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Abstract Cities are main carbon emissions generators. Land use changes can not only affect terrestrial ecosystems carbon, but also anthropogenic carbon emissions. However, carbon monitoring at a spatial level is still coarse, and low-carbon land use encounters the challenge of being unable to adjust at the patch scale. This study addresses these limitations by using land-use data and various auxiliary data to explore new methods. The approach involves developing a high-resolution carbon monitoring model and investigating a patch-scale low-carbon land use model by integrating high carbon sink/source images with the Future Land Use Simulation model. Between 2000 and 2020, the results reveal an increasing trend in both carbon emissions and carbon sinks in the Shangyu district. Carbon sinks can only offset approximately 3% of the total carbon emissions. Spatially, the north exhibits net carbon emissions, while the southern region functions more as a carbon sink. A total of 14.5% of the total land area witnessed a change in land-use type, with the transfer-out of cropland constituting the largest area at 96.44 km2, accounting for 50% of the total transferred area. Land-use transfer resulted in an annual increase of 77.72 × 104 t in carbon emissions between 2000 and 2020. Through land-use structure optimisation, carbon emissions are projected to increase by only 7154 t C/year from 2000 to 2030, significantly lower than the amount between 2000 and 2020. Further low-carbon land optimisation at the patch scale can enhance the carbon sink by 129.59 t C/year. The conclusion drawn is that there is considerable potential to reduce carbon emissions through land use control. The new methods developed in our study can effectively contribute to high-resolution carbon monitoring in spatial contexts and support low-carbon land use, promoting the application of low-carbon land use from theory to practice. This will provide technological guidance for land use planning, city planning, and so forth.

Abstract Amidst the escalating impacts of global warming, the occurrence and severity of compound extreme weather events have risen significantly, presenting substantial threats to both lives and property. Existing response strategies predominantly focus on individual events, often overlooking the cumulative effects rising from their inherent complexity. To address this critical gap, we conducted a thorough examination of sequential extreme precipitation–heatwave compound events (SEPHCE) in China from 1975 to 2020, utilizing data from 1929 meteorological stations. Our investigation revealed a consistent rise in the frequency and duration of SEPHCE, with a particularly notable surge since 1993. Furthermore, shorter interval events disproportionately affected the regions of southwestern and southeast coastal China. Furthermore, SEPHCE onset times exhibited advancement, and the endings were delayed, thereby intensifying the overall trend. These findings underscore the pressing need to prioritize effective planning and adaptation strategies to mitigate the impact of these compound event, while also addressing the potential exacerbation of inequality resulting from climate change.

Abstract The future state of drought in China under climate change remains uncertain. This study investigates drought events, focusing on the region of China, using simulations from five global climate models (GCMs) under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5) participating in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). The daily Standardized Precipitation Evapotranspiration Index (SPEI) is employed to analyze drought severity, duration, and frequency over three future periods. Evaluation of the GCMs’ simulations against observational data indicates their effectiveness in capturing historical climatic change across China. The rapid increase in CO2 concentration under high-emission scenarios in the mid- and late-future century (2040–2070 and 2071–2100) substantially influences vegetation behavior via regulation on leaf stomata and canopy structure. This regulation decelerates the increase in potential evapotranspiration, thereby mitigating the sharp rise in future drought occurrences in China. These findings offer valuable insights for policymakers and stakeholders to develop strategies and measures for mitigating and adapting to future drought conditions in China.

A timely and accurately predicted grain yield can ensure regional and global food security. The scientific community is gradually advancing the prediction of regional-scale maize yield. However, the combination of various datasets while predicting the regional-scale maize yield using simple and accurate methods is still relatively rare. Here, we have used multi-source datasets (climate dataset, satellite dataset, and soil dataset), lasso algorithm, and machine learning methods (random forest, support vector, extreme gradient boosting, BP neural network, long short-term memory network, and K-nearest neighbor regression) to predict China’s county-level maize yield. The use of multi-sourced datasets advanced the predicting accuracy of maize yield significantly compared to the single-sourced dataset. We found that the machine learning methods were superior to the lasso algorithm, while random forest, extreme gradient boosting, and support vector machine represented the most preferable methods for maize yield prediction in China (R2 ≥ 0.75, RMSE = 824–875 kg/ha, MAE = 626–651 kg/ha). The climate dataset contributed more to the prediction of maize yield, while the satellite dataset contributed to tracking the maize growth process. However, the methods’ accuracies and the dominant variables affecting maize growth varied with agricultural regions across different geographic locations. Our research serves as an important effort to examine the feasibility of multi-source datasets and machine learning techniques for regional-scale maize yield prediction. In addition, the methodology we have proposed here provides guidance for reliable yield prediction of different crops.

Climate warming hampers grassland growth, particularly in dryland regions. To preserve robust grassland growth and ensure the resilience of grassland in these arid areas, a comprehensive understanding of the interactions between vegetation and climate is imperative. However, existing studies often analyze climate–vegetation interactions using concurrent vegetation indices and meteorological data, neglecting time-lagged influences from various determinants. To address this void, we employed the random forest machine learning method to predict the grassland NDVI (Normalized Difference Vegetation Index) in Asian drylands (including five central Asia countries, the Republic of Mongolia, and Parts of China) from 2001 to 2020, incorporating time-lag influences. We evaluated the prediction model’s performance using three indexes, namely the coefficient of determination (R2), root-mean-square error (RMSE), and Mean Absolute Error (MAE). The results underscore the superiority of the model incorporating time-lag influences, demonstrating its enhanced capability to capture the grassland NDVI in Asian drylands (R2 ≥ 0.915, RMSE ≤ 0.033, MAE ≤ 0.019). Conversely, the model without time-lag influences exhibited relatively poor performance, notably inferior to the time-lag-inclusive model. The latter result aligns closely with remote sensing observations and more accurately reproduces the spatial distributions of the grassland NDVI in Asian drylands. Over the study period, the grassland NDVI in Asian drylands exhibited a weak decreasing trend, primarily concentrated in the western region. Notably, key factors influencing the grassland NDVI included the average grassland NDVI in the previous month, total precipitation in the current month, and average soil moisture in the previous month. This study not only pioneers a novel approach to predicting grassland growth but also contributes valuable insights for formulating sustainable strategies to preserve the integrity of grassland ecosystems.

Abstract Changes in winter cloudy days (CDs) exert substantial impacts on agricultural production, public transport, and the solar photovoltaic (PV) power industry. However, the physical mechanism behind the variability of winter CDs over China remains largely unclear, making it challenging to predict the future dynamics of winter CDs. In this study, we explored the spatiotemporal features of winter CDs over China from 1961 to 2013 and identified two independent formation mechanisms associated with its leading mode. Then, we projected the future changes (2021–2099) in winter CDs under global warming constrained by the identified formation mechanisms. Results revealed that: (a) The leading mode of winter CDs from 1961 to 2013 presents a homogeneous pattern. The positive winter CDs anomaly is connected to the anomalous southerlies related to the Eurasian Rossby wave train triggered by the Barents Sea thermal forcing, as well as the Asia‐Pacific zonal geopotential height gradient induced by the convection over the Maritime Continent; (b) Selected state‐of‐the‐art climate models project a notable decrease in winter CDs in the Tibetan Plateau and southern China regions, and almost unchanged winter CDs over northern China, under global warming; (c) In contrast to present‐day mechanisms, in the future, the shift of the Eurasian mean flow will terminate the role of the Barents Sea thermal forcing. Instead, the enhanced convection over the Maritime Continent emerges as the primary contributor to the decline in winter CDs. Our findings provide valuable insights for policymakers in the solar PV industry, particularly in relation to the challenges faced under global warming.

Improved agricultural production systems, together with increased grain yield, are essential to feed the growing global population in the 21st century. Global gridded crop models (GGCMs) have been extensively used to assess crop production and yield simulation on a large geographical scale. However, GGCMs are less effective when they are used on a finer scale, significantly limiting the precision in capturing the yearly maize yield. To address this issue, we propose a relatively more advanced approach that downsizes GGCMs by combining machine learning and crop modeling to enhance the accuracy of maize yield simulations on a regional scale. In this study, we combined the random forest algorithm with multiple data sources, trained the algorithm on low-resolution maize yield simulations from GGCMs, and applied it to a finer spatial resolution on a regional scale in China. We evaluated the performance of the eight GGCMs by utilizing a total of 1046 county-level maize yield data available over a 30-year period (1980–2010). Our findings reveal that the downscaled models created for maize yield simulations exhibited a remarkable level of accuracy (R2 ≥ 0.9, MAE < 0.5 t/ha, RMSE < 0.75 t/ha). The original GGCMs performed poorly in simulating county-level maize yields in China, and the improved GGCMs in our study captured an additional 17% variability in the county-level maize yields in China. Additionally, by optimizing nitrogen management strategies, we identified an average maize yield gap at the county level in China ranging from 0.47 to 1.82 t/ha, with the south maize region exhibiting the highest yield gap. Our study demonstrates the high effectiveness of machine learning methods for the spatial downscaling of crop models, significantly improving GGCMs’ performance in county-level maize yield simulations.

Growth and senescence rates are critical ecological indicators of seasonality shifts of vegetation, with both sensitive to climate change. Here we investigated daily mean vegetation growth and senescence rates, and the major climate forcing across the Northern Hemisphere (>30°N) using satellite-derived normalized difference vegetation index (NDVI) and flux-based gross primary productivity (GPP) from 1982 through 2015. Both growth and senescence rates are higher at high latitudes than those at low latitudes, with spatially-averaged values increased by 1.0 × 10−4 and 0.7 × 10−4 NDVI-units·day−1 per degree latitude. These increases were greater in Eurasia than in North America. A parallel acceleration of growth (0.8 ×10−4 NDVI-units·day−1·decade−1) and senescence (0.6 ×10−4 NDVI-units·day−1·decade−1) rates was found for the 34-year study period. The warming-induced increases in vegetation peak growth (peak NDVI) contributed strongly to this parallel acceleration, while unequal advances or delays of three key phenological indicators (the start (SOS), peak (POS), and end (EOS) of the growing season) exerted influential effects on the rates. However, no single climatic factor during any period appeared responsible for the variations in growth and senescence rates. In areas with growth and senescence rates that are determined by peak growth, temperature and precipitation during the growth period accelerated both rates through elevating peak growth. On the other hand, in areas with growth rate determined by SOS, rising temperature before SOS decelerated the growth rate by advancing SOS. In areas with senescence rate determined by EOS, both temperature and radiation during the senescence period contributed to changes in senescence rate by influencing EOS. In sum, a central focus should be placed on the linkages among climate, phenology, and growth and senescence rates for quantifying vegetation seasonality and associated ecosystem function under the changing climate.

Oil and protein content and fatty acid composition are quality traits in peanut. Elucidating the genetic mechanisms underlying these traits may help researchers to obtain improved cultivars by molecular breeding. Whole-genome resequencing of a recombinant inbred population of 318 lines was performed to construct a high-density linkage map and identify QTL for peanut quality. The map, containing 4561 bin markers, covered 2032 cM with a mean marker density of 0.45 cM. A total of 110 QTL for oil and protein content, and fatty acid composition were mapped on the 18 peanut chromosomes. The QTL qA05.1 was detected in four environments and showed a major phenotypic effect on the contents of oil, protein, and six fatty acids. The genomic region spanned by qA05.1, corresponding to a physical interval of approximately 1.5 Mb, contains two SNPs polymorphic between the parents that could cause missense mutations. The two SNP sites were employed as KASP markers and validated using lines with extremely high and low oil contents. These sites may be useful in the marker-assisted breeding of peanut cultivars with high oil contents.

As an important factor affecting the edible quality of peanut kernels, sucrose content is a complex quantitative trait regulated by multiple factors. In this study, an F2 segregating population and a recombinant inbred line (RIL) population, derived from a cross between the high sucrose content variety Jihuatian 1 and the low sucrose content line PI478819, were used as materials to map a quantitative trait locus (QTL) associated with sucrose content in peanut kernels. Four QTLs were initially located on chromosomes A03 and A06 based on BSA-seq technology, and multiple kompetitive allele-specific PCR markers were developed based on single-nucleotide polymorphisms (SNPs) in the intervals. The markers were genotyped in the RIL population and finely mapped to a stable QTL, qSUCA06, located on chromosome A06 within a 0.29-Mb physical genomic interval (112367085–112662675 bp), which accounted for 31.95%–41.05% of the phenotypic variance explained. SNP and insertion/deletion annotations were performed on genes in the candidate interval, and having screened out those genes with mutations in exons, candidate genes were verified by qRT-PCR. The results revealed that Arahy.Y2LWD9 may be the main gene regulating sucrose content. The QTL identified in this study will not only contribute to marker-assisted breeding for improvement of peanut sucrose content but also paves the way for identifying gene function.

To achieve the ultimate goal of carbon neutralization by 2060 in China, increasing carbon sequestration has become more important. Based on satellite images, field observations and spatial econometric models, this study examined vegetation coverage and carbon sequestration changes during the period 2000–2020 in China’s eight forestry engineering projects area, analysed their correlation, and compared their response to climate change. The results show that there was a significant turning green trend in all regions. The NDVI with an increasing trend accounted for 68.5 % of the whole study area, which were widely distributed and especially significant in the south. The NDVI with a decreasing trend were more concentrated in the north. For NEP, 31.44 % of the total study area showed decreasing trend, which had a wide distribution and more concentrated in the south and the coastal regions of China. NEP under increasing trend were more obvious in the centre and north of China. In time series, 77.85 % of NDVI in the total study area positively correlated with NEP, with the average correlation coefficient of R = 0.51 (P > 0.05), and the higher positive correlations were more concentrated in the centre and north of China. For precipitation, 83.32 % and 65.31 % of the total area can promote the increase in NDVI and NEP, with the effect more obvious in the north. For temperature, about half of the total area showed a positive correlation, more obvious in the north, for both NDVI and NEP. Spatially, NDVI changes corresponded well with NEP for most vegetation types, for both precipitation and temperature. The few differences only appeared for broad-leaved forest and coniferous forest changes with average precipitation higher than 50 mm between 2000 and 2020. We draw the conclusion that turning green will increase carbon sequestration, the effect is more obvious in northern China.

In recent decades, the trade-off between urbanization and vegetation dynamics has broken the balance between human activities and social-economic dimensions. Our understanding towards the complex human–nature interactions, particularly the gradient of vegetation growth pattern across different city size, is still limited. Here, we selected 35 typical cities in China and classified them into five categories according to their resident population (e.g., megacities, megapolis, big cities, medium cities, and small cities). The spatial-temporal dynamics of vegetation growth for all 35 cities were inferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). We found that averaged NDVI for all cities slightly decreased during 2000 and 2020, at a rate of 1.6 × 10−4 per year. Most cities were characterized with relatively lower NDVI in urban areas than its surrounding area (determined by a series of buffer zones, i.e., 1–25 km outside of the city boundary). The percentage of greening pixels increased from urban area to the 25 km buffer zone at a rate of 4.7 × 10−4 per km. We noticed that negative impact of urbanization on vegetation growth reduced as the distance to urban area increased, with an exception for megacities (e.g., Shanghai, Beijing, and Shenzhen). In megacities and megapolis, greening pixels were more concentrated at core urban area, implying that the positive urbanization effect on vegetation growth is much more apparent. We argue that urbanization in China might facilitate vegetation growth to a certain extent, for which an appropriate urban planning such as purposeful selection of city sizes could be a scientific guidance while targeting the city’s sustainable development goals in future.

Global warming and human activities have intensified the duration, frequency, and extent of climatic extremes. The projected rise in global mean annual temperature of 1.5 °C/2 °C is thought to have severe impacts on the population exposed to droughts. Although these impacts on humans have been widely explored, the impacts associated with the cropland exposed to droughts have not been widely investigated. Here, we have examined the spatiotemporal pattern of China’s drought conditions and cropland exposure to droughts under global warming of 1.5 °C and 2 °C, along with the avoided impacts (as evaluated by the cropland exposure to droughts) when limiting the global warming to 1.5 °C instead of 2 °C. Results suggest that compared to the reference period (1995–2014), drought conditions will be alleviated when the projected rise in mean global temperature is limited to 1.5 °C rather than 2.0 °C. Although severe droughts tend to be mainly distributed in northwestern China, drought severities are increasing in southern China, especially in the southeastern region. In addition, the total cropland exposure to droughts across China exhibits an increasing trend in response to the 0.5 °C of additional global warming, especially in northwestern China and Huang−Huai−Hai region. If global warming could be limited to 1.5 °C, the avoided impact will exceed 30%, especially in northwestern China, southwestern China, and the Huang−Huai−Hai Plain. Furthermore, the rising cropland exposure to droughts under the 2 °C global warming is likely to be triggered by the rising frequencies of moderate and extreme droughts. Therefore, climate mitigation strategies are urgently needed to keep the global temperature rise below 1.5 °C, for the future sustainability of China’s cropland.

Soil moisture plays an essential role in the land-atmosphere interface. It has become necessary to develop quality large-scale soil moisture data from satellite observations for relevant applications in climate, hydrology, agriculture, etc. Specifically, microwave-based observations provide more consistent land surface records because they are unhindered by cloud conditions. The recent microwave radiometers onboard FY-3B, FY-3C and FY-3D satellites launched by China’s Meteorological Administration (CMA) extend the number of available microwave observations, covering late 2011 up until the present. These microwave observations have the potential to provide consistent global soil moisture records to date, filling the data gaps where soil moisture estimates are missing in the existing records. Along these lines, we studied the FY-3C to understand its added value due to its unique time of observation in a day (ascending: 22:15, descending: 10:15) absent from the existing satellite soil moisture records. Here, we used the triple collocation technique to optimize a benchmark retrieval model of land surface temperature (LST) tailored to the observation time of FY3C, by evaluating various soil moisture scenarios obtained with different bias-imposed LSTs from 2014 to 2016. The globally optimized LST was used as an input for the land parameter retrieval model (LPRM) algorithm to obtain optimized global soil moisture estimates. The obtained FY-3C soil moisture observations were evaluated with global in situ and reanalysis datasets relative to FY3B soil moisture products to understand their differences and consistencies. We found that the RMSEs of their anomalies were mostly concentrated between 0.05 and 0.15 m3 m−3, and correlation coefficients were between 0.4 and 0.7. The results showed that the FY-3C ascending data could better capture soil moisture dynamics than the FY-3B estimates. Both products were found to consistently complement the skill of each other over space and time globally. Finally, a linear combination approach that maximizes temporal correlations merged the ascending and descending soil moisture observations separately. The results indicated that superior soil moisture estimates are obtained from the combined product, which provides more reliable global soil moisture records both day and night. Therefore, this study aims to show that there is merit to the combined usage of the two FY-3 products, which will be extended to the FY-3D, to fill the gap in existing long-term global satellite soil moisture records.