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The carbon sinks of North American boreal forests have been threatened by global warming and forest disturbances in recent decades, but knowledge about the carbon balance of these forests in recent years remains unknown. We tracked annual aboveground carbon (AGC) changes from 2016 to 2021 across the forest regions of NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) core study domain, using Vegetation Optical Depth derived from low-frequency passive microwave observations. The results showed that these forests showed a net AGC increase of + 28.49 Tg C/yr during the study period, with total AGC gains of + 219.34 Tg C/yr counteracting total AGC losses of −190.86 Tg C/yr. Forest degradation (-162.21 Tg C/yr), defined as a reduction in the capacity of forest to provide goods and services, contributes 5 times more to the total AGC loss than forest cover loss (-28.65 Tg C/yr), defined as the complete removal of tree cover. This indicates that degradation has dominated AGC loss in the region.

Over the past 4 decades, Southwest China has the fast vegetation growth and aboveground biomass carbon (AGC) accumulation, largely attributed to the active implementation of ecological projects. However, Southwest China has been threatened by frequent extreme drought events recently, potentially countering the expected large AGC increase caused by the ecological projects. Here, we used the L-band vegetation optical depth to quantify the AGC dynamics over Southwest China during the period 2013-2021. Our results showed a net AGC sink of 0.064 [0.057, 0.077] Pg C year−1 (the range represents the maximum and minimum AGC values), suggesting that Southwest China acted as an AGC sink over the study period. Note that the AGC loss of 0.113 [0.101, 0.136] Pg C year−1 was found during 2013-2014, which could mainly be attributed to the negative influence of extreme droughts on AGC changes in Southwest China, particularly in the Yunnan province. For each land use type (i.e., dense forests, persistent forests, nonforests, afforestation, and forestry), the largest AGC stock increase of 0.032 [0.028, 0.036] Pg C year−1 was found in nonforests, owing to their widespread land cover rate over Southwest China. For AGC density (i.e., AGC per unit area), the afforestation areas showed the largest AGC density increase of 0.808 [0.724, 0.985] Mg C ha−1 year−1, reflecting the positive effect of afforestation on AGC increase. Moreover, the karst areas exhibited a higher increasing rate of AGC density than nonkarst areas, suggesting that the karst ecosystems have a high carbon sink capacity over Southwest China.

Abstract Field-measured spectra are critical for remote sensing physical modelling, retrieval of structural, biophysical, and biochemical parameters, and other practical applications. We present a library of field spectra, which includes (1) portable field spectroradiometer measurements of vegetation, soil, and snow in the full-wave band, (2) multi-angle spectra measurements of desert vegetation, chernozems, and snow with consideration of the anisotropic reflectance of land surface, (3) multi-scale spectra measurements of leaf and canopy of different vegetation cover surfaces, and (4) continuous reflectance spectra time-series data revealing vegetation growth dynamics of maize, rice, wheat, rape, grassland, and so on. To the best of our knowledge, this library is unique in simultaneously providing full-band, multi-angle, multi-scale spectral measurements of the main surface elements of China covering a large spatial extent over a 10-year period. Furthermore, the 101 by 101 satellite pixels of Landsat ETM/OLI and MODIS surface reflectance centered around the field site were extracted, providing a vital linkage between ground measurements and satellite observations. The code language used for this work is Matlab 2016a.

This study aims to examine the individual and combined effects of soil moisture (SM) and vapor pressure deficit (VPD) on ecosystem productivity in Southwest China. Utilizing the community land model (CLM) to simulate the regional soil moisture and vapor pressure deficit, we analyzed their impacts on ecosystem productivity through a data binning approach and employed sun-induced chlorophyll fluorescence yield (SIFyield) as a productivity indicator. Our findings highlight a significant coupling effect between SM and VPD, which diminishes with finer temporal data resolution. The data binning analysis indicates that VPD has a predominant influence on SIFyield across 70% of the study area, whereas SM is more influential in the remaining 30%. Notably, the correlation between SIFyield and SM, modulated by VPD, is stronger in forest and shrubland ecosystems, whereas in grasslands, the influence pattern is reversed, with VPD having a more significant impact. The study concludes that in Southwest China, ecosystem productivity is more significantly affected by VPD than by SM.

North American boreal forests are known to be an important carbon pool in boreal ecosystems, but have experienced extensive tree mortality and carbon loss due to multiple agents of stand-replacing disturbances in recent decades. However, the impacts of these stand-replacing disturbances on forest dynamics are still unknown. We used a recently developed remote-sensing based stand-replacing disturbance product, coupled with aboveground biomass (AGB), gross primary productivity (GPP) and leaf area index (LAI) datasets to estimate the impacts of stand-replacing disturbances (e.g., fires, logging and insect outbreaks) on the carbon balance of western North American boreal forests during 2000–2012. Our results showed that fire, logging and insect outbreaks resulted in AGB losses of 23.4, 16.6, and 4.7 Tg/yr, respectively. In the post-disturbance periods, AGB did not recover to its pre-disturbed levels in the 10th year, which is longer than the recovery time of GPP and LAI. Furthermore, the losses of AGB, GPP and LAI in fire events were the dominant factors for forest recovery after stand-replacing fire. Vapor Pressure Deficit (VPD), soil clay content, temperature and precipitation were the important factors for forest recovery after stand-replacing insect outbreaks and stand-replacing logging. When removing the impact of environmental factors, our results showed a smaller magnitude of AGB, GPP and LAI loss relative to the results including these factors, although similar recovery trajectories were observed among the two results. The results have important implications for understanding the effects of stand-replacing disturbances on the carbon dynamics of boreal forests, which is required to adopt effective forest management strategies after disturbance.

In recent years, carbon emissions have become a hot spot issue, and countries have made efforts to control the increasing rate of CO2 concentration. Prior studies have mainly focused on the national total carbon emissions, but per capita carbon emissions are still poorly known. Here, we used multiple economic development indices to investigate the dynamics of per capita carbon emissions. Additionally, we used the Mann–Kendall test to assess the directions and magnitudes of trends and to investigate abrupt changes in per capita carbon emissions. Our results showed the highest positive growth rate of 0.439 mts/yr in Oman, and the highest negative growth rate of −0.462 mts/yr in the United Arab Emirates. Hurst Index analysis showed that about 86% of countries will keep the current trends of carbon emissions if current mitigation measures remain unchanged. Furthermore, we analyzed the shift in the center of gravity for per capita carbon emissions and used the contribution decomposition method to identify the drivers for the shift, which changed direction in 2004. The main driver behind the westward shift in the gravity center before 2004 was the fact that carbon emissions grew more strongly in the west than in the east before 2004, while the driver for behind the eastward shift in the gravity center after 2004 was a combination of emission reductions in the west and emission increases in the east. Our results highlighted the importance of understanding that the per capita CO2 emissions are clearly defined within the context of global carbon neutrality, which can help policymakers set more reasonable targets with which to better achieve carbon neutrality goals.

Paris polyphylla var. yunnanensis is a well-known medicinal plant that is mainly distributed in Southwest China; however, its genetic diversity and biodiversity processes are poorly understood. In this study, the sequences of cpDNA trnL-trnF fragments of 15 wild populations and 17 cultivated populations of P. polyphylla var. yunnanensis were amplified, sequenced, and aligned to study the population genetics of this species. Genetic diversity was analyzed based on nucleotide diversity, haplotype diversity, Watterson diversity, population-level diversity, and species-level genetic diversity. Genetic structure and genetic differentiation were explored using haplotype distribution maps and genetic distance matrices. A total of 15 haplotypes were identified in the 32 populations of P. polyphylla var. yunnanensis. Five unique haplotypes were identified from the fourteen haplotypes of the cultivated populations, while only one unique haplotype was identified from the ten haplotypes of the wild populations. The haplotype richness and genetic diversity of the cultivated populations were higher than those of the wild populations (HT = 0.900 vs. 0.861). In addition, there were no statistically significant correlations between geographic distance and genetic distance in the cultivated populations (r = 0.16, p > 0.05), whereas there was a significant correlation between geographical distance and genetic structure in the wild populations (r = 0.32, p > 0.05), indicating that there was a geographical and genetic connection between the wild populations. There was only 2.5% genetic variation between the wild populations and cultivated populations, indicating no obvious genetic differentiation between the wild and cultivated populations. Overall, the genetic background of the cultivated populations was complex, and it was hypothesized that the unique haplotypes and higher diversity of the cultivated populations were caused by the mixed provenance of the cultivated populations.

Over the past 2 to 3 decades, Chinese forests are estimated to act as a large carbon sink, yet the magnitude and spatial patterns of this sink differ considerably among studies. Using 3 microwave (L- and X-band vegetation optical depth [VOD]) and 3 optical (normalized difference vegetation index, leaf area index, and tree cover) remote-sensing vegetation products, this study compared the estimated live woody aboveground biomass carbon (AGC) dynamics over China between 2013 and 2019. Our results showed that tree cover has the highest spatial consistency with 3 published AGC maps (mean correlation value R = 0.84), followed by L-VOD (R = 0.83), which outperform the other VODs. An AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to 2019. The performance of the AGC estimation model was good (root mean square error = 0.05 Pg C and R2 = 0.90 with a mean relative uncertainty of 9.8% at pixel scale [0.25°]). Results of the AGC estimation model showed that carbon uptake by the forests in China was about +0.17 Pg C year−1 from 2013 to 2019. At the regional level, provinces in southwest China including Guizhou (+22.35 Tg C year−1), Sichuan (+14.49 Tg C year−1), and Hunan (+11.42 Tg C year−1) provinces had the highest carbon sink rates during 2013 to 2019. Most of the carbon-sink regions have been afforested recently, implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.

A surface soil moisture (SM) condition at high spatiotemportal resolutions is required by regional Earth system applications. Here, we mapped daily 1-km SM in the Babao River Basin in the northwest of China during the summers from 2013 to 2015 using a random forest (RF) method by merging SM information retrieved from in situ measurements, optical/thermal remote sensing, and topographical indices. Relative importance analysis was used to determine the optimal predictors for estimating high-resolution SM. A specific RF model (RFVI+sup) was constructed using the optimal predictors including remote sensing albedo, apparent thermal inertia (ATI), normalized difference vegetation index, normalized difference infrared index 5, soil adjusted vegetation index, and topographical indices (aspect and elevation). The RFVI+sup also accounted for missing observations of the thermal index (e.g., ATI) over the mountainous regions. In the comparison between the SM estimates using the new RFVI+sup model and other RF models, the spatial coverage of available estimates increased from 14% to 64% over the study region, the correlation coefficient values were improved to 0.75, the unbiased root-mean-squared difference values decreased to 0.032 m3/m3. Thus, the proposed RF method provided accurate SM estimates with high spatiotemporal resolution over the mountainous regions, by merging multiresource datasets from in situ measurements, remotely-sensed, and topographical indices.

Drought is a meteorological phenomenon that negatively impacts agricultural production. In recent years, southwest China has frequently experienced agricultural droughts; these have significantly impacted the economy and the ecological environment. Although several studies have been conducted on agricultural droughts, few have examined the factors driving agricultural droughts from the perspective of water and energy balance. This study aimed to address this gap by utilizing the Standardized Soil Moisture Index (SSMI) and the Budyko model to investigate agricultural drought in southwest China. The study identified four areas in Southwest China with a high incidence of agricultural drought from 2000 to 2020. Yunnan and the Sichuan-Chongqing border regions experienced drought in 10% of the months during the study period, while Guangxi and Guizhou had around 8% of months with drought. The droughts in these regions exhibited distinct seasonal characteristics, with Yunnan experiencing significantly higher drought frequency than other periods from January to June, while Guizhou and other areas were prone to severe droughts in summer and autumn. The Budyko model is widely used as the mainstream international framework for studying regional water and energy balance. In this research, the Budyko model was applied to analyze the water and energy balance characteristics in several arid regions of southwest China using drought monitoring data. Results indicate that the water and energy balances in Yunnan and Sichuan-Chongqing are more moisture-constrained, whereas those in Guizhou and Guangxi are relatively stable, suggesting lower susceptibility to extreme droughts. Furthermore, during severe drought periods, evapotranspiration becomes a dominant component of the water cycle, while available water resources such as soil moisture decrease. After comparing the causes of drought and non-drought years, it was found that the average rainfall in southwest China is approximately 30% below normal during drought years, and the temperature is 1–2% higher than normal. These phenomena are most noticeable during the spring and winter months. Additionally, vegetation transpiration is about 10% greater than normal during dry years in Southwest China, and soil evaporation increases by about 5% during the summer and autumn months compared to normal conditions.

SM2RAIN is a well-established methodology for estimating precipitation from satellite or observed soil moisture and it has been applied as a complementary approach to conventional precipitation monitoring methods. However, satellite soil moisture retrievals are usually subject to various biases and limited number of retrievals (and therefore large intervals) in remote areas, such as the Tibetan Plateau (TP), and little is known about their potential impacts on precipitation estimation. This study seeks to quantify the uncertainties in Soil Moisture Active and Passive (SMAP) soil moisture estimated precipitation through the commonly used SM2RAIN by referring to in situ soil moisture observations from the central Tibetan Plateau soil moisture network. The estimated precipitation is evaluated against rain gauge observations. Additional attention is paid to different orbits of the SMAP retrievals. Results show that the original SM2RAIN algorithm tends to underestimate the precipitation amount in the central TP when using SMAP soil moisture retrievals as input. The retrieval accuracy and sampling interval of SMAP soil moisture from ascending (descending) orbits each count for 1.04 mm/5 d (−0.18 mm/5 d) and 1.67 mm/5 d (0.72 mm/5 d) of estimated precipitation uncertainties as represented by root mean square error. Besides, the descending product of SMAP with a relatively less sampling interval and higher retrieval accuracy outperforms the ascending one in estimating precipitation, and the combination of both two orbits does add value to the overall SM2RAIN estimation. This study is expected to provide guidance for future applications of SM2RAIN-derived precipitation. Meanwhile, more reliable SM2RAIN precipitation estimations are desired when using higher quality satellite soil moisture products with better retrieval accuracy and smaller intervals.

Evapotranspiration (ET) is a critical component of the water, carbon and energy cycles of the land surface. Remote sensing-based models provide the possibility of mapping ET from field to regional and even global scales. A key boundary condition of the Two-Source Energy Balance (TSEB) model for computing ET is land surface temperature (LST). The proposed modification to TSEB is to use a combination of near surface soil moisture (SM) derived by microwave and LST as dual boundary conditions along with a more physically-based transpiration formulation for computing ET. Both TSEB and TSEB-SM were applied to an irrigated agricultural area with surrounding semi-arid sparse natural vegetation. In general, TSEB-SM model performance when compared to measured fluxes was similar to the TSEB model. However, the TSEB-SM model computed more reliable estimates of LE under dry soil surface/low vegetation cover conditions, primarily in the arid and semiarid natural ecosystems surrounding the irrigated agricultural area. These comparisons indicate that TSEB-SM may provide more reliable flux estimation and ET partitioning over sparsely vegetated areas compared to the traditional formulations used in TSEB. With reliable microwave remote sensing of soil moisture, TSEB-SM has potential for ET monitoring in both natural and agricultural landscapes.

Vegetation gross primary production (GPP) in terrestrial ecosystems is a key element of the carbon cycle, and its estimation highly determines the accuracy of carbon budget assessments. Currently, several global datasets of vegetation production are available at coarse or moderate spatial resolutions, but globally they still have large uncertainties, which hindered the application of GPP, especially in strongly heterogeneous agriculture ecosystems and mountainous areas. Here, we used the Markov chain Monte Carlo (MCMC) approach with the footprints of FLUXNET data to optimize the parameters of the high resolution of Global LAnd Surface Satellite (Hi-GLASS) GPP algorithm (i.e., the revised EC-LUE model) using 30 m spatial resolution Landsat data as driving data. Then, we generated a new set of algorithm parameters for high resolution GPP estimates. We used the optimized parameters with integrating footprint to calculate Hi-GLASS GPP based on Landsat data and our results revealed that on average, Hi-GLASS GPP explained 76% of variance in tower GPP at the total of 78 sites across ten vegetation types. Moreover, compared with previous 500 m GPP product such as GLASS GPP and MODerate Resolution Imaging Spectroradiometer (MODIS) GPP, our optimized Hi-GLASS algorithm using Landsat data had large superiority in simulating GPP for wetlands, savannas, shrubland and C3, C4 cropland ecosystems, and had a slightly improvement for deciduous broadleaf forests and evergreen broadleaf forest ecosystem. Our study is an effort to optimize and quantify parameter uncertainty of Hi-GLASS algorithm using high spatial resolution (30 m) Landsat data and improve the high resolution GPP estimation for better understanding global ecosystem carbon dynamics and carbon-climate feedbacks.

Forest fires are one of the most severe natural disasters facing global ecosystems, as they have a significant impact on ecological security and social development. As remote sensing technology has developed, burned areas can now be quickly extracted to support fire monitoring and post-disaster recovery. This study focused on monitoring forest fires that occurred in Chongqing, China, in August 2022. The burned area was identified using various satellite images, including Sentinel-2, Landsat8, Environmental Mitigation II A (HJ2A), and Gaofen-6 (GF-6). The burned area was extracted using visual interpretation, differenced Normalized Difference Vegetation Index (dNDVI), and differenced Normalized Burnup Ratio (dNBR). The results showed that: (1) The results of the three monitoring methods were very consistent, with a coefficient of determination R2 > 0.96. (2) A threshold method based on the dNBR-extracted burned area was used to analyze fire severity, with moderate-severity fires making up the majority (58.05%) of the fires. (3) Different topographic factors had some influence on the severity of the forest fires. High elevation, steep slopes and the northwestern aspect had the largest percentage of burned area.

In recent years, the retrieval and validation of remotely-sensed leaf area index (LAI) products over complex land surfaces have received much attention due to the high-precision land surface model simulations and applications in global climate change. However, most of these related researches mainly focus on coarse resolution products. This is because few products have been specifically designed for solving the problems derived from complex land surfaces in mountain areas until now. MuSyQ LAI is a new product derived from Gaofen-1 (GF-1) satellite data. This product is characterized with a temporal resolution of 10 days and a spatial resolution of 16 m. As is well known, high-resolution products have less uncertainties because of the homogeneities of sub-pixel. Therefore, to evaluate the precision and uncertainty of MuSyQ LAI, an up-scaling strategy was employed here to validate MuSyQ LAI for three mountain regions in Southwest China. The validation strategy can be divided into three parts. First, a regression model was built by in situ LAI measured by LAI-2200 and the normalized difference vegetation index (NDVI) from unmanned aerial vehicle (UAV) images to obtain a 0.5 m resolution LAI map. Second, an up-scaled LAI map with a spatial resolution consistent with MuSyQ LAI was calculated by the pixel-averaging method from the UAV-based LAI map. Third, the MuSyQ LAI was validated by the up-scaled UAV-based LAI in pixel scale. Simultaneously, the sources of uncertainty were analyzed and compared from the view of data source, retrieval model, and scale effects. The results suggested that MuSyQ LAI in the study areas are significantly underestimated by 53.69% due to the complex terrain and heterogeneous land cover. There are three main reasons for the underestimation. The differences between GF-1 reflectance and UAV-based reflectance employed to estimate LAI are the largest factors for the validation results, even accounting for 61.47% of the total bias. Subsequently, the scale effects led to about 28.44% bias. Last but not least, the models employed to retrieve LAI contributed merely 10.09% uncertainties to the total bias. In conclusion, the accuracy of MuSyQ LAI still has a large space to be improved from the view of reflectance over complex terrain. This study is quite important for applications of MuSyQ LAI products and also provides a reference for the improvement and application of other high-resolution remotely sensed LAI products.

Eddy-covariance (EC) measurements are widely used to optimize the terrestrial vegetation gross primary productivity (GPP) model because they provide standardized and high-quality flux data within their footprint areas. However, the extent of flux data taken from a tower site within the EC footprint, represented by the satellite-based grid cell between Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS), and the performance of the model derived from the Normalized Difference Vegetation Index (NDVI) within the EC footprint at different spatial resolutions (e.g., Landsat and MODIS) remain unclear. Here, we first calculated the Landsat-footprint NDVI and MODIS-footprint NDVI and assessed their spatial representativeness at 78 FLUXNET sites at 30 m and 500 m scale, respectively. We then optimized the parameters of the revised Eddy Covariance-Light Use Efficiency (EC-LUE) model using NDVI within the EC-tower footprints that were calculated from the Landsat and MODIS sensor. Finally, we evaluated the performance of the optimized model at 30 m and 500 m scale. Our results showed that matching Landsat data with the flux tower footprint was able to improve the performance of the revised EC-LUE model by 18% for savannas, 14% for croplands, 9% for wetlands. The outperformance of the Landsat-footprint NDVI in driving model relied on the spatial heterogeneity of the flux sites. Our study assessed the advantages of remote sensing data with high spatial resolution in simulating GPP, especially for areas with high heterogeneity of landscapes. This could facilitate a more accurate estimation of global ecosystem carbon sink and a better understanding of plant productivity and carbon climate feedbacks.

Multi-energy complementary heating (MECH) is the most promising and potential heating technology of the future. However, owing to the increase in energy types, the system is complex, and the operation procedure cumbersome. In addition, due to the uncertainty of climate conditions, it is difficult to develop a general control strategy suitable for all heating systems. In order to study the optimal control strategy of MECH systems with solar, biomass, and electric energy as three heat sources in cold regions, the system heating was tested over different periods during the entire heating season for rural residential buildings in cold regions, and the operating performance of the system was evaluated. The MECH experiment, based on the optimal control strategy, was compared with other different heating systems, and the economic and environmental benefits of the system were further evaluated. The results showed that, compared with room temperature control strategy (RS-CON), the control strategy of the heat storage tank (HST-CON) with sufficient solar energy (SE) can afford a higher solar fraction by 10%, more HST heating hours, and lower operating costs and CO2 emissions. During heating operation, Mode 3 [biomass boiler (BB) + heat storage tank (HST) heating] was the best heating mode. At the end of the heating period, Mode 5 (HST heating) met the indoor heat demand and had a significant energy-saving effect. From the perspective of the whole heating season, HST-CON heating operation can generate lower energy consumption costs and achieve almost zero CO2 emissions. This study is of great significance because it provides an engineering reference for the rational the utilization of MECH systems in cold regions.

Land surface temperature (LST) is an essential parameter in land–atmosphere interaction. However, it is still poorly understood about the effects of seasonal LST on the interannual LST change and the dominant driving force for the variation in LST. In this study, trends of time-series LST were analyzed by using both linear and nonlinear methods. Two indices were developed to evaluate the effects of seasonal LST trends on interannual LST change. The main driving factor of LST was identified based on each pixel. Two turning points were founded in 2007 and 2011 during the study period of 2003–2019. A significant cooling trend of LST appeared from 2007 to 2011/2012 with the rates of −0.2237 K/year (daytime) and −0.1239 K/year (nighttime). LST increased in almost all seasons except the daytime in autumn. The warming effect in spring and winter contributed 69.43% to interannual warming of daytime LST, and accounted for 59.02% of interannual warming of nighttime LST. In most regions of China, air temperature and vegetation were the dominant factors influencing the change of LST. The current research improved our understanding of changes of LST and the results can serve for mitigating and adapting to climate change.

Vegetation optical depth (VOD), as a microwave-based estimate of vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. However, current global operational VOD products have a coarse spatial resolution (~25 km), which limits their use for agriculture management and vegetation dynamics monitoring at regional scales (1–5 km). This study aims to retrieve high-resolution VOD from the C-band Sentinel-1 backscatter data over a grassland of the Heihe River Basin in northwestern China. The proposed approach used an analytical solution of a simplified Water Cloud Model (WCM), constrained by given soil moisture estimates, to invert VOD over grassland with 1 km spatial resolution during the 2018–2020 period. Our results showed that the VOD estimates exhibited large spatial variability and strong seasonal variations. Furthermore, the dynamics of VOD estimates agreed well with optical vegetation indices, i.e., the mean temporal correlations with normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) were 0.76, 0.75, and 0.75, respectively, suggesting that the VOD retrievals could precisely capture the dynamics of grassland.

Land surface temperature (LST) is a key parameter in the study of surface energy balance and climate change from local through to global scales. Vegetation has inevitably influenced the LST by changing the surface properties. However, the thermal environment pattern in the Yangtze River Basin (YRB) still remains unclear after the implementation of large-scale ecological restoration projects. In this study, the temporal and spatial variation characteristics of LST were analyzed based on the Theil–Sen estimator, Mann–Kendall trend analysis and Hurst exponent from 2003 to 2021. The relationships between vegetation and LST were further revealed by using correlation analysis and trajectory-based analysis. The results showed that the interannual LST was in a state of fluctuation and rise, and the increasing rate at night time (0.035 °C·yr−1) was faster than that at day time (0.007 °C·yr−1). An obvious cooling trend could be identified from 2007 to 2012, followed by a rapid warming. Seasonally, the warming speed was the fastest in summer and the slowest in autumn. Additionally, it was found that autumn LST had a downward trend of 0.073 °C·yr−1 after 2015. Spatially, the Yangtze River Delta, Hubei province, and central Sichuan province had a significant warming trend in all seasons, except autumn. The northern Guizhou province and Chongqing showed a remarkable cooling trend only in autumn. The Hurst exponent results indicated that the spring LST change was more consistent than the other three seasons. It was found by studying the effect of land cover types on LST changes that sparse vegetation had a more significant effect than dense vegetation. Vegetation greening contributed 0.0187 °C·yr−1 to the increase in LST in winter, which was spatially concentrated in the central region of the YRB. For the other three seasons, vegetation greening slowed the LST increase, and the degree of the effect decreased sequentially in autumn, summer, spring and winter. These results improve the understanding of past and future variations in LST and highlight the importance of vegetation for temperature change mitigation.

Global environmental changes have been dramatic recently, exerting substantial effects on the structures and functions of terrestrial ecosystems, especially for the ecologically-fragile karst regions. Southwest China is one of the largest karst continuum belts around the world, which also contributes about 1/3 of terrestrial carbon sequestration to China. Therefore, a deep understanding of the long-term changes of vegetation across Southwest China over the past decades is critical. Relying on the long time series of Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies normalized difference vegetation index (GIMMS NDVI3g) data set, this study examined the spatial and temporal patterns of vegetation conditions in Southwest China from 1982 to 2015, as well as their response to the environmental factors including temperature, precipitation and downward shortwave radiation. Multi-year mean NDVI showed that except the northwestern region, the NDVI of Southwest China was large, ranging from 0.5 to 0.8. Meanwhile, nearly 43.7% of the area experienced significant improvements in NDVI, whereas only 3.47% of the area exhibited significant decreases in NDVI. Interestingly, the NDVI in karst area increased more quickly with 1.035 × 10−3/a in comparison with that in the non-karst area with about 0.929 × 10−3/a. Further analysis revealed that temperature is the dominant environmental factor controlling the interannual changes in NDVI, accounting for 48.19% of the area, followed by radiation (3.71%) and precipitation (3.09%), respectively.

Drought can cause severe agricultural economic losses and hinder social and economic development. To manage drought, the process of drought events needs to be described with the help of an effective drought indicator. As a comprehensive variable, soil moisture is an essential indicator for describing agricultural drought. In this work, the extreme drought events in southwest China were analysed by the Global Land Data Assimilation System (GLDAS) root zone soil moisture data set. To define the drought quantitatively, a Standardized Soil Moisture Drought Index (SSMI) was calculated using the soil moisture data, then used to get the duration, frequency, and severity of drought events in southwest China. The results showed that the frequency and intensity of drought in southwest China had an apparent upward trend before 2014 and an apparent downward trend since 2014. Moreover, there are apparent differences in the frequency and intensity of drought in various regions of southwest China. Yunnan Province is prone to spring drought events. Guangxi Province and Guizhou Province are prone to spring, autumn and winter droughts, and the intensity of autumn and winter droughts is significantly higher than that of spring droughts. The Sichuan-Chongqing border area is prone to summer drought. We found that the monthly variation of soil moisture in different provinces in southwest China is consistent, but the seasonal variation of drought is different. Meanwhile, the performance of the SSMI was compared to the commonly used drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI). The results showed that the SSMI is more sensitive to drought than both SPEI and PDSI in southwest China. The results also demonstrate that GLDAS soil moisture data can be used to study drought at a small regional scale.

An increase in the frequency and severity of droughts associated with global warming has resulted in deleterious impacts on forest productivity in Southwest China. Despite attempts to explore the response of vegetation to drought, less is known about forest’s resilience in response to drought in Southwest China. Here, the reduced resilience of the forest was found based on remotely sensed optical and microwave vegetation products. The spatial distribution and temporal variation of resilience-reduced forest were assessed using the standardized precipitation evapotranspiration index (SPEI) and vegetation optical depth (VOD). Our findings showed that 40–50% of the forest appeared to have abnormally low resilience approximately 6 months after the severe drought. The spatial distributions of abnormally low resilience had a good agreement with the regions affected by the 2009–2011 drought events. In particular, our results indicated that areas of afforestation were more susceptible to drought than natural forest, maybe due to the different water uptake strategy of the diverse root systems. Our findings highlight the vulnerability of afforestation areas to climate change, and recommend giving more attention to soil water availability.

Lake Qinghai has shrunk and then expanded over the past few decades. Quantifying the contributions of climate change and human activities to lake variation is important for water resource management and adaptation to climate change. In this study, we calculated the water volume change of Lake Qinghai, analyzed the climate and land use changes in Lake Qinghai catchment, and distinguished the contributions of climate change and local human activities to water volume change. The results showed that lake water volume decreased by 9.48 km3 from 1975 to 2004 and increased by 15.18 km3 from 2005 to 2020. The climate in Lake Qinghai catchment is becoming warmer and more pluvial, and the changes in land use have been minimal. Based on the Soil and Water Assessment Tool (SWAT), land use change, climate change and interaction effect of them contributed to 7.46%, 93.13% and −0.59%, respectively, on the variation in surface runoff into the lake. From the perspective of the water balance, we calculated the proportion of each component flowing into and out of the lake and found that the contribution of climate change to lake water volume change was 97.55%, while the local human activities contribution was only 2.45%. Thus, climate change had the dominant impact on water volume change in Lake Qinghai.

Fritillaria taipaiensis is a valuable traditional Chinese medicinal plant affected by germplasm degradation during cultivation. To find a suitable solution for this problem, F. taipaiensis was inoculated with exogenous arbuscular mycorrhiza (AM) fungi, potassium-solubilizing bacteria, and phosphorus-solubilizing bacteria in pot experiments. The effects of inoculation on the AM colonization, photosynthetic pigment, and superoxide dismutase contents, rhizospheric enzyme activities, alkaloid, and nucleotide content of F. taipaiensis were studied. The results showed that the inoculation increased mycorrhizal infection intensity of F. taipaiensis. Compared to the control group, the photosynthetic pigment contents in the leaf of the inoculation groups were increased; the content of soluble protein, the activities of the superoxide dismutase, catalase, and peroxidase in leaves were all significantly increased, and the content of malondialdehyde was decreased. The microbial inoculum also increased the content of active ingredients in F. taipaiensis bulbs and increased the enzyme activities, total microorganism number, bacteria/fungi, and actinomycetes/fungi ratios in the rhizosphere. To sum up, microbial fertilizer can significantly improve the rhizospheric environment and medicinal quality of F. taipaiensis. This research provides a theoretical basis and experimental evidence for the sustainable development of an F. taipaiensis industry.

The timely and accurate mapping of paddy rice is important to ensure food security and to protect the environment for sustainable development. Existing paddy rice mapping methods are often remote sensing technologies based on optical images. However, the availability of high-quality remotely sensed paddy rice growing area data is limited due to frequent cloud cover and rain over the southwest China. In order to overcome these limitations, we propose a paddy rice field mapping method by combining a spatiotemporal fusion algorithm and a phenology-based algorithm. First, a modified neighborhood similar pixel interpolator (MNSPI) time series approach was used to remove clouds on Sentinel-2 and Landsat 8 OLI images in 2020. A flexible spatiotemporal data fusion (FSDAF) model was used to fuse Sentinel-2 data and MODIS data to obtain multi-temporal Sentinel-2 images. Then, the fused remote sensing data were used to construct fusion time series data to produce time series vegetation indices (NDVI\LSWI) having a high spatiotemporal resolution (10 m and ≤16 days). On this basis, the unique physical characteristics of paddy rice during the transplanting period and other auxiliary data were combined to map paddy rice in Yongchuan District, Chongqing, China. Our results were validated by field survey data and showed a high accuracy of the proposed method indicated by an overall accuracy of 93% and the Kappa coefficient of 0.85. The paddy rice planting area map was also consistent with the official data of the third national land survey; at the town level, the correlation between official survey data and paddy rice area was 92.5%. The results show that this method can effectively map paddy rice fields in a cloudy and rainy area.

Land surface temperature (LST) is a crucial input parameter in the study of land surface water and energy budgets at local and global scales. Because of cloud obstruction, there are many gaps in thermal infrared remote sensing LST products. To fill these gaps, an improved LST reconstruction method for cloud-covered pixels was proposed by building a linking model for the moderate resolution imaging spectroradiometer (MODIS) LST with other surface variables with a random forest regression method. The accumulated solar radiation from sunrise to satellite overpass collected from the surface solar irradiance product of the Feng Yun-4A geostationary satellite was used to represent the impact of cloud cover on LST. With the proposed method, time-series gap-free LST products were generated for Chongqing City as an example. The visual assessment indicated that the reconstructed gap-free LST images can sufficiently capture the LST spatial pattern associated with surface topography and land cover conditions. Additionally, the validation with in situ observations revealed that the reconstructed cloud-covered LSTs have similar performance as the LSTs on clear-sky days, with the correlation coefficients of 0.92 and 0.89, respectively. The unbiased root mean squared error was 2.63 K. In general, the validation work confirmed the good performance of this approach and its good potential for regional application.