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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.

Abstract Background Grassland ecosystems play an important role in the terrestrial carbon cycles through carbon emission by ecosystem respiration (R e ) and carbon uptake by plant photosynthesis (GPP). Surprisingly, given R e occupies a large component of annual carbon balance, rather less attention has been paid to developing the estimates of R e compared to GPP. Results Based on 11 flux sites over the diverse grassland ecosystems in northern China, this study examined the amounts of carbon released by R e as well as the dominant environmental controls across temperate meadow steppe, typical steppe, desert steppe and alpine meadow, respectively. Multi-year mean R e revealed relatively less CO2 emitted by the desert steppe in comparison with other grassland ecosystems. Meanwhile, C emissions of all grasslands were mainly controlled by the growing period. Correlation analysis revealed that apart from air and soil temperature, soil water content exerted a strong effect on the variability in R e , which implied the great potential to derive R e using relevant remote sensing data. Then, these field-measured R e data were up-scaled to large areas using time-series MODIS information and remote sensing-based piecewise regression models. These semi-empirical models appeared to work well with a small margin of error (R 2 and RMSE ranged from 0.45 to 0.88 and from 0.21 to 0.69 g C m−2 d−1, respectively). Conclusions Generally, the piecewise models from the growth period and dormant season performed better than model developed directly from the entire year. Moreover, the biases between annual mean R e observations and the remotely-derived products were usually within 20%. Finally, the regional R e emissions across northern China’s grasslands was approximately 100.66 Tg C in 2010, about 1/3 of carbon fixed from the MODIS GPP product. Specially, the desert steppe exhibited the highest ratio, followed by the temperate meadow steppe, typical steppe and alpine meadow. Therefore, this work provides a novel framework to accurately predict the spatio-temporal patterns of R e over large areas, which can greatly reduce the uncertainties in global carbon estimates and climate projections.

Abstract As the rapid development of portable and wearable devices, different electromagnetic interference (EMI) shielding materials with high efficiency have been desired to eliminate the resulting radiation pollution. However, limited EMI shielding materials are successfully used in practical applications, due to the heavy thickness and absence of sufficient strength or flexibility. Herein, an ultrathin and flexible carbon nanotubes/MXene/cellulose nanofibrils composite paper with gradient and sandwich structure is constructed for EMI shielding application via a facile alternating vacuum-assisted filtration process. The composite paper exhibits outstanding mechanical properties with a tensile strength of 97.9 ± 5.0 MPa and a fracture strain of 4.6 ± 0.2%. Particularly, the paper shows a high electrical conductivity of 2506.6 S m−1 and EMI shielding effectiveness (EMI SE) of 38.4 dB due to the sandwich structure in improving EMI SE, and the gradient structure on regulating the contributions from reflection and absorption. This strategy is of great significance in fabricating ultrathin and flexible composite paper for highly efficient EMI shielding performance and in broadening the practical applications of MXene-based composite materials.

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.

The steady laminar natural convective heat transfer for air has been compared numerically between cavities with rectangular and triangular structures. The equations of momentum, heat, and mass transfer were solved using the finite difference method, and central difference approximations that have second-order accuracy were used; furthermore, a good convergence was guaranteed by using the successive under-relaxation method. The Prandtl number is fixed at 0.71, while the Rayleigh number and the aspect ratio of the enclosures were changed in the range of 103–106 and varied over 1, 0.5, 0.2, and 0.1, respectively. The cavity was heated from one vertical wall and cooled from one side, while the horizontal walls were insulated. The influence of the Rayleigh number and the aspect ratio of the both kinds of cavities on the rate of heat transfer was investigated. Results were presented using flow patterns and isotherms plots; additionally, local and mean Nusselt numbers are also obtained for all cases. The obtained results convey that at a certain aspect ratio, an increment in Rayleigh numbers significantly augments the heat transfer, the intensity of natural convection, and average Nusselt number. Another important finding is that conduction is the dominated mechanism when the aspect ratio is small originally, and then, as the aspect ratio increases, the role of the convection boosts, and finally, the convection is overwhelmed by the convection. It can be concluded that for a given aspect ratio, only when the Rayleigh number is large enough can it induce a significant convection.

Abstract Extreme precipitation over drylands, especially deserts, has been observed, whereas the precipitation changes in Chinese deserts have been rarely studied. Here, we used a daily grid precipitation data set generated via weather station data (0.25° horizontal grid spacing) to investigate the spatial and temporal changes in extreme precipitation in Chinese deserts. Extreme precipitation based on the changes in the total precipitation (PRCPTOT) and the annual maximum daily precipitation (Rx1day) in the Chinese desert exhibited markedly increasing trends and presented a spatial distribution of wetting in the western deserts and drying in the eastern deserts. The increase in extreme precipitation could minimize wind erosion and intensify dune stabilization in the western Chinese deserts.

A global warming hiatus occurred during 1998 and 2012 but its effects on phenology are unclear. Here the authors examine the trends in spring and autumn phenology in the northern hemisphere and the effects of the warming hiatus and show that phenology change rate in the northern hemisphere slowed down during the warming hiatus.

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.

As the capital and one of the metropolises in China, Beijing has met with a number of serious so-called "urban diseases" in the process of rapid urbanization such as blind expansion of urban areas, explosion of population and the increase of urban heat island effect. To treat these "urban diseases" and make the metropolis develop healthful and sustainable in Beijing in the future, the spatial characteristics of metropolis developments in Beijing are explored in this paper. The urban built-up areas in Beijing are extracted using the DMSP-OLS nighttime light data from 1992 to 2013. The characteristics of the urban developments of Beijing are studied, including spatial and temporal scales of urban developments, urban barycenter of Beijing and its transfer trajectory, variations of urban spatial forms and the differences of urban internal developments. The results have shown that the built-up areas had been increasing and circling extending from the central urban areas to the outer spaces in the last 21 years. The built-up area had expanded by 878km2 in 1992-2013, and the built-up area in 2013 had expanded to three times comparing to that of 1992. The expanding area of the built-up area in the northeast is the largest. The expansion of the urban had mainly occurred in 1996-2007, and the expanded area had accounted for 92% of the total research period. During the whole research period, the urban barycenter of Beijing had moved 5000.71 meters towards Northeast 28° of its original place from Dongcheng District to Chaoyang District. The development level of each municipal district had been increasing year by year, and the development differences among the municipal districts had been gradually reduced; the spatial forms of Beijing had been alternately changed between extensive and intensive expansion. The results of this study can help to plan urban land use and people migration of Beijing.

Quantitative remote sensing product (QRSP) validation is a complex process to assess the accuracy and uncertainty independently using reference data with multiple land cover types and long-time series. A web-based system named as LAnd surface remote sensing Product VAlidation system (LAPVAS) is described in this paper, which is used to implement the QRSPs validation process automatically. The LAPAVS has two subsystems, the Validation Databases Subsystem and the Accuracy Evaluation Subsystem. Three functions have been implemented by the two subsystems for a comprehensive QRSP validation: (1) a standardized processing of reference data and storage of these data in validation databases; (2) a consistent and comprehensive validation procedure to assess the QRSPs’ accuracy and uncertainty; and (3) a visual process customization tool with which the users can register new validation data, host new reference data, and readjust the validation workflows for the QRSP accuracy assessment. In LAPVAS, more than 100 GB of reference data warehoused in validation databases for 13 types of QRSPs’ validation. One of the key QRSPs, land surface albedo, is selected as an example to illustrate the application of LAPVAS. It is demonstrated that the LAPVAS has a good performance in the land surface remote sensing product validation.

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.

The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale.

Research on land surface processes at the catchment scale has drawn much attention over the past few decades, and a number of watershed observatories have been established worldwide. The Heihe River Basin (HRB), which contains the second largest inland river in China, is an ideal natural field experimental area for investigation of land surface processes involving diverse landscapes and the coexistence of cold and arid regions. The Heihe Integrated Observatory Network was established in 2007. For long-term observations, a hydrometeorological observatory, ecohydrological wireless sensor network, and satellite remote sensing are now in operation. In 2012, a multiscale observation experiment on evapotranspiration over heterogeneous land surfaces was conducted in the midstream region of the HRB, which included a flux observation matrix, wireless sensor network, airborne remote sensing, and synchronized ground measurements. Under an open data policy, the datasets have been publicly released following careful data processing and quality control. The outcomes highlight the integrated research on land surface processes in the HRB and include observed trends, scaling methods, high spatiotemporal resolution remote sensing products, and model–data integration in the HRB, all of which are helpful to other endorheic basins in the “Silk Road Economic Belt.” Henceforth, the goal of the Heihe Integrated Observatory Network is to develop an intelligent monitoring system that incorporates ground-based observatory networks, unmanned aerial vehicles, and multi-source satellites through the Internet of Things technology. Furthermore, biogeochemical processes observation will be improved, and the study of integrating ground observations, remote sensing, and large-scale models will be promoted further.

Capturing the spatial heterogeneity and characteristic scale is the key to determining the spatial patterns of land surfaces. In this research, the core observation area of the middle reaches of the Heihe River Basin was selected as the study area, and the scale identification of several typical objects was carried out by implementing experiments on moderate- and high-resolution remotely sensed ASTER and CASI NDVI images. The aim was to evaluate the potential of the local variance and semivariance analysis to characterize the spatial heterogeneity of objects, track their changes with scale, and obtain their scales. Our results show that natural objects have multiscale structures. For a single object with a recognizable size, the results of the two methods are relatively consistent. For continuously distributed samples of indistinctive size, the scale obtained by the local variance is smaller than that obtained by the semivariance. As the image resolution becomes coarser and the research scopes expand, the scales of objects are also increasing. This article also indicates that with a small research area of uniform objects, the local variance and semivariance are easy to facilitate researchers to quickly select the appropriate spatial resolution of remote sensing data according to the research area.

Nighttime light data play an important role in the research on cities, while the urban centers over a large spatial scale are still far from clearly understood. Aiming at the current challenges in monitoring the spatial structure of cities using nighttime light data, this paper proposes a new method for identifying urban centers for massive cities at the large spatial scale based on the brightness information captured by the Suomi National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor. Based on the method for extracting the peak point based on digital elevation model (DEM) data in terrain analysis, the maximum neighborhood and difference algorithms were applied to the NPP-VIIRS data to extract the pixels with the peak nighttime light intensity to identify the potential locations of urban centers. The results show 7239 urban centers in 2200 cities in China in 2017, with an average of 3.3 urban centers per city. Approximately 68% of the cities had significant polycentric structures. The developed method in this paper is useful for identifying the urban centers and can provide the reference to the city planning and construction.

As an important component to quantify the carbon budget, accurate evaluation of terrestrial gross primary production (GPP) is crucial for large-scale applications, especially in dryland ecosystems. Based on the in situ data from six flux sites in northwestern China from 2014 to 2016, this study compares seasonal and interannual dynamics of carbon fluxes between these arid and semi-arid ecosystems and the atmosphere. Meanwhile, the reliability of multiple remotely-derived GPP products in representative drylands was examined, including the Breathing Earth System Simulator (BESS), the Moderate Resolution Imaging Spectroradiometer (MODIS) and data derived from the OCO-2 solar-induced chlorophyll fluorescence (GOSIF). The results indicated that the carbon fluxes had clear seasonal patterns, with all ecosystems functioning as carbon sinks. The maize cropland had the highest GPP with 1183 g C m−2 y−1. Although the net ecosystem carbon exchange (NEE) in the Tamarix spp. ecosystem was the smallest among these flux sites, it reached 208 g C m−2 y−1. Furthermore, distinct advantages of GOSIF GPP (with R2 = 0.85–0.98, and RMSE = 0.87–2.66 g C m−2 d−1) were found with good performance. However, large underestimations in three GPP products existed during the growing seasons, except in grassland ecosystems. The main reasons can be ascribed to the uncertainties in the key model parameters, including the underestimated light use efficiency of the MODIS GPP, the same coarse land cover product for the BESS and MODIS GPP, the coarse gridded meteorological data, and distribution of C3 and C4 plants. Therefore, it still requires more work to accurately quantify the GPP across these dryland ecosystems.

Gridded population results at a fine resolution are important for optimizing the allocation of resources and researching population migration. For example, the data are crucial for epidemic control and natural disaster relief. In this study, the random forest model was applied to multisource data to estimate the population distribution in impervious areas at a 30 m spatial resolution in Chongqing, Southwest China. The community population data from the Chinese government were used to validate the estimation accuracy. Compared with the other regression techniques, the random forest regression method produced more accurate results (R2 = 0.7469, RMSE = 2785.04 and p < 0.01). The points of interest (POIs) data played a more important role in the population estimation than the nighttime light images and natural topographical data, particularly in urban settings. Our results support the wide application of our method in mapping densely populated cities in China and other countries with similar characteristics.

Significant impact of extreme droughts on human society and ecosystem has occurred in many places of the world, for example, Southwest China (SWC). Considerable research concentrated on analyzing causes and effects of droughts in SWC, but few studies have examined seasonal indicators, such as variations of surface water and vegetation phenology. With the ongoing satellite missions, more and more earth observation data become available to environmental studies. Exploring the responses of seasonal indicators from satellite data to drought is helpful for the future drought forecast and management. This study analyzed the seasonal responses of surface water and vegetation phenology to drought in SWC using the multi-source data including Seasonal Water Area (SWA), Permanent Water Area (PWA), Start of Season (SOS), End of Season (EOS), Length of Season (LOS), precipitation, temperature, solar radiation, evapotranspiration, the Palmer Drought Severity Index (PDSI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and data from water conservancy construction. The results showed that SWA and LOS effectively revealed the development and recovery of droughts. There were two obvious drought periods from 2000 to 2017. In the first period (from August 2003 to June 2007), SWA decreased by 11.81% and LOS shortened by 5 days. They reduced by 21.04% and 9 days respectively in the second period (from September 2009 to June 2014), which indicated that there are more severe droughts in the second period. The SOS during two drought periods delayed by 3~6 days in spring, while the EOS advanced 1~3 days in autumn. All of PDSI, SWA and LOS could reflect the period of droughts in SWC, but the LOS and PDSI were very sensitive to the meteorological events, such as precipitation and temperature, while the SWA performed a more stable reaction to drought and could be a good indicator for the drought periodicity. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. Our results improved the understanding of seasonal responses to extreme droughts in SWC, which will be helpful to the drought monitoring and mitigation for different seasons in this ecologically fragile region.

Satellite-derived vegetation indices (VIs) have been widely used to approximate or estimate gross primary productivity (GPP). However, it remains unclear how the VI-GPP relationship varies with indices, biomes, timescales, and the bidirectional reflectance distribution function (BRDF) effect. We examined the relationship between VIs and GPP for 121 FLUXNET sites across the globe and assessed how the VI-GPP relationship varied among a variety of biomes at both monthly and annual timescales. We used three widely-used VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and 2-band EVI (EVI2) as well as a new VI - NIRV and used surface reflectance both with and without BRDF correction from the moderate resolution imaging spectroradiometer (MODIS) to calculate these indices. The resulting traditional (NDVI, EVI, EVI2, and NIRV) and BRDF-corrected (NDVIBRDF, EVIBRDF, EVI2BRDF, and NIRV, BRDF) VIs were used to examine the VI-GPP relationship. At the monthly scale, all VIs were moderate or strong predictors of GPP, and the BRDF correction improved their performance. EVI2BRDF and NIRV, BRDF had similar performance in capturing the variations in tower GPP as did the MODIS GPP product. The VIs explained lower variance in tower GPP at the annual scale than at the monthly scale. The BRDF-correction of surface reflectance did not improve the VI-GPP relationship at the annual scale. The VIs had similar capability in capturing the interannual variability in tower GPP as MODIS GPP. VIs were influenced by temperature and water stresses and were more sensitive to temperature stress than to water stress. VIs in combination with environmental factors could improve the prediction of GPP than VIs alone. Our findings can help us better understand how the VI-GPP relationship varies among indices, biomes, and timescales and how the BRDF effect influences the VI-GPP relationship.

Precise quantification of terrestrial gross primary production (GPP) has been recognized as one of the most important components in understanding the carbon balance between the biosphere and the atmosphere. In recent years, although many large-scale GPP estimates from satellite data and ecosystem models have been generated, few attempts have been made to compare the different GPP products at national scales, particularly for various climate zones. In this study, two of the most widely-used GPP datasets were systematically compared over the eight climate zones across China’s terrestrial ecosystems from 2001 to 2015, which included the moderate resolution imaging spectroradiometer (MODIS) GPP and the breathing Earth system simulator (BESS) GPP products. Additionally, the coarse (0.05o) GPP estimates from the vegetation photosynthesis model (VPM) at the same time scale were used for auxiliary analysis with the two products. Both MODIS and BESS products exhibited a decreasing trend from the southeast region to the northwest inland. The largest GPP was found in the tropical humid region with 5.49 g C m−2 d−1 and 5.07 g C m−2 d−1 for MODIS and BESS, respectively, while the lowest GPP was distributed in the warm temperate arid region, midtemperate semiarid region and plateau zone. Meanwhile, the work confirmed that all these GPP products showed apparent seasonality with the peaks in the summertime. However, large differences were found in the interannual variations across the three GPP products over different climate regions. Generally, the BESS GPP agreed better than the MODIS GPP when compared to the seasonal and interannual variations of VPM GPP. Furthermore, the spatial correlation analysis between terrestrial GPP and the climatic factors, including temperature and precipitation, indicated that natural rainfall dominated the variability in GPP of Northern China, such as the midtemperate semiarid region, while temperature was a key controlling factor in the Southern China and the Tibet Plateau area.

This study presents a revised temporal scaling method based on a detection algorithm for the temporal stability of the evaporative fraction (EF) to estimate total daytime evapotranspiration (ET) at a regional scale. The study area is located in the Heihe River Basin, which is the second largest inland river basin in China. The remote sensing data and field observations used in this study were obtained from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project. The half-hourly EF values (EFEC) calculated using meteorological observations from an eddy covariance (EC) system and an automatic meteorological station (AMS) represented the diurnal pattern of the EF across the majority of the study area. The remotely sensed instantaneous midday EF (EFASTER), which indicates the spatial distribution of the midday EF over the entire study area, was calculated from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image. The temporal stability of EFEC was examined using a detection algorithm. Intervals with inconsistent EFEC values were distinguished from those with consistent EFEC values; the total daytime ET (from 9:00 to 19:00) within these interval types was integrated separately. Validation of the total daytime ET at the satellite pixel scale was conducted using measurements from17 EC towers. Using the detection algorithm for the temporal stability of the EF and dynamic adjustment, the revised temporal scaling method resulted in a root-mean-square error (RMSE) of 0.54 (mm·d−1), a mean relative error (MRE) of 7.26% and a correlation coefficient (Corr.) of 0.81; all of these values were superior to those of the two other methods (i.e., the constant EF and variable EF methods). The revised method easily extends to other areas and exhibits a superior performance in flat and regularly-irrigated farmlands at the regional scale.

Validation is an essential and important step before the application of remote sensing products. This paper introduces a prototype of the validation network for remote sensing products in China (VRPC). The VRPC aims to improve remote sensing products at a regional scale in China. These improvements will enhance the applicability of the key remote sensing products in understanding and interpretation of typical land surface processes in China. The framework of the VRPC is introduced first, including its four basic components. Then, the basic selection principles of the observation sites are described, and the principles for the validation of the remote sensing products are established. The VRPC will be realized in stages. In the first stage, four stations that have improved remote sensing observation facilities have been incorporated according to the selection principles. Certain core observation sites have been constructed at these stations. Next the Heihe Station is introduced in detail as an example. The three levels of observation (the research base, pixel-scale validation sites, and regional coverage) adopted by the Heihe Station are carefully explained. The pixel-scale validation sites with nested multi-scale observation systems in this station are the most unique feature, and these sites aim to solve some key scientific problems associated with remote sensing product validation (e.g., the scale effect and scale transformation). Multi-year of in situ measurements will ensure the high accuracy and inter-annual validity of the land products, which will provide dynamic regional monitoring and simulation capabilities in China. The observation sites of the VRPC are open, with the goal of increasing cooperation and exchange with global programs.

An accurate estimation of carbon fluxes is very important in carbon cycle studies. A remote sensing based gross primary production (GPP) and net ecosystem production (NEP) algorithm, RS-CFLUX, was presented in this work. The algorithm was calibrated with Markov Chain Monte Carlo (MCMC) method at Daman superstation and Zhangye wetland station in the midstream of the Heihe River Basin. Results indicated that both of the stations present high GPP (1442.04 g C/m2/year at Daman superstation and 928.89 g C/m2/year at Zhangye wetland station) and NEP (409.38 g C/m2/year at Daman superstation and 422.60 g C/m2/year at Zhangye wetland station). The RS-CFLUX model can correctly simulate the seasonal dynamics and quantities of carbon fluxes at both stations, using photosynthetically active radiation (PAR), land surface temperature (LST), normalized difference water index (NDWI) and enhanced vegetation index (EVI) as input. RS-CFLUX model were sensitive to maximum light use efficiency, respiration at reference temperature, activation energy parameter of respiration.

This paper aims to retrieve temporal high-resolution LAI derived by fusing MOD15 products (1 km resolution), field-measured LAI and ASTER reflectance (15-m resolution). Though the inversion of a physically based canopy reflectance model using high-resolution satellite data can produce high-resolution LAI products, the obstacle to producing temporal products is obvious due to the low temporal resolution of high resolution satellite data. A feasible method is to combine different source data, taking advantage of the spatial and temporal resolution of different sensors. In this paper, a high-resolution LAI retrieval method was implemented using a dynamic Bayesian network (DBN) inversion framework. MODIS LAI data with higher temporal resolution were used to fit the temporal background information, which is then updated by new, higher resolution data, herein ASTER data. The interactions between the different resolution data were analyzed from a Bayesian perspective. The proposed method was evaluated using a dataset collected in the HiWater (Heihe Watershed Allied Telemetry Experimental Research) experiment. The determination coefficient and RMSE between the estimated and measured LAI are 0.80 and 0.43, respectively. The research results suggest that even though the coarse-resolution background information differs from the high-resolution satellite observations, a satisfactory estimation result for the temporal high-resolution LAI can be produced using the accumulated information from both the new observations and background information.

This study presents preliminary results of the validation of the Moderate Resolution Imaging Spectroradiometer (MODIS) daily LST products (MOD/MYD11A1, Version 5) using longwave radiation ground measurements obtained at 12 stations in the North Arid and Semi-Arid Area Cooperative Experimental Observation Integrated Research program. In this evaluation process, the broadband emissivity at each station was obtained from the ASTER Spectral Library or estimated from the MODIS narrowband emissivity Collection 5. A comparison of the validation results based on those two methods shows that no significant differences occur in the short-term validation, and a sensitivity analysis of the broadband emissivity demonstrates that it has a limited effect on the evaluation results. In general, the results at the 12 stations indicate that the LST products have a lower accuracy in China’s arid and semi-arid areas than in other areas, with a mean absolute error of 2–3 K. Compared with the temporal mismatch, the spatial mismatch has a stronger effect on the validation results in this study, and the stations with homogeneous land cover have more comparable MODIS LST accuracies. Comparisons between the stations indicate that the spatial mismatch can increase the influence of the temporal mismatch.

More than 20 techniques have been developed to de-noise time-series vegetation index data from different satellite sensors to reconstruct long time-series data sets. Although many studies have compared Normalized Difference Vegetation Index (NDVI) noise-reduction techniques, few studies have compared these techniques systematically and comprehensively. This study tested eight techniques for smoothing different vegetation types using different types of multi-temporal NDVI data (Advanced Very High Resolution Radiometer (AVHRR) (Global Inventory Modeling and Map Studies (GIMMS) and Pathfinder AVHRR Land (PAL), Satellite Pour l’ Observation de la Terre (SPOT) VEGETATION (VGT), and Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra)) with the ultimate purpose of determining the best reconstruction technique for each type of vegetation captured with four satellite sensors. These techniques include the modified best index slope extraction (M-BISE) technique, the Savitzky-Golay (S-G) technique, the mean value iteration filter (MVI) technique, the asymmetric Gaussian (A-G) technique, the double logistic (D-L) technique, the changing-weight filter (CW) technique, the interpolation for data reconstruction (IDR) technique, and the Whittaker smoother (WS) technique. These techniques were evaluated by calculating the root mean square error (RMSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). The results indicate that the S-G, CW, and WS techniques perform better than the other tested techniques, while the IDR, M-BISE, and MVI techniques performed worse than the other techniques. The best de-noise technique varies with different vegetation types and NDVI data sources. The S-G performs best in most situations. In addition, the CW and WS are effective techniques that were exceeded only by the S-G technique. The assessment results are consistent in terms of the three evaluation indexes for GIMMS, PAL, and SPOT data in the study area, but not for the MODIS data. The study will be very helpful for choosing reconstruction techniques for long time-series data sets.

The remote sensing of solar-induced chlorophyll fluorescence (SIF) has attracted considerable attention as a new monitor of vegetation photosynthesis. Previous studies have revealed the close correlation between SIF and terrestrial gross primary productivity (GPP), and have used SIF to estimate vegetation GPP. This study investigated the relationship between the Orbiting Carbon Observatory-2 (OCO-2) SIF products at two retrieval bands (SIF757, SIF771) and the autumn crop production in China during the summer of 2015 on different timescales. Subsequently, we evaluated the performance to estimate the autumn crop production of 2016 by using the optimal model developed in 2015. In addition, the OCO-2 SIF was compared with the moderate resolution imaging spectroradiometer (MODIS) vegetation indices (VIs) (normalized difference vegetation index, NDVI; enhanced vegetation index, EVI) for predicting the crop production. All the remotely sensed products exhibited the strongest correlation with autumn crop production in July. The OCO-2 SIF757 estimated autumn crop production best (R2 = 0.678, p < 0.01; RMSE = 748.901 ten kilotons; MAE = 567.629 ten kilotons). SIF monitored the crop dynamics better than VIs, although the performances of VIs were similar to SIF. The estimation accuracy was limited by the spatial resolution and discreteness of the OCO-2 SIF products. Our findings demonstrate that SIF is a feasible approach for the crop production estimation and is not inferior to VIs, and suggest that accurate autumn crop production forecasts while using the SIF-based model can be obtained one to two months before the harvest. Furthermore, the proposed method can be widely applied with the development of satellite-based SIF observation technology.