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The Sentinel-2A satellite having embedded advantage of red edge spectral bands offers multispectral imageries with improved spatial, spectral and temporal resolutions as compared to the other contemporary satellites providing medium resolution data. Our study was aimed at exploring the potential of Sentinel-2A imagery to estimate Above Ground Biomass (AGB) of Subtropical Pine Forest in Pakistan administered Kashmir. We developed an AGB predictive model using field inventory and Sentinel 2A based spectral and textural parameters along with topographic features derived from ALOS Digital Elevation Model (DEM). Field inventory data was collected from 108 randomly distributed plots (0.l ha each) across the study area. The stepwise linear regression method was employed to investigate the potential relationship between field data and corresponding satellite data. Biomass and carbon mapping of the study area was carried out through established AGB estimation model with R (0.86), R2 (0.74), adjusted R2 (0.72) and RMSE value of 33 t/ha. Our results showed that first order textures (mean, standard deviation and variance) significantly contributed in AGB predictive modeling while only one spectral band ratio made contribution from spectral domain. Our study leads to the conclusion that Sentinel-2A optical data is a potential source for AGB estimation in subtropical pine forest of the area of interest with added benefit of its free of cost availability, higher quality data and long-term continuity that can be utilized for biomass carbon distribution mapping in the resource constraint study area for sustainable forest management.

The Yellow River delta (YRD) is regarded as one of the most rapid sedimentation deltas in the worldwide and the youngest land in China. Existing studies more focused on evolutions of river mouth region, thus provided limited knowledge on the evolution of the entire YRD and its corresponding trajectories of shrinkage and expansion. Here, we used 1984–2018 Landsat images and the change detection analysis algorithm to track annual area change trajectories of present YRD with the help of Google Earth Engine cloud platform. Results showed that entire YRD area documented a significantly shrinking trend with slope of –18.5 km2/decade ( p p p

The mountainous and hilly region plays an important role in ecological safety and production in China. However, recent studies have poorly characterized the parallel structure of land use in the valleys of the mountainous and hilly region using topographic factors (e.g. elevation, slope, aspect). Here, the loess hilly region of northern Shaanxi Province is used as a representative case area to analyze the vertical distribution pattern of land-use conversion using the relative elevation concept and the HAND index. The differences in the vertical structure of land-use conversion between absolute elevation and relative elevation were compared. We found that the classifications of absolute and relative elevation had similar proportions of each relative elevation grade in each absolute elevation grade. Cropland, woodland, and grassland were distributed evenly in each grade of absolute/relative elevation, while water body, built-up land and unused land were more likely to spread in low grades of relative elevation than those of absolute elevation. The land-use conversion (i.e. loss of cropland and gain in woodland and built-up land) showed an apparently stepped distribution with relative elevation classification, suitable for revealing vertical distributions of land-use conversion in the loess hilly region. Cropland transformed to woodland was mainly distributed in high grade of relative elevation, decreasing with a decrease in grades, while built-up land transformed from cropland and grassland was mainly distributed in low grade of relative elevation, decreasing with increases in grades. The grade of relative elevation where cropland transformed to woodland descended with the implementation of the Grain for Green Project. Our results suggest that it is better to analyze the vertical distribution of land-use conversion with relative elevation classification in hilly regions.

Vertically integrated atmospheric water vapor (VIWV) over the Indo-Pacific warm pool (IPWP) indirectly affects terrestrial vegetation growth (TVG) patterns through atmospheric water vapor transmission. However, their linkages and mechanisms are poorly understood. This study intends to understand the contributions of VIWVIPWP to TVG and the mechanisms by which VIWVIPWP impacts TVG. Combining monthly SST, VIWV, and NDVI data from 1982 to 2015, the linkage between VIWVIPWP and NDVI is investigated during April–June (AMJ). A strong correlation between VIWVIPWP and NDVI suggests that VIWVIPWP is an important factor affecting TVG. A composite analysis of VIWVIPWP anomalies and their relation to NDVI patterns shows that VIWVIPWP positively influences the NDVI of 68.1% of global green land during high-VIWVIPWP years but negatively influences 74.7% in low years. Corresponding to these results, during high-VIWVIPWP years, the warm and humid terrestrial climate conditions improved TVG by 9% and 2% in the Northern and Southern Hemispheres, respectively, but cold and dry conditions inhibited TVG for both hemispheres during the low years. Additionally, strong spatial correlations between VIWVIPWP and precipitation imply that VIWVIPWP affects the spatial–temporal pattern of precipitation. There is a stronger interaction between the Pacific north–south ridge and the two land troughs during high-VIWVIPWP years than during low-VIWVIPWP years. The zonally averaged wind at 850 hPa and VIWV results indicate that, during high-VIWVIPWP years, the enhanced wind from the ocean brings more atmospheric water vapor to land, increasing the probability of precipitation and resulting in moist climate conditions that promote AMJ vegetation growth. In brief, VIWVIPWP indirectly induces vegetation growth by affecting the distributions of terrestrial VIWV and precipitation.

Wheat vegetative growth, defined as the stage from green-up date to heading date (STAGE), is a crucial phrase, which largely affects crop yield. Previous some studies mainly focused on site-based changes of STAGE. However, there are few studies concerning large-scale changes of STAGE, which limits our understanding of regional changes of STAGE in response to climate change. Here, we introduced robust satellite-based phenological algorithms as well as third-generation global inventory modeling and mapping studies normalized difference vegetation index for the period of 1982–2015 to spatially derive winter wheat green-up date, heading date, and STAGE over croplands of the North China Plain (NCP). Changes of STAGE and their response to climate change were then investigated. Results showed that a strong predicted ability of introduced heading date algorithm was observed with r of 0.88 ( p R 2 = 0.11, p = 0.06). Stage of 23.4% cropland pixels performed significantly lengthened trends ( p r = 0.59, p < 0.01). This study highlights important roles of remote sensing data and satellite-based phenological algorithms for regional crop growth monitoring and management.

Separating anthropogenic contributions from observed vegetation change is helpful for improving our understanding of the effects of human activities on regional ecosystems. In this study, using 1982–2015 GIMMS3g normal difference vegetation index (observed NDVI), monthly climatic variables and land use data, we investigated anthropogenic contributions on vegetation change in the farming-pastoral ecotone of northern China (FPEN). Specifically, given that large-area ecological engineering was practiced since 1999 and large-area land use changes were recorded after 2000, we assumed that human activities only had little impact before 1999. Based on this assumption, we developed a climate-based NDVI model (also termed as TPR-based NDVI model) by using 1982–1999 observed NDVI and assembled monthly precipitation, temperature and solar radiation. Subsequently, the TPR-based NDVI model as well as the residual analysis method were used to separate anthropogenic contributions from observed NDVI in the period of 2000–2015. Results showed that most FPEN performed a greening trend for the period of 1982–2015. Yet a browning trend was also found in central and northern FPEN. The browning trend was largely related to changes of observed NDVI after 2000. Spatial statistics for the best related climatic variable with observed NDVI displayed that temperature, precipitation and solar radiation separately accounted for 42.45%, 31.05% and 26.50% of FPEN, implying their similar importance for vegetation growth in space. Importantly, this study found that anthropogenic contributions dominated trends of observed NDVI over FPEN. Human activities significantly increased NDVI in western and southeastern FPEN (p

Studying the shifts of vegetation phenology and the response of vegetation phenology to climate change helps to understand the dynamics of future ecosystems. However, since previous studies mostly focused on temperate ecosystems, much less is known about the biogeographic phenological shifts in sub-tropical regions, which have abundant biodiversity. The Yangtze River Basin (YRB) is located in the subtropical region of China and has abundant natural resources, a large population, and rapid economic development. Studying the variation characteristics of phenology and its responses to recent climate changes in YRB are important for understanding the impact of regional climate on subtropical ecosystems. In this study, we extracted the phenological parameters using Global Inventory Modeling and Mapping Studies (GIMMS) data to investigate the spatial and temporal variations of vegetation phenology across YRB during 1982–2015 and to examine how vegetation phenology responds to climate within different ecological zones. The results revealed that the start of growing season (SOS) was significantly advanced by 0.2 days/year (p

Climate warming and its corresponding impacts on agriculture system increasingly attach great attentions. Earlier studies more concerned the impacts of the cultivated area expansion under climate change. Yet limited knowledge is about the impacts of climate warming on the cropping index change with the shifts of cropping system border. In this study, we used climatic data (1961-2015) to firstly investigate impacts of warming temperature on potential cropping system border expansion of China, and further used agricultural statistical data and satellite-based land use data to analyze the response of current land system to potential cropping system border expansion. Results of this study indicated that obviously advanced SDT10 and prolonged EDT10 contributed to the 88.4% regions of increased AAT10 at the past half century. Moreover, the northward expansion of the suitable cultivated areas in different cropping systems provided advantages for potential multiple cropping index (PMCI) improvement. Unfortunately, this study found that a significantly declined multiple cropping index (MCI) was observed in the peri-urban regions and the provinces with large out-migration of agriculture labor. The evidently increased MCI was only greatly observed in Xinjiang province. Besides, the potential increment of multiple cropping index (PIMCI) for different cropping system border expansion regions presented a rising trend and reached 53.6% in 2015 due to warming climate. Particularly, the significantly increased PIMCI was observed in the Loess Plateau, the Inner Mongolia, the Middle-lower Yangtze Plain, Northeast China Plain, Southern China and Beijing-Tianjin-Hebei Metropolitan Region. However, the response of current land system to the changes of PMCI and PIMCI was not timely. Based on the findings of our study, some potential agriculture development strategies were suggested by comprehensively considering regional natural conditions, agricultural production conditions and socioeconomic conditions. We hope these findings of this study could provide some valuable information for agricultural development policy decision-making.

Alpine vegetation plays a crucial role in global carbon cycle. Snow cover is an essential component of alpine land cover and shows high sensitivity to climate change. The Tibetan Plateau (TP) has a typical alpine vegetation ecosystem and is rich of snow resources. With global warming, the snow of the TP has undergone significant changes that will inevitably affect the growth of alpine vegetation, but observed evidence of such interaction is limited. In particular, a comprehensive understanding of the responses of alpine vegetation growth to snow cover variability is still not well characterized on TP region. To investigate this, we calculated three indicators, the start (SOS) and length (LOS) of growing season, and the maximum of normalized difference vegetation index (NDVImax) as proxies of vegetation growth dynamics from the Moderate Resolution Imaging Spectroradiometer (MODIS) data for 2000–2015. Snow cover duration (SCD) and melt (SCM) dates were also extracted during the same time frame from the combination of MODIS and the Interactive Multi-sensor Snow and Ice Mapping System (IMS) data. We found that the snow cover phenology had a strong control on alpine vegetation growth dynamics. Furthermore, the responses of SOS, LOS and NDVImax to snow cover phenology varied among different biomes, eco-geographical zones, and temperature and precipitation gradients. The alpine steppes showed a much stronger negative correlation between SOS and SCD, and also a more evidently positive relationship between LOS and SCD than other types, indicating a longer SCD would lead to an earlier SOS and longer LOS. Most areas showed positive correlation between SOS and SCM, while a contrary response was also found in the warm but drier areas. Both SCD and SCM showed positive correlations with NDVImax, but the relationship became weaker with the increase of precipitation. Our findings provided strong evidence between vegetation growth and snow cover phenology, and changes in snow cover should be also considered when analyzing alpine vegetation growth dynamics in future.

ERA-Interim has been widely considered as a valid proxy for observations at global and regional scales. However, the verifications of ERA-Interim precipitation and temperature in mainland China have been rarely conducted, especially in the spatial and long-term performances. Therefore, in this study, we employed the interpolated ground station (STA) data to evaluate the spatial and temporal patterns and trends of ERA-Interim precipitation and temperature during 1980–2012. The results showed that relatively weaker performances were observed in ERA-Interim precipitation, with the skill score (S index) ranging from 0.41 to 0.50. Interannual ERA-Interim precipitation presented comparable trends with STA precipitation at the annual and seasonal scales. Spatial patterns of empirical orthogonal function (EOF) modes and corresponding principal components were evidently different between annual ERA-Interim and STA precipitation. For temperature, annual and seasonal patterns of ERA-Interim data were in good consistency with those of STA over China with the S index ranging from 0.59 to 0.70. Yet interannual STA temperature recorded stronger warming trends (from 0.37 K decade−1 of wintertime to 0.53 K decade−1 of springtime) at the annual and seasonal scales compared to corresponding periods for ERA-Interim temperature (from 0.03 K decade−1 of wintertime to 0.25 K decade−1 of summertime). Overall, ERA-Interim precipitation and temperature had good agreement with STA data in east China with lower elevation (

Frequent drought events are threatening regional/global food security. However, current models for predicting crop yield loss suffered from drought threat is still limited. In this study, seven drought indices were employed to develop an aggregate drought index (ADI) to quantify the drought effect on crop yield in the North China Plain (NCP). Based on time-series and panel regression models, relationships between climatic yield anomalies of winter wheat (ΔY) and ADI values before sowing of winter wheat and during its growth were investigated. Results showed that ADI assembled the advantages of drought indices and reasonably described the range, severity and duration of drought events. During the study period, trends of annual ADI displayed remarkable spatial heterogeneity in the growth stages. Increasing trends of ADI over 0.005/year were found in most areas of NCP at STAGE 1 (from July to September in last year). At STAGE 4 (from grain-filling to maturation), increasing and decreasing trends were observed in northern and southern NCP, respectively. Based on panel model, ADI in the four stages could predict about 20% variations of ΔY in areas with high irrigation supplies. Meanwhile, more than 40% variations of ΔY may be predicted in eastern NCP with low irrigation supplies. The study highlights the importance of integrating drought information and irrigation for assessment of winter wheat yield variations.

Evaluating the uncertainties in regional/global carbon flux estimates is essential for better understanding of terrestrial carbon dynamics. At the regional scale, climate input data is an important source of model simulation uncertainty. In this study, a process-based ecosystem model, CEVSA, was run driven by four climate input datasets during 1980–2004, i.e., climate input datasets interpolated from 756 (756s) and 2400 weather stations (2400s), the NCEP/NCAR and Princeton reanalysis datasets. We used the 2400s dataset as the reference because it was derived from high density weather station interpolation. The simulated Net Primary Productivity (NPP) based on interpolated climate data from the 756s and the two reanalysis datasets were compared with that from the 2400s dataset. Then, we quantified the uncertainty of model simulations at regional-scale caused by climate input data, and evaluated the performance of different climate datasets across different eco-regions. Our results suggest that the 756s, Princeton and NCEP/NCAR reanalysis datasets overestimated the 25-year mean annual temperature by 7.66%–12.25% and the precipitation by 2.83%–8.43%, respectively; accordingly, the simulated NPP ranged from 3.53 to 3.96 Pg C, 6% to 12% higher than the reference over the entire China. The 756s and the two reanalysis datasets captured well the trend and interannual variations of annual NPP during the study period, but showed systematic errors in the total amount of NPP compared with the 2400s dataset. To increase the station density in the eco-regions with a station density greater than 1.0 station per 10 4 km 2 (1.0 s/10 4 km 2 ) would not decrease the uncertainty for model simulation at a 0.1° spatial resolution. The NCEP/NCAR and Princeton reanalysis datasets showed larger uncertainties in most eco-regions compared with the interpolated datasets. Our results also suggest that the accuracy of the NCEP/NCAR reanalysis data should be further improved in most eco-regions. On Qinghai-Tibet Plateau and in northwestern China, all four climate input datasets had relatively lower accuracy due to the limited observation data available. Future work should further evaluate the simulated NPP against observations and quantify the accuracy of driving climate data to decrease the uncertainty of model simulations at the regional scale.

Quantifying forest evapotranspiration (ET) is essential for understanding of climatic response of forest carbon and water cycling. However, there are still large uncertainties in forest ET predictions, especially in plant transpiration (PT). The poor estimations of forest ET and PT are largely attributed to the neglect of wet canopy evaporation and uncertainties in the stomatal conductance. Thus, by coupling a revised Ball—Woodrow–Berry (BWB) model, a precipitation intercepted algorithm and the gross primary production (GPP) model to Shuttleworth–Wallace (SW) model, this study introduced a modified SW ${\rm{(SW}}_{{\rm{m}}})$ model. The performances of this model were subsequently tested in three different forest sites with long-term observed records. Compared with previous models, ${\rm{SW}}_{{\rm{m}}}$ had a canopy stomatal scheme with stronger ecological significance and simpler GPP estimation scheme. Our analyses reveal the following. 1) ${\rm{SW}}_{{\rm{m}}}$ evidently improves the agreements between estimated and measured ET compared to original SW ( R 2 increasing by 0.19–0.68). ${\rm{SW}}_{{\rm{m}}}$ could more accurately partition PT and evaporation, when compared with an earlier BWB-based SW ( R 2 increasing by ∼0.03). This finding also supports the use of Lohammer function in semiempirical model of stomatal conductance. 2) Accurate predictions of GPP are helpful for improving ET estimations in ${\rm{SW}}_{{\rm{m}}}\,(r\,= \,{\text{0.73,\,p}}\, , suggesting that carbon and water fluxes are inherently linked. 3) In addition to GPP, leaf area index evidently affects the performances of estimated ET in ${\rm{SW}}_{{\rm{m}}}$ . These results suggest that critically coupling carbon and water cycling are very important for improving forest ET prediction.

Advances in the timing of spring green-up date are a typical response of vegetation global change. Long-term observations of plant phenology have been used to track vegetation response to climate change. Normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) have been the most commonly used indicators in reconstructing spring green-up dates from remote sensing over the past several decades. The Moderate Resolution Imaging Spectroradiometer (MODIS) based phenology time series from NDVI and EVI with an enhanced TIMESAT algorithm are the two operational phenology products developed recently with sun-sensor geometry corrected reflectance. However, a comprehensive intercomparison and evaluation of these two spring green-up datasets using intensive ground observations have not been conducted, limiting their applications in regional interpretation of land surface phenology. Therefore, we used 455 ground observations from USA National Phenology Network (NPN) and 106 observations from 21 AmeriFlux sites to assess and validate the spring green-up dates for United States for 2000–2013. Our results indicate that the spring green-up dates from NDVI and EVI showed a good agreement in eastern United States, while substantial differences (70 days) were found in western and southern areas. Ground observations generally had a significant correlation (p < 0.01) with the two MODIS spring green-up dates, especially for deciduous broadleaf forests with root mean square error (RMSE) of 12 days and 16 days when compared with USA-NPN and AmeriFlux observations, respectively. Spring green-up dates from EVI overall showed a better relationship and a lower RMSE in reference to both USA-NPN and AmeriFlux observations than NDVI-based spring green-up dates. Our results highlight the importance of a rigorous validation of remote sensing products to better understand their limitations in operational applications.

Non-photosynthetic components within the canopy (e.g., dry leaves and stem) contribute little to photosynthesis and therefore, remote sensing of gross primary production (GPP) could be improved by the removal of these components. A scaled enhanced vegetation index (EVI), which is usually regarded as a linear function of EVI, was found to have the strongest relationship with chlorophyll level fraction of absorbed photosynthetically active radiation (FPARchl) and can help improve GPP estimation in croplands compared to canopy level FPAR (FPARcanopy). However, the application of the FPARchl theory to other plant functional types (PFTs) and the underlying reasons remain largely unknown. In this study, based on standard MODIS algorithm we comprehensively assessed the performances of FPARcanopy, scaled EVI (FPARchl1), normalized difference vegetation index (NDVI), scaled NDVI (FPARchl2) and EVI as proxies of FPAR for estimating GPP at four forest and six non-forest sites (e.g., grasslands, croplands and wetlands) from ChinaFLUX, representing a wide range of ecosystems with different canopy structures and eco-climatic zones. Our results showed that the scaled EVI (FPARchl1) as FPAR effectively improved the accuracy of estimated GPP for the entire PFTs. FPARchl1 substantially improved forest GPP estimations with higher coefficient of determination (R2), lower root mean square error (RMSE) and lower bias. In comparison, for non-forest PFTs, the improvement in R2 between estimated GPP based on FPARchl1 (GPPchl1) and flux tower GPP was less evident than those between flux GPP and GPP estimations from FPARcanopy (GPPcanopy), FPARchl2, NDVI and EVI. The temperature and water attenuation scalars played important roles in reducing the difference of various GPP and indirectly reducing the impact of different FPARs on GPP in non-forest PFTs. Even so, FPARchl1 is an ecologically more meaningful parameter since FPARchl1 and flux tower GPP dropped to zero more synchronously in both forest and non-forest sites. In particular, we found that the improvement of GPPchl1 relative to GPPcanopy was positively correlated with the maximum leaf area index (LAI), implying the importance of site characteristic in regulating the strength of the improvement. This is encouraging for remote sensing of GPP for which vegetation parameter retrieval has often been found to be less successful at high LAI due to saturations in reflective and scattering domains. Our results demonstrate the significance of accurate and ecologically meaningful FPAR parameterization for improving our current capability in GPP modeling.

Human transformation of landscapes is pervasive and accelerating across the Earth. However, existing studies have not provided a comprehensive picture of how precipitation frequency and intensity respond to vegetation cover change. Therefore, this study took the Loess Plateau as a typical example, and used satellite-based Normalized Difference Vegetation Index (NDVI) data and daily gridded climatic variables to assess the responses of precipitation dynamics to human-induced vegetation cover change. Results showed that the total precipitation amount exhibited little change at the regional scale, showing an upward but statistically insignificant (p > 0.05) trend of 7.6 mm/decade in the period 1982–2015. However, the frequency of precipitation with different intensities showed large variations over most of the Loess Plateau. The number of rainy days (light, moderate, heavy, very heavy and severe precipitation) increased in response to increased vegetation cover, especially in the central-eastern Loess Plateau. Anthropogenic land cover change is largely responsible for precipitation intensity changes. Additionally, this study also observed high spatially explicit heterogeneity in different precipitation intensities in response to vegetation cover change across the Loess Plateau. These findings provide some reference information for our understanding of precipitation frequency and intensity changes in response to regional vegetation cover change in the Loess Plateau.

Clarifying the spatial and temporal variations in precipitation-use efficiency (PUE) is helpful for advancing our knowledge of carbon and water cycles in Tibetan grassland ecosystems. Here we use an integrated remote sensing normalized difference vegetation index (NDVI) and in-situ above-ground net primary production (ANPP) measurements to establish an empirical exponential model to estimate spatial ANPP across the entire Tibetan Plateau. The spatial and temporal variations in PUE (the ratio of ANPP to mean annual precipitation (MAP)), as well as the relationships between PUE and other controls, were then investigated during the 2001–2012 study period. At a regional scale, PUE increased from west to east. PUE anomalies increased significantly (>0.1 g·m–2·mm–1/10 yr) in the southern areas of the Tibetan Plateau yet decreased (>0.02 g·m–2·mm–1/10 yr) in the northeastern areas. For alpine meadow, we obtained an obvious breaking point in trend of PUE against elevation gradients at 3600 m above the sea level, which showed a contrasting relationship. At the inter-annual scale, PUE anomalies were smaller in alpine steppe than in alpine meadow. The results show that PUE of Tibetan grasslands is generally high in dry years and low in wet years.

Arid region characterizes more than 30% of the Earth’s total land surface area and the area is still increasing due to the trends of desertification, yet the extent to which it modulates the global C balance has been inadequately studied. As an emerging technology, IoT monitoring can combine researchers, instruments, and field sites and generate archival data for a better understanding of soil abiotic CO2uptake in arid region. Images’ similarity analyses based on IoT monitoring can help ecologists to find sites where the abiotic uptake can temporally dominate and how the negative soil respiration fluxes were produced, while IoT monitoring with a set of intelligent video recognition algorithms enables ecologists to revisit these sites and the experiments details through the videos. Therefore, IoT monitoring of geospatial images, videos, and associated optimization and control algorithms should be a research priority towards expanding insights for soil abiotic CO2uptake and a better understanding of how the uptake happens in arid region. Nevertheless, there are still considerable uncertainties and difficulties in determining the overall perspective of IoT monitoring for insights into the missing CO2sink.

An ecosystem model serves as an important tool to understand the carbon cycle in the forest ecosystem. However, the sensitivities of parameters and uncertainties of the model outputs are not clearly understood. Parameter sensitivity analysis (SA) and uncertainty analysis (UA) play a crucial role in the improvement of forest gross primary productivity GPP simulation. This study presents a global SA based on an extended Fourier amplitude sensitivity test (EFAST) method to quantify the sensitivities of 16 parameters in the Flux-based ecosystem model (FBEM). To systematically evaluate the parameters&rsquo, sensitivities, various parameter ranges, different model outputs, temporal variations of parameters sensitivity index (SI) were comprehensively explored via three experiments. Based on the numerical experiments of SA, the UA experiments were designed and performed for parameter estimation based on a Markov chain Monte Carlo (MCMC) method. The ratio of internal CO2 to air CO2 ( , canopy quantum efficiency of photon conversion , maximum carboxylation rate at 25 ( were the most sensitive parameters for the GPP. It was also indicated that were influenced by temperature throughout the entire growth stage. The result of parameter estimation of only using four sensitive parameters (RMSE =1.657) is very close to that using all the parameters (RMSE = 1.496). The results of SA suggest that sensitive parameters, such as strongly influence on the forest GPP simulation, and the temporal characteristics of the parameters` SI on GPP and NEE were changed in different growth. The sensitive parameters were a major source of uncertainty and parameter estimation based on the parameter SA could lead to desirable results without introducing too great uncertainties.

Substantial efforts have been made to identify crop types by region, but few studies have been able to classify crops in early season, particularly in regions with heterogeneous cropping patterns. This is because image time series with both high spatial and temporal resolution contain a number of irregular time series, which cannot be identified by most existing classifiers. In this study, we firstly proposed an improved artificial immune network (IAIN), and tried to identify major crops in Hengshui, China at early season using IAIN classifier and short image time series. A time series of 15-day composited images was generated from 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Near-infrared (NIR) band and normalized difference vegetation index (NDVI) were selected as optimal bands by pair-wise Jeffries–Matusita distances and Gini importance scores calculated from the random forest algorithm. When using IAIN to identify irregular time series, overall accuracy of winter wheat and summer crops were 99% and 98.55%, respectively. We then used the IAIN classifier and NIR and NDVI time series to identify major crops in the study region. Results showed that winter wheat could be identified 20 days before harvest, as both the producer’s accuracy (PA) and user’s accuracy (UA) values were higher than 95% when an April 1–May 15 time series was used. The PA and UA of cotton and spring maize were higher than 95% with image time series longer than April 1–August 15. As spring maize and cotton mature in late August and September–October, respectively, these two crops can be accurately mapped 4–6 weeks before harvest. In addition, summer maize could be accurately identified after August 15, more than one month before harvest. This study shows the potential of IAIN classifier for dealing with irregular time series and Sentinel-1 and Sentinel-2 image time series at early-season crop type mapping, which is useful for crop management.

The biophysical effect of land use and land cover change (LUCC) on regional climatic regulation is currently of growing interest. However, in water-limited temperate regions, the net biophysical effect of conversion from croplands to grasslands on regional climatic regulation remains poorly understood to date. To answer this concern, a modified land surface model (mEASS) and two different land use scenarios in a typical study area of the Loess Plateau of China were used in this study. We first validated the performances of mEASS model by using observations from six flux tower sites with different land cover and three metrics of the coefficient of determination (R2), the root mean square error (RMSE) and the difference between the simulated and observed data (bias). Subsequently, the biophysical effect of conversion from croplands to grasslands was investigated. Results indicated that mEASS model could well capture the seasonal dynamics of net radiation and latent heat with high R2 and lower RMSE and bias at grassland, forest and cropland sites. In the context of semi-arid and semi-humid climatic conditions, conversion from croplands to grasslands caused the cooling effect (−0.3 W/m2) at the annual scale. Similar cooling effects were found in spring (−0.4 W/m2), autumn (−0.8 ± 0.1 W/m2) and winter (−0.9 ± 0.1 W/m2). The decreased latent heat (inducing warming effects) were completely offset by decreased net radiation (inducing cooling effects), which were responsible for the net cooling effects. However, a warming effect with 1.0 ± 0.1 W/m2 was observed in summer. This is because that magnitude of decreased latent heat is greater than that of decreased net radiation in summer. These findings will enrich our understanding for the biophysical effect of conversion from croplands to grasslands in water-limited temperate regions.

Understanding the changes in snow cover is essential for biological and hydrological processes in the Tibetan Plateau (TP) and its surrounding areas. However, the changes in snow cover phenology over the TP have not been well documented. Using Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products and the Interactive Multi-sensor Snow and Ice Mapping System (IMS) data, we reported daily cloud-free snow cover product over the Tibetan Plateau (TP) for 2000–2015. Snow cover start (SCS), melt (SCM) and duration (SCD) dates were calculated for each hydrological year, and their spatial and temporal variations were analyzed with elevation variations. Our results show no widespread decline in snow cover over the past fifteen years and the trends of snow cover phenology over the TP has high spatial heterogeneity. Later SCS, earlier SCM, and thus decreased SCD mainly occurred in the areas with elevation below 3500 m a.s.l., while regions in central and southwestern edges of the TP showed advanced SCS, delayed SCM and consequently longer SCD. The roles of temperature and precipitation on snow cover penology varied in different elevation zones, and the impact of both temperature and precipitation strengthened as elevation increases.

The biophysical effect of land use conversion plays a significant role in regulating climate change. Owing to albedo and evapotranspiration (ET) change, the effect of energy budget difference on land surface temperature (LST) is important but unclear among contrasting land use types, especially in temperate semi-arid regions. Based on moderate-resolution imaging spectroradiometer (MODIS) data, we compared the differences in albedo, ET, and LST between cropland and grassland (CR-GR), and between planted forest and grassland (PF-GR) in the Horqin Sandy Land of Inner Mongolia, an agro-pastoral ecotone of northern China. Our main objective was to explore the magnitude and direction of albedo and ET change during the growing season and, subsequently, to estimate the biophysical effects on LST as a result of land use and land cover change. Our results indicate no significant difference in mean monthly albedo for CR-GR and PF-GR. Cropland lost more water through ET and significantly decreased daytime LST compared with grassland from July to September, but no significant differences in ET and LST were observed for PF-GR in any month. The biophysical climate effects were more pronounced for CR-GR compared with PF-GR. The response of LST to the changes in energy budget confirmed that ET was the critical driving factor relative to albedo. Compared with grassland, cropland and planted forest tended to cool the land surface by 5.15°C and 1.51°C during the growing season, respectively, because of the biophysical effects. Our findings suggest the significance of local-scale biophysical effect on climate variation after land use conversion in semi-arid regions.

The fraction of photosynthetically active radiation (FPAR) as a key biophysical variable plays an important role in gross primary production (GPP) modeling. In this study, we employed six different FPAR derived from MODIS FPAR, leaf area index (LAI), normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), respectively, which were widely used for modeling GPP in previous studies. Flux GPP was collected from ChinaFLUX, covering four forest sites and four non-forest sites. Correlation analysis was used to evaluate the potential of different FPAR to model GPP. Our results showed that FPAR derived from EVI had the stronger correlation with flux GPP than MOD15 FPAR and FPAR derived from LAI, but FPAR derived from NDVI was not observed. This study highlights the potential of FPAR derived from EVI in GPP modeling.

The photochemical reflectance index (PRI) has been suggested as an indicator of light use efficiency (LUE) for various plant functional types at both leaf and canopy scales. However, the application of PRI for the estimation of LUE has been always contaminated by canopy structures and directional effects associated with changes in illumination and viewing angles. The most important variable that controls the structural associated characteristics is the leaf area index (LAI), which is a major determinant of the absorbed photosynthetic active radiation (APAR) by a canopy. Using ground measured PRI and LUE over three growing seasons on winter wheat in China during 2005–2007, we found that canopy LAI overall explained 66% (p < 0.001) variance of PRI, indicating the essential of removing influences of external structural factors on PRI. Suggested by this, we defined the structural-related signal in PRI (sPRI) as a function of LAI and consequently, the residual PRI (rPRI=PRI-sPRI) would be independent on canopy structural characteristics. Our results showed that a mixed non-linear model using rPRI and variety and sampling time as fixed and random effects, respectively, had an improved accuracy in the estimation of LUE over PRI with increased R2 for both the overall dataset and data of each year. This finding is also supported when validated at flux tower site that the MODIS-derived daily rPRI can provide improved modeling of LUE (R2=0.49, p

Rapid urbanization has occurred in northwestern China, threatening the sustainability of its fragile dryland ecosystems. A lack of precise urban land-cover information has limited our understanding on the urbanization in the dryland. Here, we examined urban land-cover changes from 2000 to 2014 in 21 major cities that comprise over 50% of the developed land in arid China, using Landsat Enhanced Thematic Mapper Plus and Operational Land Imager data, and a hybrid classification method. The 15-m resolution urban land-cover products (including impervious surfaces, vegetation, bare soil, and water bodies) had an overall accuracy of 90.37%. Based on these new land use products, we found the urbanization in arid China was characterized by the dramatic expansion of impervious surface (+13.23%) and reduction of bare soil (−13.41%), while the proportions of vegetation (+0.27%) and water (−0.10%) remained stable. The observed dynamic equilibrium of vegetated ratio implies an increasing harmonization of urbanization and greening, which was particularly important for the sustainability of fragile urban ecosystems in arid regions. From an economic perspective, gross domestic product and population were significantly correlated with impervious surfaces, and oasis cities displayed a stronger ability to attract new residents than desert cities.

The biogeophysical effects of land cover change (LCC) on land surface temperature are largely understood, especially in tropical and boreal zones, but fewer studies have reported the biogeophysical consequences of ecological restoration of mining areas located in arid and semi-arid temperate zones. In this study, we employed 2000–2015 satellite-based net radiation (Rn) and latent heat flux (LE) data from mining areas (taking Dongsheng in Inner Mongolia as an example) and adjacent ecological restoration areas to understand the biogeophysical effect of revegetation around mining areas. The results showed that revegetation increased Rn (0.25 ± 0.17 W/m2) and LE (0.43 ± 0.26 W/m2). The warming effect of increased Rn (∆Rn), however, was completely offset by the cooling effect of increased LE (∆LE), thus revegetation caused an overall cooling effect of −0.18 ± 0.17 W/m2. Land surface temperature (LST) from mining areas and adjacent ecological restoration areas also showed that revegetation decreased LST (∆LST being −0.2 ± 0.1 K). The difference of ∆Rn and ∆LE explained 27% of the ∆LST variations. These findings reinforce our understanding for the potential climatic benefits of the ecological restoration around mining areas in arid and semi-arid temperate zones.

Monitoring the spring green-up date (GUD) has grown in importance for crop management and food security. However, most satellite-based GUD models are associated with a high degree of uncertainty when applied to croplands. In this study, we introduced an improved GUD algorithm to extract GUD data for 32 years (1982–2013) for the winter wheat croplands on the North China Plain (NCP), using the third-generation normalized difference vegetation index form Global Inventory Modeling and Mapping Studies (GIMMS3g NDVI). The spatial and temporal variations in GUD with the effects of the pre-season climate and soil moisture conditions on GUD were comprehensively investigated. Our results showed that a higher correlation coefficient (r = 0.44, p