as a benchmark. This study used all 11 available wetland PhenoCam sites (about 30 site years), covering diverse wetland types from different climate zones. We found that the LandSent30-based phenology results were in overall higher consistency with the PhenoCam results compared to MODIS, which could be related to the better explanation capacity of LandSent30 data in the heterogeneous landscapes of wetlands. This also means that the LandSent30 has an advantage over the 500 m MODIS regarding wetland vegetation phenological retrievals. It should be noted that the LandSent30 did not show a greatly improved performance, which could be related to the specificity and complexity of the wetlands landscape. We also illustrated the potential effects of the location and observation direction of PhenoCam cameras, the selection of Region of Interest (ROI), as well as the landscape composition of the site. Overall, this study highlights the complexity of wetland phenology from both ground and remote sensing observations at different scales, which paves the road for understanding the role of wetlands in global climate change and provides a basis for understanding the real phenological changes of wetland surfaces.

Deciduous broadleaf forests (DBF) are an extremely widespread vegetation type in the global ecosystem and an indicator of global environmental change; thus, they require accurate phenological monitoring. However, there is still a lack of systematic understanding of the sensitivity of phenological retrievals for DBF in terms of different spatial resolution data and proxy indices. In this study, 79 globally distributed DBF PhenoCam Network sites (total 314 site-years, 2013–2018) were used as the reference data (based on green chromaticity coordinates, GCC). Different spatial resolutions (30 m Landsat and Sentinel-2 data, and 500 m MCD43A4 data) and satellite remote sensing vegetation indices (normalized difference vegetation index, NDVI; enhanced vegetation index, EVI; and near-infrared reflectance of vegetation, NIRV) were compared to find the most suitable data and indices for DBF phenological retrievals. The results showed that: (1) for different spatial resolutions, both 30 m Landsat–Sentinel-2 data and 500 m MODIS data accurately captured (R2 > 0.8) DBF phenological metrics (i.e., the start of the growing season, SOS, and the end of the growing season, EOS), which are associated with the comparatively homogeneous landscape pattern of DBF; (2) for SOS, the NIRv index was closer to GCC than EVI and NDVI, and it showed a slight advantage over EVI and a significant advantage over NDVI. However, for EOS, NDVI performed best, outperforming EVI and NIRv; and (3) for different phenological metrics, the 30 m data showed a significant advantage for detecting SOS relative to the 500 m data, while the 500 m MCD43A4 outperformed the 30 m data for EOS. This was because of the differences between the wavebands used for GCC and for the satellite remote sensing vegetation indices calculations, as well as the different sensitivity of spatial resolution data to bare soil. This study provides a reference for preferred data and indices for broad scale accurate monitoring of DBF phenology.

Measurement(s) area of different crop types • area of cropland Technology Type(s) machine learning Factor Type(s) type of crop • year of data collection Sample Characteristic - Environment cultivated environment • cropland ecosystem Sample Characteristic - Location Northeast China • China Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13567526

Lucid waters and lush mountains are invaluable assets. The countryside is an important carrier of these assets, and rural revitalization is a national strategy to meet the needs of the people. This study constructs the framework system for improving the efficiency of resource utilization, refining the effect of urban–rural integration, and optimizing the efficiency of industrial development for increasing farmers’ income. The challenges facing rural revitalization are clarified, especially including limited space for the growth of cultivated land quantity and significant challenges for quality improvement, insufficient effective supply of labor resources, uneven spatial distribution of water resources, low utilization efficiency of agricultural water resources, and so on. Finally, it puts forward the possible direction of future policies for rural revitalization, which is mainly reflected in the transformation of resource utilization efficiency improvement from single element to multi-element, the development of an urban–rural integration effect from extensive to lean, and the optimization of the industrial enrichment efficiency from management to comprehensive service. The analytical framework of resource utilization, urban–rural integration development, and industrial enrichment will provide regulatory policies and theoretical support for the flow of urban–rural factors.

The authors of this study compile data on spatial and temporal dynamics of surface water bodies across China, covering a time span from 1989 – 2016. The study describes hot-spot areas with strongly decreasing trends in surface water area and terrestrial water storage in North China and discusses implications of water resources and security in China.

Agricultural drought threatens food security. Numerous remote-sensing drought indices have been developed, but their different principles, assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas. Here, we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces, northern China (predominantly rain-fed and irrigated agriculture, respectively): vegetation health index (VHI), temperature vegetation dryness index (TVDI) and drought severity index (DSI). We compared the agreement between these indices and the standardized precipitation index (SPI), soil moisture, winter wheat yield and National Meteorological Drought Monitoring (NMDM) maps. On average, DSI outperformed the other indices, with stronger correlations with SPI and soil moisture. DSI also corresponded better with soil moisture and NMDM maps. The jointing and grain-filling stages of winter wheat are more sensitive to water stress, indicating that winter wheat required more water during these stages. Moreover, the correlations between the drought indices and SPI, soil moisture, and winter wheat yield were generally stronger in Shaanxi province than in Henan province, suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas.

The role of paddy rice agriculture in the spatial and temporal dynamics of atmospheric methane concentration remains unclear. Here, Zhang et al. show that regions with dense rice paddies have high satellite-based column averaged CH4 concentrations (XCH4), and that seasonal dynamics of XCH4 mirror those of paddy rice growth.

Unique ecosystems distributed in alpine areas of the Qinghai–Tibetan Plateau play important roles in climate change mitigation, local food supply, and conservation of species diversity. To understand the water use efficiency (WUE) of this fragile and sensitive region, this study combined observed data from the eddy covariance system and the Shuttleworth–Wallace (S-W) model to measure the continuous mass exchange, including gross primary productivity (GPP), evapotranspiration (ET), and canopy transpiration (T) throughout 2 or 3 years (2016–2018) in three common alpine ecosystems (i.e., alpine steppe, alpine meadow, and alpine swamp). These ecosystems represent a water availability gradient and thus provide the opportunity to quantify environmental and biological controls on WUE at various spatiotemporal scales. We analyzed the ecosystem WUE (WUEe; defined as the ratio of GPP to ET) and canopy WUE (WUEc; defined as the ratio of GPP and canopy T). It was found that the yearly WUEe was 1.40, 1.63, and 2.16 g C kg–1 H2O, and the yearly WUEc was 8.93, 2.46, and 5.19 g C kg–1 H2O in the three typical ecosystems, respectively. The controlling factors of yearly WUE diverged between WUEe and WUEc. We found that plant functional group proportion (e.g., gramineous and Cyperaceae) highly explained the yearly WUEe variation across sites, and a good correlation was observed between community species diversity and WUEc. These findings suggest that community composition and trait change are critical in regulating WUEe and WUEc across different alpine ecosystems and that the regulation mechanisms may differ fundamentally between WUEe and WUEc.

Urbanization affects vegetation within city administrative boundary and nearby rural areas. Gross primary production (GPP) of vegetation in global urban areas is one of important metrics for assessing the impacts of urbanization on terrestrial ecosystems. To date, very limited data and information on the spatial-temporal dynamics of GPP in the global urban areas are available. In this study, we reported the spatial distribution and temporal dynamics of annual GPP during 2000–2016 from 8,182 gridcells (0.5° by 0.5° latitude and longitude) that have various proportion of urban areas. Approximately 79.3% of these urban gridcells had increasing trends of annual GPP during 2000-2016. As urban area proportion (%) within individual urban gridcells increased, the means of annual GPP trends also increased. Our results suggested that for those urban gridcells, the negative effect of urban expansion (often measured by impervious surfaces) on GPP was to large degree compensated by increased vegetation within the gridcells, mostly driven by urban management and local climate and environment. Our findings on the continued increases of annual GPP in most of urban gridcells shed new insight on the importance of urban areas on terrestrial carbon cycle and the potential of urban management and local climate and environment on improving vegetation in urban areas.

Urban vegetation experiences multiple natural and human impacts during urbanization, including land conversion, local environmental factors, and human management, which may bring positive or negative impacts on vegetation gross primary productivity (GPP) at multiple scales. In this study, we analyzed the spatial-temporal changes of GPP and three urbanization factors: land urbanization (impervious surface coverage), population urbanization (Population), and economic urbanization Gross domestic product (GDP) at city-district-grid scales in Beijing during 2000–2018. Overall, both GPP and three urbanization factors showed an increased trend. The relationships between GPP and urbanization factors exhibit diverse characteristics at multiple scales: unlike the linear relationship observed at city scale, the relationships at district and grid scales all demonstrated nonlinear relationship, even a U shape between GPP and population/GDP. Furthermore, the positive impact of urbanization on GPP increased and offset the negative impact of land conversion from 9.9% in 2000 to 35% in 2018, indicating that urban management and climate during urbanization effectively promote vegetation photosynthesis and neutralize the negative impact of urban area expansion. Our findings highlight the increased growth offset by urbanization on vegetation and the importance of analysis at a finer scale. Understanding these urbanization types’ impact on vegetation is pivotal in formulating comprehensive strategies that foster sustainable urban development and preserve ecological balance.

With increasing livestock production due to high demand for consumption, the planted area of green fodder, an essential livestock supplement, has grown rapidly and will continue to grow in China. However, the climate feedback of this rapid land cover conversion is still unclear. Using multisource data (e.g. remote sensing observation and meteorological data), we compared the land surface temperature of green fodder plantation areas and native grassland in the northeastern Tibetan Plateau. The green fodder area was detected to be cooler than the native grassland by −0.54 ± 0.98 °C in the daytime throughout the growing season. The highest magnitude (−1.20 ± 1.68 °C) of cooling was observed in August. A nonradiative process, indicated by the energy redistribution factor, dominated the cooling effects compared to the radiative process altered by albedo variation. The results indicate the potential cooling effects of increasing green fodder area on native grassland, highlighting the necessity of investigating climate feedback from anthropogenic land use change, including green fodder expansion.

Vegetation greening contributes to gross carbon sequestration by plants and thus plays a vital role in mitigation of global warming. Terrestrial vegetation has a significant greening trend in South Asia (SA); however, it remains elusive how this greening translates to gross primary production (GPP) (proxy of gross carbon uptake by plants at ecosystem scale) in SA. In this study, we investigated the spatiotemporal dynamics of GPP in correspondence to leaf area increase (based on leaf area index as proxy of greening) over SA using remote sensing driven data on Google Earth Engine (GEE) cloud computation, during 2003–2017. We found a much weaker increase of GPP in SA as compared to the overall greening trend, primarily attributed to the disturbance in cropland activities of the region. Especially, greening was highly significant (p 0.05) over this region. Further, natural vegetation areas also showed low vegetation greening and GPP increase during the study period. The analysis of the potential drivers revealed that both climatic and anthropogenic stresses (intensive cropping, population pressure) had a negative impact on the regional GPP, especially in IGP. Our study indicated that the overwhelming greening of SA because of enhanced cropland activities, did not contribute much to GPP over the region. The restricted cropland canopy carbon uptake and low GPP increase in forests led to a lower than expected increase of gross carbon assimilation over SA in the context of climate changes and anthropogenic disturbances. Our study inferred that effective reforestation programs and conservation of natural vegetation areas are essential in SA for efficient vegetation carbon assimilation.

Remote Sensing (RS) has been used in urban mapping for a long time; however, the complexity and diversity of urban functional patterns are difficult to be captured by RS only. Emerging Geospatial Big Data (GBD) are considered as the supplement to RS data, and help to contribute to our understanding of urban lands from physical aspects (i.e., urban land cover) to socioeconomic aspects (i.e., urban land use). Integrating RS and GBD could be an effective way to combine physical and socioeconomic aspects with great potential for high-quality urban land use classification. In this study, we reviewed the existing literature and focused on the state-of-the-art and perspective of the urban land use categorization by integrating RS and GBD. Specifically, the commonly used RS features (e.g., spectral, textural, temporal, and spatial features) and GBD features (e.g., spatial, temporal, semantic, and sequence features) were identified and analyzed in urban land use classification. The integration strategies for RS and GBD features were categorized into feature-level integration (FI) and decision-level integration (DI). To be more specific, the FI method integrates the RS and GBD features and classifies urban land use types using the integrated feature sets; the DI method processes RS and GBD independently and then merges the classification results based on decision rules. We also discussed other critical issues, including analysis unit setting, parcel segmentation, parcel labeling of land use types, and data integration. Our findings provide a retrospect of different features from RS and GBD, strategies of RS and GBD integration, and their pros and cons, which could help to define the framework for future urban land use mapping and better support urban planning, urban environment assessment, urban disaster monitoring and urban traffic analysis.

Implementation of a measuring, reporting, and verifying (MRV) framework is essential for reducing emissions from deforestation and forest degradation (REDD+). According to the United Nations Framework Convention on Climate Change, MRV can be regarded as an important mechanism to mitigate global warming. Upland Myanmar, with an elevation of ~80–2600 m, is experiencing tropical deforestation, which is commonly explained by the expansion of shifting cultivation. The vegetation change tracker algorithm, with its high-automation and wild-adaptation features, and the enhanced integrated forest z-score were applied in this elevation-based study of time series deforestation monitoring in upland Myanmar using data from 2003 to 2015. Four spatial patterns of deforestation, namely stripes, adjacent, filled, and staggered, were found in the research area. Moreover, our work showed that the center of elevation of deforestation was ~1000 m. Further analysis revealed that this center tended to shift to a higher elevation over time; a “golden cross”/changeover could be deciphered at ~1000 m, indicating that the scale and intensity of shifting cultivation continue to expand vertically. The results suggest the need to track the elevation-based signature of vegetation clearings to help achieve the goals of REDD+ at the regional level in tropical rainforest countries.

Urban green space (e.g., parks, farmland, gardens, etc.) design in different urban functional regions (e.g., residential land, commercial land, etc.) depends on different planning purposes. The changes in urban green spaces are highly related to urban land-use changes (e.g., from residential land to commercial land). However, the investigation of urban green space patterns in response to urban land-use changes has been ignored. This research takes Hangzhou city, a typical example in terms of urbanization, population growth, economic development, and land-use changes, as the study site, aiming to explore the landscape patterns of urban green space changes with different urban land-use changes. The results showed that urban green spaces increased from 2017 to 2021, and the growth was mainly concentrated in the urban core area, indicating that the city has made remarkable achievements in the planning of green spaces. Specifically, the increase in urban green spaces in the first ring belt was mainly related to the old town transformation program in the residential land. The change from the residence parcels to the business parcels determined the increase in green spaces in the second and third ring belts, probably because of the attractiveness of customers. In addition, a large number of open-space parcels have changed to business parcels around the urban periphery, which might be due to the transformation from farmland to impervious surfaces. Combined with the urban land-use and green-space policies, the findings highlighted that a reasonable urban land-use layout can promote the optimization and layout of urban green spaces. The private sector (e.g., shopping malls) can also contribute to the increase in green spaces. The understanding of urban green landscapes with different urban land-use changes can provide references for analyzing and optimizing green space in other cities experiencing rapid urban land-use changes.

With advancements in big geospatial data and artificial intelligence, multi-source data and diverse data-driven methods have become common in dengue risk prediction. Understanding the current state of data and models in dengue risk prediction enables the implementation of efficient and accurate prediction in the future. Focusing on predictors, data sources, spatial and temporal scales, data-driven methods, and model evaluation, we performed a literature review based on 53 journal and conference papers published from 2018 to the present and concluded the following. (1) The predominant predictors include local climate conditions, historical dengue cases, vegetation indices, human mobility, population, internet search indices, social media indices, landscape, time index, and extreme weather events. (2) They are mainly derived from the official meteorological agency satellite-based datasets, public websites, department of health services and national electronic diseases surveillance systems, official statistics, and public transport datasets. (3) Country-level, province/state-level, city-level, district-level, and neighborhood-level are used as spatial scales, and the city-level scale received the most attention. The temporal scales include yearly, monthly, weekly, and daily, and both monthly and weekly are the most popular options. (4) Most studies define dengue risk forecasting as a regression task, and a few studies define it as a classification task. Data-driven methods can be categorized into single models, ensemble learning, and hybrid learning, with single models being further subdivided into time series, machine learning, and deep learning models. (5) Model evaluation concentrates primarily on the quantification of the difference/correlation between time-series observations and predicted values, the ability of models to determine whether a dengue outbreak occurs or not, and model uncertainty. Finally, we highlighted the importance of big geospatial data, data cloud computing, and other deep learning models in future dengue risk forecasting.

Paddy rice agriculture in Southern China, especially Hunan Province, has been suffered from soil contamination. Several policies including rice fallow and decreasing cropping intensity have been implemented for food safety here. It is thus important to monitor rice planting area and cropping intensity to understand the effectiveness of those land-use policies. However, it is challenging to map rice planting areas due to the complex cropping systems (mixed single- and double-cropping), persistent cloud covers, small crop fields, let alone cropping intensity. Here we used all the available Sentinel-2 and all-weather Sentinel-1 imagery to generate a time series data cube to extract paddy rice planting areas and the rice cropping intensity in the Changsha, Zhuzhou, and Xiangtan areas, which is a traditional rice-growing region with small farms in China. Specifically, we investigated the performances of different features (i.e., spectral, seasonal, polarization backscatter) by comparing five scenarios with different combinations of sensors and features, and identified the most suitable features for certain rice types (early, middle, and late rice). The random forest classifier was used for the classification in the Google Earth Engine (GEE) platform, and a reference map in 2017 based on visual interpretation on the GaoFen-2 images were used for collecting the training and validation samples. The results showed the combined data from Sentinel-1/2 generally outperformed classifications using only a single sensor (Sentinel-1/2), but the contribution of different sensors to certain rice types varied. The early, middle and late rice with the highest accuracies within the five scenarios had the overall accuracies of 85%, 95%, and 95%, respectively (F1 = 0.55, 0.85, and 0.85). The compositing of different types of rice allowed us to generate the rice cropping intensity map with an overall accuracy of 81%, which to our limited knowledge is the first effort to map cropping intensity at 10-m resolution in such a fragmented subtropical region. The result showed the single cropping dominated the rice cropping system in the study area 88%, which used to be a typical area with double cropping of rice. Our study demonstrates the potential of mapping rice cropping intensity in a cloudy and highly fragmented region in South China using all the available Sentinel-1/2 data, which would advance our understanding of regional rice production and mitigation of soil contamination.

Convolutional neural network (CNN)-based remote sensing (RS) image segmentation has become a widely used method for building footprint mapping. Recently, DeeplabV3+, an advanced CNN architecture, has shown satisfactory performance for building extraction in different urban landscapes. However, it faces challenges due to the large amount of labeled data required for model training and the extremely high costs associated with the annotation of unlabelled data. These challenges encouraged us to design a framework for building footprint mapping with fewer labeled data. In this context, the published studies on RS image segmentation are reviewed first, with a particular emphasis on the use of active learning (AL), incremental learning (IL), transfer learning (TL), and their integration for reducing the cost of data annotation. Based on the literature review, we defined three candidate frameworks by integrating AL strategies (i.e., margin sampling, entropy, and vote entropy), IL, TL, and DeeplabV3+. They examine the efficacy of AL, the efficacy of IL in accelerating AL performance, and the efficacy of both IL and TL in accelerating AL performance, respectively. Additionally, these frameworks enable the iterative selection of image tiles to be annotated, training and evaluation of DeeplabV3+, and quantification of the landscape features of selected image tiles. Then, all candidate frameworks were examined using WHU aerial building dataset as it has sufficient (i.e., 8188) labeled image tiles with representative buildings (i.e., various densities, areas, roof colors, and shapes of the building). The results support our theoretical analysis: (1) all three AL strategies reduced the number of image tiles by selecting the most informative image tiles, and no significant differences were observed in their performance; (2) image tiles with more buildings and larger building area were proven to be informative for the three AL strategies, which were prioritized during the data selection process; (3) IL can expedite model training by accumulating knowledge from chosen labeled tiles; (4) TL provides a better initial learner by incorporating knowledge from a pre-trained model; (5) DeeplabV3+ incorporated with IL, TL, and AL has the best performance in reducing the cost of data annotation. It achieved good performance (i.e., mIoU of 0.90) using only 10–15% of the sample dataset; DeeplabV3+ needs 50% of the sample dataset to realize the equivalent performance. The proposed frameworks concerning DeeplabV3+ and the results imply that integrating TL, AL, and IL in human-in-the-loop building extraction could be considered in real-world applications, especially for building footprint mapping.

The principal difficulty in extracting urban surface water using remote-sensing techniques is the influence of noise from complex urban environments. Although various methods exist, there are still many sources of noise interference when extracting urban surface water, and automatic cartographic methods with long time series are especially scarce. Here, we construct an automatic urban surface water extraction method from the combination of traditional water index, urban shadow index (USI), and land surface temperature (LST) by using the Google Earth Engine cloud computing platform and Landsat imagery. The three principal findings derived from the application of the method were as follows. (i) In comparison with autumn and winter, LST in spring and summer could better distinguish water from high-reflection ground objects, shadows, and roads and roofs covered by asphalt. (ii) The overall accuracy of Automated Water Extraction Index (AWEIsh) in Zhengzhou was 77.5% and the Kappa coefficient was 0.55; with consideration of the USI and LST, the overall accuracy increased to 96.0% and the Kappa coefficient increased to 0.92. (iii) During 1990–2020, the area of urban surface water in Zhengzhou increased, with an evident trend in expansion from 11.51 km2 in 2008 to 49.28 km2 in 2020. Additionally, possible omissions attributable to using 30m-resolution imagery to extract urban water areas were also discussed. The method proposed in this study was proven effective in eliminating the influence of noise in urban areas, and it could be used as a general method for high-accuracy long-term mapping of urban surface water.

The phenology-based approach has proven effective for paddy rice mapping due to the unique flooding and transplanting features of rice during the early growing season. However, the method may be greatly affected if no valid observations are available during the flooding and rice transplanting phase. Here, we compare the effects of data availability of different sensors in the critical phenology phase, thereby supporting paddy rice mapping based on phenology-based approaches. Importantly, our study further analyzed the effects of the spatial pattern of the valid observations related to certain factors (i.e., sideslips, clouds, and temporal window lengths of flooding and rice transplanting), which supply the applicable area of the phenology-based approach indications. We first determined the flooding and rice transplanting phase using in situ observational data from agrometeorological stations and remote sensing data, then evaluated the effects of data availability in this phase of 2020 in China using all Landsat-7 and 8 and Sentinel-2 data. The results show that on the country level, the number of average valid observations during the flooding and rice transplanting phase was more than ten for the integration of Landsat and Sentinel images. On the sub-country level, the number of average valid observations was high in the cold temperate zone (17.4 observations), while it was relatively lower in southern China (6.4 observations), especially in Yunnan–Guizhou Plateau, which only had three valid observations on average. Based on the multicollinearity test, the three factors are significantly correlated with the absence of valid observations: (R2 = 0.481) and Std.Coef. (Std. Err.) are 0.306 (0.094), −0.453 (0.003) and −0.547 (0.019), respectively. Overall, these results highlight the substantial spatial heterogeneity of valid observations in China, confirming the reliability of the integration of Landsat-7 and 8 and Sentinel-2 imagery for paddy rice mapping based on phenology-based approaches. This can pave the way for a national-scale effort of rice mapping in China while further indicating potential omission errors in certain cloud-prone regions without sufficient optical observation data, i.e., the Sichuan Basin.

Convolutional neural network (CNN)-based very high-resolution (VHR) image segmentation has become a common way of extracting building footprints. Despite publicly available building datasets and pre-trained CNN models, it is still necessary to prepare sufficient labeled image tiles to train CNN models from scratch or update the parameters of pre-trained CNN models to extract buildings accurately in real-world applications, especially the large-scale building extraction, due to differences in landscapes and data sources. Deep active learning is an effective technique for resolving this issue. This study proposes a framework integrating two state-of-the-art (SOTA) models, U-Net and DeeplabV3+, three commonly used active learning strategies, (i.e., margin sampling, entropy, and vote entropy), and landscape characterization to illustrate the performance of active learning in reducing the effort of data annotation, and then understand what kind of image tiles are more advantageous for CNN-based building extraction. The framework enables iteratively selecting the most informative image tiles from the unlabeled dataset for data annotation, training the CNN models, and analyzing the changes in model performance. It also helps us to understand the landscape features of iteratively selected image tiles via active learning by considering building as the focal class and computing the percent, the number of patches, edge density, and landscape shape index of buildings based on labeled tiles in each selection. The proposed method was evaluated on two benchmark building datasets, WHU satellite dataset II and WHU aerial dataset. Models in each iteration were trained from scratch on all labeled tiles. Experimental results based on the two datasets indicate that, for both U-Net and DeeplabV3+, the three active learning strategies can reduce the number of image tiles to be annotated and achieve good model performance with fewer labeled image tiles. Moreover, image tiles with more building patches, larger areas of buildings, longer edges of buildings, and more dispersed building distribution patterns were more effective for model training. The study not only provides a framework to reduce the data annotation efforts in CNN-based building extraction but also summarizes the preliminary suggestions for data annotation, which could facilitate and guide data annotators in real-world applications.

Greening of the Earth is observed during the past several decades and both climatic and non-climatic factors drive this process. However, the greening spatio-temporal patterns and the role of human activities such as agricultural intensification in hyper-arid regions remain unclear. This study aimed to (a) reveal the greening pattern in China’s southern Xinjiang using satellite estimations of normalized difference vegetation index and leaf area index data during 1982–2019, and (b) examine the impacts of human activities in terms of land use land cover (LULC) data. Our multi-decadal analysis is ideal to reveal long-term trends and support a better understanding of the anthropogenic effects in this hyper-arid and endorheic region. The results showed that vegetation as a whole increased significantly in southern Xinjiang and the greening rate of cropland was much higher than the other LULC types. Significant greening was found over >90% of cropland, while insignificant changes and browning trends were found over nearly half the area of the other LULCs. The proportion of greening areas was more than 80% within 1 km from human-dominated areas while the proportion decreased to 40% with distances >15 km. The spatial heterogeneity of the greening indicated that, despite widely reported beneficial effects of warmer and wetter climate for a general greening trend, human activities could be the dominant factor modulating the greening rates disproportionately over different LULCs in arid and hyper-arid areas.

Cropland area and cropping frequency play very crucial roles in determining regional food production. However, rapid urbanization accompanied by declining surplus-agricultural labor force has greatly altered patterns of agriculture land use and cropping frequency. Due to lack of continuous cropland and cropland-use intensity maps, our knowledge is still limited to understand whether the urbanization process must have a negative effect for changes in cropland-use intensity. Herein, we took the North China Plain (NCP), both the largest winter wheat and summer maize rotation area and rapidly urbanized area in China, as the study area, and used 250 m moderate resolution imaging spectroradiometer NDVI anomaly data, the correlation of NDVI time series in two neighboring years and machine learning algorithms to investigate spatiotemporal patterns and trends of cropland area and cropping frequency change over the NCP from 2000 to 2019. Results showed a significantly decreased cropland area observed since 2004 (slope = 783.8 km ^2 a ^−1 , p < 0.01), while area of double-season cropping presented a relatively steady trend (slope = 446.9 km ^2 a ^−1 , p = 0.335). As expected, decreased croplands were mainly occupied by urban and built-up land expansion, however, existing cropland-use intensity was yet improved. Patterns and trends of double-season cropping types were varied spatially. Particularly, the area of winter wheat and summer maize rotation presented a significantly increasing trend (slope = 3423.3 km ^2 a ^−1 , p < 0.01). Furthermore, the respective area of winter wheat and summer maize both displayed significantly increasing trends with slope of 2953.8 and 2874.9 km ^2 a ^−1 ( p < 0.01) in entire period. Land-use and grain subsidy policies are considered as largely responsible for this phenomenon. These satellite-observed findings highlight that positive land-use policies and managements will be helpful for profitably keeping/improving the harvest area.

Timely and accurate forecasts of dengue cases are of great importance for guiding disease prevention strategies, but still face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation capability due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without the application of historical case information. Geospatial big data, cloud computing platforms (e.g., Google Earth Engine, GEE), and emerging deep learning algorithms (e.g., long short term memory, LSTM) provide new opportunities for advancing these efforts. Here, we focused on the dengue epidemics in the urban agglomeration of the Federal District of Brazil (FDB) during 2007–2019. A new framework was proposed using geospatial big data analysis in the Google Earth Engine (GEE) platform and long short term memory (LSTM) modeling for dengue case forecasts over an epidemiological week basis. We first defined a buffer zone around an impervious area as the main area of dengue transmission by considering the impervious area as a human-dominated area and used the maximum distance of the flight range of Aedes aegypti and Aedes albopictus as a buffer distance. Those zones were used as units for further attribution analyses of dengue epidemics by aggregating the pixel values into the zones. The near weekly composite of potential driving factors was generated in GEE using the epidemiological weeks during 2007–2019, from the relevant geospatial data with daily or sub-daily temporal resolution. A multi-step-ahead LSTM model was used, and the time-differenced natural log-transformed dengue cases were used as outcomes. Two modeling scenarios (with and without historical dengue cases) were set to examine the potential of historical information on dengue forecasts. The results indicate that the performance was better when historical dengue cases were used and the 5-weeks-ahead forecast had the best performance, and the peak of a large outbreak in 2019 was accurately forecasted. The proposed framework in this study suggests the potential of the GEE platform, the LSTM algorithm, as well as historical information for dengue risk forecasting, which can easily be extensively applied to other regions or globally for timely and practical dengue forecasts.

Monoculture rubber plantations have been replacing tropical rain forests substantially in Southern China and Southeast Asia over the past several decades, which have affected human wellbeing and ecosystem services. However, to the best of our knowledge on the extent of rubber plantation expansion and their stand ages is limited. We tracked the spatiotemporal dynamics of deciduous rubber plantations in Xishuangbanna, the second largest natural rubber production region in China, from 2000 to 2010 using time-series data from the Phased Array type L-band Synthetic Aperture Radar (PALSAR), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS). We found that rubber plantations have been expanding across a gradient from the low-elevation plains to the high elevation mountains. The areas of deciduous rubber plantations with stand ages ≤5, 6–10, and ≥11-year old were ~1.2 × 105 ha, ~0.8 × 105 ha, and ~2.9 × 105 ha, respectively. Older rubber plantations were mainly located in low-elevation and species-rich regions (500–900 m) and younger rubber trees were distributed in areas of relative high-elevation with fragile ecosystems. Economic and market factors have driven the expansion of rubber plantations, which is not only a threat to biodiversity and environmental sustainability, but also a trigger for climatic disasters. This study illustrates that the integration of microwave, optical, and thermal data is an effective method for mapping deciduous rubber plantations in tropical mountainous regions and determining their stand ages. Our results demonstrate the spatiotemporal pattern of rubber expansions over the first decade of this century.

Plantations of Panax notoginseng (PN), traditional herbal medicine for the prevention and treatment of vascular diseases, are expanding rapidly in China, especially in the Yunnan province of China, due to its increasing demands and prices and causing dramatic environmental concerns. However, existing information on its planting area and spatial distribution are limited. Here, we mapped the PN planting area by using a new integrated pixel- and object-based (IPOB) approach, the Random Forest (RF) classifier, and the high-resolution ZiYuan-3 (ZY-3) imagery. We improved the procedures of classification in three aspects: (1) a new spectral index—Normalized Difference PN Index (NDPI)—was proposed, (2) the efficiency and scale of segmentation were optimized by using the Bi-level Scale-sets Model (BSM), and (3) feature variables were selected through an iteration analysis from 99 feature variables (spectral, textural, geometric, and geographic). Compared with the pixel- and the object-based methods, the IPOB has the highest F1 score of 0.98 and also has high robustness in terms of user and producer accuracies (97% and 99%, respectively), following by the object-based method (F1 = 0.94) and the pixel-based method (F1 = 0.93). The high accuracy was expected since the target class has very distinctive spectral and textural characteristics. Although all three approaches showed reasonably high accuracies due to the application of the NDPI and optimized procedures, the result showed the outperformance of the proposed IPOB approach. The framework established in this study expects to apply for regional or national PN surveys extensively. The information on the area and spatial distribution of PN can guide the government on policy making for the planting and exporting of traditional Chinese medicine resources.

Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy.

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00854-6.

China launched the Three-North Shelterbelt Forest Program (TNSFP) in 1978 in northern China to combat desertification and dust storms, but it is still controversial in ecologically fragile arid and semi-arid areas, which is partly due to the uncertainties of monitoring of the spatial-temporal changes of forest distribution. In this study, we aim to provide an overall retrospect of the forest changes (i.e., forest gain and forest loss) in northern China during 2007–2017, and to analyze the forest changes in different precipitation zones. We first generated annual forest maps at 30 m spatial resolution during 2007–2017 in northern China through integrating Landsat and PALSAR/PALSAR-2 data. We found the PALSAR/Landsat-based forest maps outperform other four existing products (i.e., JAXA F/NF, FROM-GLC, GlobeLand30, and NLCD-China) from either PALSAR or Landsat data, with a higher overall accuracy 96% ± 1%. The spatial-temporal analyses of forests showed a substantial forest expansion from 316,898 ± 34,537 km2 in 2007 to 384,568 ± 35,855 km2 in 2017 in the central and eastern areas. We found a higher forest loss rate (i.e., 35%) in the precipitation zones with the annual mean precipitation less than 400 mm (i.e., the arid and semi-arid areas) comparing to that (i.e., 25%) in the zones with more than 400 mm (i.e., the humid areas), which suggests the lower surviving rate in the drylands. This study provides satellite-based evidence for the forest changes in different precipitation zones, and suggests that the likely impacts of precipitation on afforestation effectiveness should be considered in future implementation of ecological restoration projects like TNSFP.

The authors would like to change the authors’ affiliation in the recent published paper [...]

Human land use intensity affects the surface energy balance by changing the biogeophysical parameters. This study used Moderate Resolution Imaging Spectroradiometer remote sensing data and surface energy balance algorithms to quantify changes in surface energy budgets corresponding to changes in land use in Beijing from 2000 to 2015. Land use was reclassified by considering land use intensity. The difference in the latent heat flux (LE) and net radiation (Rn) (LE−Rn) expressed the warming or cooling effect. The results showed that: (i) The increasing trend of net longwave radiation in Beijing offset the decreasing trend of net shortwave radiation. The Rn changed slightly, while the LE and LE−Rn showed a significant increase of 0.55 and 0.56 W/(m²∙year), respectively. The findings indicated that considering only radiative forcing, or even Rn, was not enough to measure the impacts of land use change on the energy budget. (ii) The order of Rn, LE, and LE−Rn values from high to low were natural and seminatural areas, cropland, mixed pixel areas, urban expansion areas, and old urban areas. Compared with natural and seminatural areas, the changing LE−Rn trend in the other four land use types decreased with the increase in human impact intensity, indicating that human activities weakened the positive change trend of LE−Rn and increased the warming effect. (iii) Although the temporal trend of LE increased in Beijing from 2000 to 2015, the effect of Rn on LE−Rn was greater than that of LE, especially in the four land use types affected by human activities. The results for surface temperature in various land use types confirmed this point. This study highlights the energy budget differences of various land use types affected by human activities. It makes an important contribution to understanding the urban heat island effect from a biogeophysical perspective.

The impact of human activities on vegetation has been the focus of much research, but the impact on radiation energy through surface albedo associated with vegetation greenness and length of the growth season is still not well documented. Based on the land cover data for the years 2000 and 2015, this study first divided the land cover change in Beijing from 2000 to 2015 into five types according to the impact of human activities and vegetation resilience, namely, old urban areas (OU), urban expansion areas (UE), cropland (CP), mixed pixel areas (MP, which means the land covers other than urban expansion which had changed from 2000 to 2015), and the residual vegetation cover areas (pure pixels (PP), dominated by natural and seminatural vegetation, such as grassland, forest, and wetland). Then, we calculated the direct radiative forcing from the albedo change from 2000 to 2015 and analyzed the effect of vegetation on the albedo under different land cover types based on multi-resource Moderate Resolution Imaging Spectroradiometer (MODIS) products of vegetation, albedo, and solar radiation. The results showed that the most typical changes in land cover were from urban expansion. By comparing the PP with the four human-affected land cover types (OU, UE, MP, and CP), we confirmed that the radiative forcing increment between 2001−2003 and 2013−2015 in PP (0.01 W/m2) was much smaller than that in the four human-affected land cover types (the mean increment was 0.92 W/m2). This study highlights that human activities affected vegetation growth. This, in turn, brought changes in the albedo, thereby enhancing radiative forcing in Beijing during 2000−2015.

This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS employed a bottom-up, two-step modeling strategy beginning with a spatially and temporally biased sample: project datasets collected and contributed by US Forest Service (USFS) and other forestry stakeholders in 29 different project areas in the northwestern USA. Plot-level AGB estimates collected in the project areas served as the response variable for predicting AGB primarily from lidar metrics of canopy height and density (R ^2 = 0.8, RMSE = 115 Mg ha ^−1 , Bias = 2 Mg ha ^−1 ). This landscape model was used to map AGB estimates at 30 m resolution where lidar data were available. A stratified random sample of AGB pixels from these landscape-level AGB maps then served as training data for predicting AGB regionally from Landsat image time series variables processed through LandTrendr. In addition, climate metrics calculated from downscaled 30 year climate normals were considered as predictors in both models, as were topographic metrics calculated from elevation data; these environmental predictors allowed AGB estimation over the full range of observations with the regional model (R ^2 = 0.8, RMSE = 152 Mg ha ^−1 , Bias = 9 Mg ha ^−1 ), including higher AGB values (>400 Mg ha ^−1 ) where spectral predictors alone saturate. For both the landscape and regional models, the machine-learning algorithm Random Forests (RF) was consistently applied to select predictor variables and estimate AGB. We then calibrated the regional AGB maps using field plot data systematically collected without bias by the national Forest Inventory and Analysis (FIA) Program. We found both our project landscape and regional, annual AGB estimates to be unbiased with respect to FIA estimates (Biases of 1% and 0.7%, respectively) and conclude that they are well suited to inform forest management and planning decisions by our contributing stakeholders. Social media abstract Lidar-based biomass estimates can be upscaled with Landsat data to regionally unbiased annual maps.