Search

Northeast China plays a pivotal role in producing commodity grains. The precipitation and temperature distribution during the growth season is impacted by geographical and climate factors, rendering the region vulnerable to drought. However, relying on a single index does not reflect the severity and extent of drought in different regions. This research utilised the random forest (RF) model for screening remote sensing indices. Relative soil moisture (RSM) was employed to compare seven commonly used indices: the temperature vegetation dryness index (TVDI), vegetation supply water index (VSWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), multi-band drought index (MBDI), and normalised difference drought index (NDDI). The effectiveness of these indices for monitoring drought during different developmental stages of spring maize was evaluated. Trend rates were employed to investigate the temporal changes in drought patterns of spring maize from 2003 to 2020, and the Sen + Mann–Kendall test was used to analyse spatial variations. The results showed the following: (1) The seven remote sensing indices could accurately track drought during critical growth stages with the TVDI demonstrating higher applicability than the other six indices. (2) The application periods of two TVDIs with different parameters differed for the drought monitoring of spring maize in different developmental periods. The consistency and accuracy of the normalised difference vegetation index (NDVI)-based TVDI (TVDIN) were 5.77% and 34.62% higher than those of the enhanced vegetation index (EVI)-based TVDI (TVDIE), respectively, in the early stage. In contrast, the TVDIE exhibited 13.46% higher consistency than the TVDIN in the middle stage, and the accuracy was the same. During the later stage, the TVDIE showed significantly higher consistency and accuracy than the TVDIN with consistency increases of 9.61% and 38.64%, respectively. (3) The drought trend in northeast China increased from 2003 to 2020, exhibiting severe spring drought and a weakening of the drought in summer. The southern, southwestern, and northwestern parts of northeast China showed an upward drought trend; the drought-affected areas accounted for 37.91% of the study area. This paper identified the most suitable remote sensing indices for monitoring drought in different developmental stages of spring maize. The results provide a comprehensive understanding of the spatial–temporal patterns of drought during the past 18 years. These findings can be used to develop a dynamic agricultural drought monitoring model to ensure food security. [ABSTRACT FROM AUTHOR]

The mining area in the Muli region, Qinghai Province, China, is an important source of water and an ecological security barrier in the Qilian Mountains region and has a very important ecological status. A series of ecological problems such as vegetation degradation and loss of biodiversity caused by mining have attracted widespread attention. In this paper, we used Landsat secondary data from 2000 to 2021 from the Muli region to obtain the spatial and temporal distribution characteristics of the vegetation in the Muli region by inversion of the fractional vegetation cover, above-ground biomass and the land surface phenology to comprehensively analyze the ecological changes in the vegetation in the Muli region. The results showed the following: (1) the above-ground biomass and cover of grassland in the Muli region showed a decreasing trend between 2000 and 2021, with a particularly pronounced decrease in grassland cover between 2009 and 2016; (2) the start of the vegetation growth cycle, i.e., the beginning of the vegetation growing season (SOG) became more advanced, the end of the vegetation growing season (EOG) was delayed, and the length of the growing cycle (LOG) became longer for most of the vegetation in the Muli region; (3) the results of this comprehensive analysis showed that the grassland in the Muli region showed dynamic changes with complex characteristics from 2000 to 2021, and anthropogenic disturbances had some influence on ecological indicators such as fractional vegetation cover and biomass. The extension of the vegetation growing season might be related to climate change. Based on the results of this paper, it is recommended to utilize biomass and fractional vegetation cover as indicators to assess the grass growth status of mining sites. This study analyzed the spatial and temporal characteristics of grasslands in the Muli area with several indicators, which will help relevant departments continue to improve and optimize ecological restoration measures. In addition, this study provides a reference for achieving comprehensive restoration of the ecological environment and ecological functions in mining areas. [ABSTRACT FROM AUTHOR]

The Northeast China maize belt is one of the three major golden maize belts in the world and has been severely affected by climate change, however, the evapotranspiration (ET) partitioning is not clear. It is important to study ET and its components under climate change. In this paper, the water balance model SOILWAT2 was used to estimate ET partitioning in drought and humid years, seasons, and maize growth stages from 1989 to 2018 over rainfed maize farmland. The results indicated that the SOILWAT2 model performed well for the prediction of ET and its partitioning compared with eddy covariance measurements. The mean yearly ET, transpiration (T), soil evaporation (Es), and canopy interception evaporation (Int) were 432.3 mm, 197.6 mm, 204.7 mm and 19.2 mm, respectively, over 30 years. Es/ET was 6.3% lower in drought years than in humid years, T/ET was conversely higher (6.2% higher in drought years). There was no clear difference of Int/ET between humid and drought years. In the growing season, T/ET, Es/ET, and Int/ET varied from 40.0% to 75.0%, 22.8% to 55.7%, and 0.7% to 7.0%, respectively. T/ET decreased along with the growth of maize and was greatest at the greening–jointing stage. Es/ET was smallest at the greening–jointing stage. We found a power function relationship between T/ET, Es/ET, and leaf area index (LAI) and above-ground biomass. Our results indicated that for the rainfed farmland, drought may limit maize yield by increasing water loss of maize through increasing T under climate change conditions. Therefore, securing food yield will depend on increases in water-use efficiency and other adaptive strategies, such as drought-resistant varieties, and irrigation. [ABSTRACT FROM AUTHOR]

Drought is one of the most common natural threats to agricultural production worldwide. Few studies have studied the effects of agricultural practices on drought mitigation at a regional scale over a long period. This paper analyzes the spatiotemporal characteristics in the agricultural drought-affected area change index (ADAC), which was developed to assess the drought mitigation. The linear regression method was used to investigate the impact factors on the change of ADAC in the three main winter wheat provinces of northern China. The results showed that the average ADAC during the main growing season in the study area was approximately −61.5% over the past 38 years, which indicated a great decrease of the agricultural drought-affected area. The significant decreasing trends of ADAC values across the study area during 1981–2000 could be explained by the area percentage equipped for irrigation (APEI) by 49.2–89.7%. There was a lack of pronounced change trends of ADAC during 2001–2018, implying that the positive effects of irrigation infrastructure in the plain area might reach a plateau under the constraints of available water resources, and other agricultural practices need to be investigated in the future. This research provides helpful decision information on drought adaptation management and water conservation project planning. [ABSTRACT FROM AUTHOR]

Exploring the evolution of vegetation cover and its drivers in the Ferghana Basin helps to understand the current ecological status of the Ferghana Basin and to analyze the vegetation changes and drivers, with a view to providing a scientific basis for regional ecological and environmental management and planning. Based on GIMMS NDVI3g and meteorological data, the spatial and temporal evolution characteristics of NDVI were analyzed from multiple perspectives with the help of linear trend and Mann–Kendall (MK) test methods using arcgis and the R language spatial analysis module, combined with partial correlation coefficients and residual analysis methods to analyze the impacts of climate change and human activities on the regional vegetation cover of the Ferghana Basin from 1982 to 2015. NDVI driving forces. The results showed the following: (1) The growing season of vegetation NDVI in the Ferghana Basin showed an increasing trend in the 34-year period, with an increase rate of 0.0044/10a, and the spatial distribution was significantly different, which was high in the central part of the country and low in the northern and southern parts of the country. (2) Temperature and precipitation simultaneously co-influenced the vegetation NDVI growth season, with most of the temperature and precipitation contributing in the spring, most of the temperature in the summer being negatively phased and the precipitation positively correlated, and most of the temperature and precipitation in the fall inhibiting vegetation NDVI growth. (3) The combined effect of climate change and human activities was the main reason for the overall rapid increase and great spatial variations in vegetation NDVI in China, and the spatial distribution of drivers, namely human activities and climate change, contributed 44.6% to vegetation NDVI in the growing season. The contribution of climate change and human activities to vegetation NDVI in the Ferghana Basin was 62.32% and 93.29%, respectively. The study suggests that more attention should be paid to the role of human activities and climate change in vegetation restoration to inform ecosystem management and green development. [ABSTRACT FROM AUTHOR]

It is essential to monitor the dynamics of vegetation at different scales in space and time to promote the sustainable development of terrestrial ecosystems. We used the Google Earth Engine (GEE) cloud platform to perform a comprehensive analysis of the changes in normalized difference vegetation index (NDVI) Mann-Kendall (MK) + Sen trend in the hinterland region of the Maowusu sandland in China over the last two decades. We performed bias-correlation studies using soil and climate data. Furthermore, we performed a partial Mantel test to analyze the spatial and temporal fluctuations of vegetation health-related indices. Additionally, we developed a logistic dual model of the phenology index using the Lenvenberg–Marquardt technique. The objective was to uncover the factors contributing to the regional shifts in vegetation dynamics. We provide a comprehensive analytic method designed to monitor vegetation over some time and forecast its future recovery. The findings indicate that over the past 20 years, more than 90% of the regional NDVI in the study area has exhibited a consistent and significant upward trend. This trend is primarily influenced by the adverse impact of temperature and the beneficial impact of precipitation. Additionally, long-term phenological indicators in the study area reveal that the vegetation's growth cycle commences on the 125th day of the year and concludes on the 267th day of the year. This suggests that the shorter duration of the vegetation's growth season may be attributed to the local climate and unfavorable groundwater depth conditions. levated temperatures throughout the next spring and autumn seasons would significantly affect the wellbeing of plants, with soil moisture being a crucial determinant of plant development in the examined region. This study presents a wide range of analytical tools for monitoring vegetation over a long period and predicting its future recovery. It considers factors such as vegetation health, phenology, and climatic influences. The study establishes a solid scientific foundation for understanding the reasons behind regional vegetation changes in the future. [ABSTRACT FROM AUTHOR]

The response of vegetation phenology to urbanization has become a growing concern. As impervious surfaces change as urbanization advances, the variation in vegetation phenology at the dynamic urbanization level was analyzed to significantly quantify the impact of urbanization processes on vegetation phenology. Based on the MOD13Q1 vegetation index product from 2001 to 2020, vegetation phenology parameters, including the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (GSL), were extracted, and the spatial–temporal variation in vegetation phenology, as well as its response to urbanization, was comprehensively analyzed. The results reveal that (1) from 2001 to 2020, the average rates of change for the SOS, EOS, and GSL were 0.41, 0.16, and 0.57 days, respectively. (2) The vegetation phenology changes showed significant spatial–temporal differences at the urbanization level. With each 10% increase in the urbanization level, the SOS and EOS were advanced and delayed by 0.38 and 0.34 days, respectively. (3) The urban thermal environment was a major factor in the impact of urbanization on the SOS and EOS. Overall, this study elucidated the dynamic reflection of urbanization in phenology and revealed the complex effects of urbanization on vegetation phenology, thus helping policymakers to develop effective strategies to improve urban ecological management. [ABSTRACT FROM AUTHOR]

Zizania latifolia (Z. latifolia) is a popular aquatic vegetable with various nutrients in south China, but little is known about its cultivars and growing seasons in terms of the nutritional components. This work aims to characterize the nutrients of five Z. latifolia cultivars in different growing seasons. The results showed that Z. latifolia samples differed in terms of chemical parameters, which were significantly affected by variety, growing season, and their interaction. Zhejiao No. 8, harvested in the autumn, stood out with the highest levels of vitamin C. Tangxiajiao and Zhejiao No. 1 contained the highest values of total soluble solids, reducing sugar, soluble proteins, and amino acids. Significant differences were observed between the autumn Z. latifolia and spring samples; the former were of higher quality than the latter based on hierarchical clustering analysis and principal component analysis. Moreover, total amino acids (TAA) and glutamic acid (GLU) were selected as the key indicators for Z. latifolia comprehensive quality by multiple linear regression analysis. This study provides essential information on Z. latifolia quality characteristics corresponding to cultivars and growing seasons, which lays the foundation for promoting the quality improvement of Z. latifolia scientifically. [ABSTRACT FROM AUTHOR]

The accurate monitoring of vegetation phenology is critical for carbon sequestration and sink enhancement. Vegetation phenology in arid zones is more sensitive to climate responses; therefore, it is important to conduct research on phenology in arid zones in response to global climate change. This study compared the applicability of the enhanced vegetation index (EVI), which is superior in arid zones, and global solar-induced chlorophyll fluorescence (GOSIF), which has a high spatial resolution, in extracting vegetation phenology in arid zones, and explored the mechanism of the differences in the effects of environmental factors on the phenology of different vegetation types. Therefore, this study employed a global solar-induced chlorophyll fluorescence (GOSIF) dataset to determine the start and end of the vegetation growth season (SOSSIF and EOSSIF, respectively) in the arid zone of Northwest China from 2001 to 2019. The results were compared with those from the EVI-based MODIS climate product MCD12Q2 (SOSEVI and EOSEVI). Variations in the sensitivity of these climatic datasets concerning temperature, precipitation, and standardised precipitation evapotranspiration index (SPEI) were assessed through partial correlation analysis. Results: Compared to the MCD12Q2 climatic products, SOSSIF and EOSSIF closely matched the observed climate data in the study area. Spring onset was delayed at higher altitudes and latitudes, and the end of the growing season occurred earlier in these areas. Both SOSSIF and EOSSIF significantly advanced from 2001 to 2019 (trend degrees −0.22 and −0.48, respectively). Spring vegetation phenology was chiefly influenced by precipitation while autumn vegetation phenology was driven by both precipitation and SPEI. GOSIF-based climate data provides a more accurate representation of vegetation phenology compared to traditional vegetation indices. The findings of this study contribute to a deeper understanding of the potential ability of EVI and SIF to reveal the influence of vegetation phenology on the carbon cycle. [ABSTRACT FROM AUTHOR]

With global climate change, changes in vegetation phenology have become increasingly evident. Horqin Sandy Land is located near the eastern part of the West Liaohe River. It is the largest sandy land in China and its ecological environment is fragile. Investigating the changes in vegetation phenology in these sandy areas and determining the relationship between vegetation phenology and meteorological factors are of great importance for predicting the impacts of future climate change and understanding the response mechanisms of ecosystems. In this study, we used the time series of the Normalized Difference Vegetation Index (NDVI) from 2000 to 2021 and extracted the vegetation phenology in the Horqin Sandy Land using high-order curve fitting methods, including the start date of the growing season (SOS), the end date of the growing season (EOS), and the length of the growing season (LOS). We analyzed their temporal variation and used partial correlation analysis to determine their relationship with meteorological factors (temperature and precipitation). In addition, we compared the phenology and microclimate of forest and grassland within the study area. In the Horqin Sandy Land, the vegetation SOS was concentrated between the 115th and 150th day, the EOS was concentrated between the 260th and 305th day, and the LOS ranged from 125 to 190 days. Over the past 22 years, the SOS, EOS, and LOS of vegetation in the Horqin Sandy Land showed trends of delay, shift, and extension, with rates of change of 0.82 d/10a, 5.82 d/10a, and 5.00 d/10a, respectively. The start date of the growing season in the Horqin Sandy Land was mainly influenced by precipitation in April of the current year, while the end date was mainly influenced by precipitation in August of the current year. Overall, the SOS in the forested areas of the Horqin Sandy Land was slightly later than in the grasslands, but the EOS in the forested areas was significantly later than in the grasslands, resulting in a longer LOS in the forests. In addition, annual precipitation and the rate of precipitation increase were higher in the forested areas than in the grasslands, but soil temperature was higher in the grasslands than in the forests. Vegetation phenology in the Horqin Sandy Land has undergone significant changes, mainly manifested in the delayed end date of the growing season, the extended length of the growing season, and the differences between forest and grassland. This indicates that climate change has indeed affected phenological changes and provides a theoretical basis for subsequent ecological restoration and desertification prevention efforts in the region. [ABSTRACT FROM AUTHOR]

Crop mapping at an earlier time within the growing season benefits agricultural management. However, crop spectral information is very limited at the early crop phenological stages, leading to difficulties for within-season crop identification. In this study, we proposed a deep learning-based fusion method for crop mapping within the growing season, which first learned a priori information (i.e., pre-season crop types) from historical crop planting data and then integrated the a priori information with the satellite-derived crop types estimated from spectral times-series data. We expect that preseason crop types provided by crop rotation patterns is an effective supplement to spectral information to generate reliable crop maps in the early growing season. We tested the proposed fusion method at three representative sites in the U.S. with different crop rotation intensities and one site with cloudy weather conditions in the Sichuan Province of China. The experimental results showed that the fusion method incorporated the strengths of pre-season crop type estimates and the spectral-based crop type estimates and thus achieved higher crop classification accuracy than the two estimates throughout the growing season. We found that pre-season crop estimates had a higher accuracy in the scenarios with either nearly continuous planting or half-time planting of the same crop. In addition, the historical crop type data strongly affected the performance of pre-season crop estimates, suggesting that high-quality historical crop planting data are particularly important for crop identification at earlier times in the growing season. Our study highlighted the great potential for near real-time crop mapping through the fusion of spectral information and crop rotation patterns. [ABSTRACT FROM AUTHOR]

The terrestrial gross primary productivity (GPP) has increased over the past two decades. However, the climatic attribution and the physiological and phenological processes that control the trends in the GPP are still unclear. Here, we used remote-sensing-based vegetation GPP and phenology datasets, analyzed the spatial and temporal variation in the GPP, investigated the influence of the growing season length (GSL) and the maximum value of gross primary productivity (GPPmax) on the annual GPP, and quantified the effect of climate variables on the annual GPP. Our results identified a significant increase in the annual GPP (11.97 gC/m2/yr) during 2001–2020 in China's deciduous forest. The GPPmax trend dominated the trends in the GPP, when compared with the GSL. Moreover, climate warming in summer contributes to the increase in the GPP and the GPPmax, while the extension of the GSL is primarily due to the temperature rise in spring. The annual GPP of the planted forest showed a higher increasing rate than the natural forest, due to the significant enhancement of the GPPmax and the high sensitivity of the GSL to climatic factors in the planted forest. Our findings provide a new perspective on the phenological and physiological causes of the trends in the GPP, and emphasize the importance of capturing the variability in the GPPmax when modeling the GPP. [ABSTRACT FROM AUTHOR]

Leveraging circular regression, this study analyzed phenological data from China spanning the period 2003 to 2015, meticulously examining the effects of temperature, precipitation, and CO2 concentrations on the phenological patterns of woody and herbaceous plants. For woody plants, the results showed that rising temperatures and increased precipitation notably advanced early growth phases, such as budburst, leaf unfolding, and first flowering (p < 0.001). Specifically, CO2 concentrations had a pronounced impact on the leaf unfolding phase (p < 0.001). In contrast, autumnal events, particularly fruit maturity, autumn coloring, and leaf fall, were delayed by warmer temperatures and higher precipitation (p < 0.001), Of these events, only fruit maturity demonstrated sensitivity to CO2 concentration variations. In the realm of herbaceous plants, elevated temperatures and precipitation collectively hastened the budburst phase (p < 0.001), which is an effect further accentuated by high CO2 levels (p < 0.001). Moreover, rising temperatures and augmented precipitation were instrumental in advancing the flowering phase (p < 0.001). Conversely, warmer conditions slowed down the fruiting process (p < 0.001), with this delay somewhat mitigated by the effects of increased precipitation. Interestingly, while CO2 concentrations had negligible influence on the flowering and fruiting stages, they noticeably delayed seed dispersal and the initiation of senescence (p < 0.001). Overall, the prevailing trend suggests that plants, whether woody or herbaceous in nature, tend to prolong their growth season under warmer and more humid conditions. The influence of CO2 concentration, however, is contingent upon the specific phenological phase and plant type. Our findings emphasize the nuanced and stage-specific responses of plant phenology to temperature, precipitation, and CO2, highlighting the value of using circular regression in ecological studies. [ABSTRACT FROM AUTHOR]

Improved understanding of the responses of stem radial growth to climates is necessary for modeling and predicting the response of forest ecosystems to future climate change. We used dendrochronological methods to study climate effects on the radial growth of a subalpine deciduous conifer, Larix potaninii. Tree-ring residual chronologies were developed for five sites at the upper distributional limits in the Central Hengduan Mountains, Southwestern China. Redundancy analysis and response function were used to compare inter-annual variability in growth sensitivity among the chronologies and to identity key climatic factors controlling tree radial growth. The results showed that both precipitation and temperature influenced tree growth, and response patterns were consistent for five chronologies. During the current year's early growing season (Tmean in May and Tmax in June), temperature positively affected the radial growth of L. potaninii, while September Tmin and October precipitation in the previous year and May and June precipitation in the current year all had negative impacts on its radial growth. L. potaninii growth appeared to be mainly limited by photothermal conditions in May and June. In the context of increasing CO2 concentrations accompanied with warmer temperatures, future climate change would likely stimulate the radial growth of L. potaninii in Central Hengduan Mountain. [ABSTRACT FROM AUTHOR]

China is the world's largest rice producer. Thus, the stability of rice production plays a decisive role in food security. Among the types of rice, double rice (including early rice and late rice) accounts for the largest proportion of rice in China. Climate change is widely expected to affect rice yields. Studying the response of double rice yield to climate change will benefit strategic decisions related to future crop adaptation. In this paper, the relationship between climate factors and the yield of double rice during 1992–2013 in south China was analysed to determine the responses of double rice yield to climate change. The results showed that the daily average air temperature during the early rice and late rice growing seasons increased by 0.34 °C and 0.68 °C, 0.29 °C and 0.67 °C, and 0.11 °C and 0.31 °C per 10-year period in the northern subtropical zone (NST), middle subtropical zone (MST) and south subtropical zone (SST), respectively, in the last 20 years. The change trend in solar radiation was not obvious, but it fluctuated greatly. A 1 °C increase in average air temperatures decreased early rice yield by 5.36% and 2.16% in SST and MST, respectively; decreased late rice yield by 0.75% and 1.43% in MST and NST, respectively; and increased late rice yield by 3.93% in SST. A solar radiation increases of 100 MJ m−2 increased early rice yield by 1.02%, 1.54% and 1.71% in SST, MST and NST, respectively, and decreased late rice yield by 0.89% in SST. We found that annual average temperatures of 17.3 °C and 18.6 °C were the early rice and late rice yield variation thresholds, respectively; in addition, above the background temperature in south China, the early rice yield will decrease and the late rice yield will increase. [ABSTRACT FROM AUTHOR]

Machine learning comprises a group of powerful state-of-the-art techniques for land cover classification and cropland identification. In this paper, we proposed and evaluated two models based on random forest (RF) and attention-based long short-term memory (A-LSTM) networks that can learn directly from the raw surface reflectance of remote sensing (RS) images for large-scale winter wheat identification in Huanghuaihai Region (North-Central China). We used a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images over one growing season and the corresponding winter wheat distribution map for the experiments. Each training sample was derived from the raw surface reflectance of MODIS time-series images. Both models achieved state-of-the-art performance in identifying winter wheat, and the F1 scores of RF and A-LSTM were 0.72 and 0.71, respectively. We also analyzed the impact of the pixel-mixing effect. Training with pure-mixed-pixel samples (the training set consists of pure and mixed cells and thus retains the original distribution of data) was more precise than training with only pure-pixel samples (the entire pixel area belongs to one class). We also analyzed the variable importance along the temporal series, and the data acquired in March or April contributed more than the data acquired at other times. Both models could predict winter wheat coverage in past years or in other regions with similar winter wheat growing seasons. The experiments in this paper showed the effectiveness and significance of our methods. [ABSTRACT FROM AUTHOR]

Grassland ecosystems are an important component of global terrestrial ecosystems and play a crucial role in the global carbon cycle. Therefore, it is important to study the carbon dioxide (CO2) process in the Middle Tien Shan grassland ecosystem, which can be regarded as a typical representative of the mountain grasslands in Xinjiang. Eddy covariance (EC) and the global carbon fluxes dataset (GCFD) were utilized to continuously monitor the Middle Tien Shan grassland ecosystem in Xinjiang throughout the 2018 growing season. The findings revealed notable daily and monthly fluctuations in net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (Reco). On a daily basis, there was net absorption of CO2 during the day and net emission during the night. The grassland acted as a carbon sink from 6:00 to 18:00 and as a carbon source for the remaining hours of the day. On a monthly scale, June and July served as carbon sinks, whereas the other months acted as carbon sources. The accumulated NEE, GPP, and Reco during the growing season were −329.49 g C m−2, 779.04 g C m−2, and 449.55 g C m−2, respectively. On the half-hourly and daily scales, soil temperature (Ts) was the main contributor to CO2 fluxes and had the greatest influence on the variations in CO2 fluxes. Additionally, air temperature (Ta) showed a strong correlation with CO2 fluxes. The grassland ecosystems exhibited the strongest CO2 uptake, reaching its peak at soil temperatures of 25 °C. Moreover, as the air temperatures rose above 15 °C, there was a gradual decrease in NEE, while CO2 uptake increased. The applicability of GCFD data is good in the grassland ecosystem of the Middle Tien Shan Mountains, with correlations of 0.59, 0.81, and 0.73 for NEE, GPP, and Reco, respectively, compared to field observations. In terms of remote sensing spatial distribution, the Middle Tien Shan grassland ecosystem exhibits a carbon sink phenomenon. [ABSTRACT FROM AUTHOR]

Non-structural carbohydrates' (NSCs) allocation pattern is closely related to environmental factors, plant metabolism, and xylem function. At the same time, we know little about whole-tree NSC allocation patterns in different seasons, especially the high-elevation species which suffer from environmental stress. We examined the concentration of NSCs in various parts of Faxon fir trees (needles, branches, trunks, and roots) at five elevations (2800 m, 3000 m, 3200 m, 3400 m, and 3600 m) over four months (July 2019, October 2019, January 2020, and April 2020). The goal was to understand how NSC allocation patterns vary by location and time in high-elevation species and what factors contribute to these variations. The results showed that the needles had the highest concentration, followed by roots, branches, and trunks. The NSC concentration was highest in January 2020 and lowest in July 2019. The total non-structural carbohydrates (TNSCs) and soluble sugar concentrations of roots and needles were substantially higher in the cold (non-growing season) than in the warm (growing season) season. At different elevations, the soluble sugar concentrations in the needles and trunks remained the highest and lowest, respectively. Branches and roots' soluble sugar concentrations alternated and varied with the seasons at all elevations. Many factors, such as climate, morphological traits, and carbon content, affected the spatial and temporal patterns of non-structural carbohydrates, with temperature, plant moisture conditions, and carbon content being the main driving factors. Various factors' interaction mainly influenced NSCs' spatial and temporal patterns. Non-structural carbohydrates significantly improve the resistance of Faxon fir trees' terminal organs in adverse environments. [ABSTRACT FROM AUTHOR]

This study investigates the evolution of soil nitrogen (N) contents and forms along a 17-year wildfire chronosequence in the Daxing'an Mountains. Surface soil and subsoil samples were collected during different recovery periods after wildfires. Then, the mineral N (i.e., NH4+-N and NO3−-N) and amino acid-N (AAN) contents in the soil extracts were measured and used to calculate the different ratios as indicators of the N forms. The results showed that the NH4+-N, NO3−-N, and AAN contents increased immediately after the wildfire. With vegetation restoration, the NH4+-N and NO3−-N contents became similar to those of unburned forests nine years and two months after the wildfire, respectively. The AAN content was mostly recovered one year post-fire. The wildfire did not lead to substantial changes in the mineral N form, but the ratio significantly increased and recovered after nine years. The soil available N form was altered by wildfires. After the wildfire, the dominant available N form changed from equivalent AAN and mineral N to a predominance of AAN in the growing season, and the predominance of AAN decreased to varying degrees in the non-growing season. With the recovery of the white birch and Dahurian larch, AAN again became the dominant N form, but the predominance of AAN was low before the freeze-up. Our study demonstrates that wildfires directly affect the soil N contents and forms, and such effects could be diminished by the restoration of the soil environment and vegetation over time. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) represents total vegetation productivity and is crucial in regional or global carbon balance. The Northeast China (NEC), abundant in vegetation resources, has a relatively large vegetation productivity; however, under obvious climate change (especially warming), whether and how will the vegetation productivity and ecosystem function of this region changed in a long time period needs to be revealed. With the help of GPP products provided by the Global LAnd Surface Satellite (GLASS) program, this paper gives an overview of the regional feedback of vegetation productivity to the changing climate (including temperature, precipitation, and solar radiation) across the NEC from 1982 to 2015. Analyzing results show a slight positive response of vegetation productivities to warming across the NEC with an overall increasing trend of GPPGS (accumulated GPP within the growing season of each year) at 4.95 g C/m2. yr−2 over the last three decades. More specifically, the growth of crops, rather than forests, contributes more to the total increasing productivity, which is mainly induced by the agricultural technological progress as well as warming. As for GPP in forested area in the NEC, the slight increment of GPPGS in northern, high-latitude forested region of the NEC was caused by warming, while non-significant variation of GPPGS was found in southern, low-latitude forested region. In addition, an obvious greening trend, as reported in other regions, was also found in the NEC, but GPPGS of forests in southern NEC did not have significant variations, which indicated that vegetation productivity is not bound to increase simultaneously with greening, except for these high-latitude forested areas in the NEC. The regional feedback of vegetation productivity to climate change in the NEC can be an indicator for vegetations growing in higher latitudes in the future under continued climate change. [ABSTRACT FROM AUTHOR]

Understanding the impact of water availability on vegetation growth in the context of climate change is crucial for assessing the resilience of vegetation to environmental shifts. In this study, the relationship between vegetation growth and water availability was studied using a variety of indicators. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and Solar-Induced Chlorophyll Fluorescence (SIF) were utilized as vegetation growth indicators, while the standardized precipitation evapotranspiration index (SPEI) and soil moisture indicators served as water use indices. To investigate the vegetation response to water deficit in the Loess Plateau during the growing season from 2000 to 2020, Spearman's rank correlation coefficients were calculated using a 5-year sliding window approach. The spatial and temporal heterogeneity of vegetation response to water deficit during the growing seasons were also explored. The results showed that: (1) with the improvement of moisture conditions, vegetation growth recovered significantly, and there was no expansion trend for vegetation water deficit. (2) The most sensitive timescale of vegetation to water deficit was 6–8 months; the response degree and sensitivity of vegetation to water surplus and deficit were the highest from June to August; and broadleaved forest was the vegetation type most sensitive to water deficit in the early growing season, while grass was the vegetation type most sensitive to water deficit during the mid and late growing seasons. (3) Soil moisture emerged as the dominant factor influencing vegetation growth in the Loess Plateau, followed by precipitation, albeit to a lesser extent. These findings contribute to understanding the mechanism and characteristics of the response of vegetation to climate fluctuations induced by global climate change. [ABSTRACT FROM AUTHOR]

The management of crop production in a sandy soil "culture" is difficult, mainly due to its low soil-water-holding capacity, organic matter and poor fertilizer efficiency. Options to increase soil water and nutrient retention for these soils include the addition of surface mulch covers, amendment with biochar and the use of layers of a mixture of charcoal and compost material. Our objective was to measure the distribution of water and nutrients for layers of control 1 (CK1), control 2 (CK2) and compost material of different thicknesses (0.02, 0.05 and 0.10 m) buried 0.01 m from the surface in a column (0.2 m radius, 0.5 m height) filled with sand. The experiment was conducted in a greenhouse located at the Agricultural Science Training Base of Ningxia University, China. There were three replicates per treatment and one soil column per replicate. The soil columns were watered with 2 L via a surface drip emitter and 45 days later, soil samples were obtained in 0.01 m increments across the diameter and depth of 0.4 m, with a total of 12 samples per column. In each sample, we measured soil water, pH, electrical conductivity, ammonium and nitrate nitrogen and available P and K. The results showed that the distribution of water content and nutrient contents were centered on the dripper and diffused to its surroundings. Notably, the horizontal diffusion distance was smaller than that of the vertical direction. In the vertical direction, compared with control 1, adding compost changed the spatial distribution of WC and nutrients and had a greater impact on available potassium (AK) than on inorganic nitrogen (IN) and available phosphorus (AP). Compared with control 1, the composting treatment decreased the content of water in the 0–10 cm surface soil, reduced the electrical conductivity (EC) and nitrate nitrogen (NO3-N), C5 and C10 increased the available potassium. Moreover, composting treatments increased the electrical conductivity, available phosphorus, available potassium and nitrate nitrogen of the 10–30 cm substrate by 61–384%, 10–240%, 11–45% and 133–929%, respectively, when compared with control 1.The nutrients increased as the thickness of the compost interlayer increased. A principal component analysis (PCA) of the C5 and C10 treatments significantly distinguished them from control 1. A linear regression fitting analysis showed that the inorganic nitrogen, available potassium and total nutrients positively correlated with the water content and electrical conductivity of the sand. The 5 cm and 10 cm composting interlayers had a high water content and ability to conserve fertilizer for sand culture, but C10 caused an excessive accumulation of nutrients. Thus, it was concluded that a composting interlayer that was less than 5 cm reduced the base fertilizer input by 24–84%. All these results suggest that applying a composting interlayer of 5 cm could retain more suitable root zone water and fertilizer for the next crop season and provide technological support to reduce fertilizer inputs. [ABSTRACT FROM AUTHOR]

Engineering corridors on the Qinghai–Tibet Plateau have substantially modified the regional ecosystem functions and environment, resulting in changes in the alpine ecosystem. In addition, the building and operation of these engineering corridors have led to rapid permafrost degradation, which in turn has impacted local vegetation along these corridors. This study investigated vegetation changes and their driving factors by the methods of coefficient of variation, correlation analysis, and GeoDetector in a 30 km wide buffer zone at each side along the National Highway G214 (G214) at the northern and southern flanks of the Bayan Har Mountains in part of the source area of the Yellow and Yangtze rivers on the southern Qinghai Plateau, West China. The following results were obtained: (1) The Normalized Difference Vegetation Index in Growing Season (NDVIgs) rose slightly in 2010–2019, with an average annual change rate of 0.006/a. Patterns of NDVIgs along the G214 exhibited "low at the northern flank and high at the southern flank of the Bayan Har Mountains". (2) Spatially, average NDVIgs increased from the first buffer zone at the distance of 0–10 km from the highway centerline to the second buffer zone at 20–30 km perpendicularly away from the G214. Furthermore, the first buffer zone had the lowest coefficient of variation, possibly due to a low vegetation recovery as a result of the greatest influence of the G214 on NDVIgs at 0–10 km. (3) Furthermore, annual precipitation (AP) was the dominant factor for significantly (p < 0.01) and positively influencing the variations in NDVIgs (R = 0.75, p < 0.01). Additionally, NDVIgs was more strongly influenced by the two combined factors than any single one, with the highest q-value (0.74) for the interactive influences of AP and annual average air temperature (AAAT) and followed by that of the AP and mean annual ground temperature (MAGT) at the depth of zero annual amplitude (15 m). Evidently, the construction and operation of the G214 have directly and indirectly affected vegetation through changing environmental variables, with significant impacts on NDVIgs extended at least 20 km outwards from the highway. This study helps better understand the environmental impacts along the engineering corridors in elevational permafrost regions at mid and low latitudes and their management. [ABSTRACT FROM AUTHOR]

The sowing date and density are considered to be the main factors affecting crop yield. The determination of the sowing date and sowing density, however, is fraught with uncertainty due to the influence of climatic conditions, topography, variety and other factors. Therefore, it is necessary to find a comprehensive consideration of these factors to guide the production of winter rapeseed. A reliable crop model could be a crucial tool to investigate the response of rapeseed growth to changes in the sowing date and density. At present, few studies related to rapeseed model simulation have been reported, especially in the comprehensive evaluation of the effects of sowing date and density factors on rapeseed development and production. This study aimed to evaluate the performance of the AquaCrop model for winter rapeseed development and yield simulation under various sowing dates and densities, and to optimize the sowing date and density for agricultural high-efficient production in the Jianghuai Plain. Two years of experiments were carried out in the rapeseed growing season in 2020 and 2021. The model parameters were fully calibrated and the simulation performances in different treatments of sowing dates and densities were evaluated. The results indicated that the capability of the AquaCrop model to interpret crop development for different sowing dates was superior to that of sowing densities. For rapeseed canopy development, the RMSE for three sowing dates and densities scenarios were 7–22% and 16–23%, respectively. The simulated biomass and grain yield for different sowing dates treatments (RMSE: 0.8–2.1 t·ha−1, Pe: 0–35.3%) were generally better than those of different densities treatments (RMSE: 0.7–3.9 t·ha−1, Pe: 8.2–90%). Compared with other sowing densities, higher overestimation errors of the biomass and yield were observed for the low-density treatment. Adequate agreement for crop evapotranspiration simulation was achieved, with an R2 of 0.79 and RMSE of 26 mm. Combining the simulation results and field data, the optimal sowing scheme for achieving a steadily high yield in the Jianghuai Plain of east China was determined to be sowing in October and a sowing density of 25.0–37.5 plant·m−2. The study demonstrates the great potential of the AquaCrop model to optimize rapeseed sowing patterns and provides a technical means guidance for the formulation of local winter rapeseed production. [ABSTRACT FROM AUTHOR]

As the climate warms, the impact of drought on plants has increased. We aimed to construct a comprehensive drought index (CDI), coupling soil-vegetation-atmosphere drought and heat conditions based on multi-source information, and to combine it with static and dynamic drought hazard evaluation models to analyze the spatial and temporal distribution characteristics of agricultural drought disasters and hazards during the growing season (May to September) in Northeast China (NEC). The results demonstrated that the CDI could combine the benefits of meteorology (standardized precipitation evapotranspiration index, SPEI), vegetation (vegetation health index, VHI), and soil (standardized soil moisture condition index, SMCI) indices. This was performed using a relative weighting method based on the remote sensing data of solar-induced chlorophyll fluorescence (SIF) to determine the weights of SPEI, VHI, and SMCI. The CDI for drought monitoring has the advantages of broad spatial range, long time range, and high accuracy, and can effectively reflect agricultural drought; the growing season in NEC showed a trend of becoming drier during 1982–2020. However, the trends of the drought index, the impact range of drought events, and the hazard of agricultural drought all turned around 2000. The drought hazard was highly significant (p < 0.001) and decreased from 2000 to 2020. The frequency of drought disasters was the highest, and the hazard was the greatest in May. The best level of climatic yield anomalies in maize were explained by drought hazard in August (R2 = 0.28). In the center and western portions of the study area, farmland and grassland areas were where higher levels of hazard were most commonly seen. The dynamic hazard index is significantly correlated with climatic yield anomalies and can reflect the actual impact of drought on crop yield. The study results serve as a scientific foundation for drought risk assessment and management, agricultural planning, and the formulation of drought adaptation policies, as well as for ensuring food security in China. [ABSTRACT FROM AUTHOR]

Assessing vegetation phenology is very important for better understanding the impact of climate change on the ecosystem, and many vegetation index datasets from different remote sensors have been used to quantify vegetation phenology from a regional to global perspective. This study mainly analyzes the similarities and differences in phenology derived from GIMMS NDVI3g and MODIS NDVI datasets across different biomes throughout temperate China. We applied three commonly used methods to extract the start and end of the growing season (SOS and EOS) from two datasets between 2000 and 2015, and analyzed the spatio-temporal characteristics and trends of key phenological parameters between these two datasets in temperate China. Results showed that the multi-year mean GIMMS NDVI was higher than MODIS NDVI throughout most of temperate China, and the consistencies between GIMMS NDVI and MODIS NDVI for all biomes in the senescence phase were better than those in the green-up phase. NDVI differences between GIMMS and MODIS resulted in some distinctions between phenology derived from the two datasets. The results of SOS and EOS for three methods also showed wide discrepancies in spatial patterns, especially in SOS. For different biomes, differences of SOS in forests were obviously less than that in shrublands, grasslands-IM, grasslands-QT and meadows, whereas the differences of EOS in forests were relatively greater than that in SOS. Moreover, large differences of phenological trends were found between GIMMS and MODIS datasets from 2000 to 2015 in entire region and different biomes, and it is particularly noteworthy that both SOS and EOS showed a low proportion of the identical significant trends. The results suggested NDVI datasets obtained from GIMMS and MODIS sensors could induce the differences of the inversion of vegetation phenology in some degree due to the differences of instrumental characteristics between these two sensors. These findings highlighted that inter-calibrate datasets derived from different satellite sensors for some biomes (e.g., grasslands) should be needed when analyzing land surface phenology and their trends, and also provided baseline information for choosing different NDVI datasets in subsequent studies on vegetation patterns and dynamics. [ABSTRACT FROM AUTHOR]

The Temperature–Vegetation–Precipitation–Drought Index (TVPDI) has a good performance in drought monitoring in China. However, different regions have different responses to droughts due to terrain differences. In southeastern Tibet, the drought monitoring capacity of some drought indices without soil information has to be assessed on account of the poor sensitivity between temperature and soil humidity. Therefore, soil moisture was added to calculate a new drought index based on TVPDI in southeastern Tibet, named the Temperature–Vegetation–Soil-Moisture–Precipitation–Drought Index (TVMPDI). Then, the TVMPDI was validated by using the Standardized Precipitation Evapotranspiration Index (SPEI) and other remote sensing drought indices, including the Vegetation Health Index (VHI) and Scale Drought Conditions Index (SDCI), during the growing seasons of 2003–2018. The Standardized Precipitation Index (SPI) and SPEI were used to represent meteorological drought and Gross Primary Productivity (GPP) was used to represent agricultural drought. The relation between TVMPDI and these drought indices was compared. Finally, the time trends of TVMPDI were also analyzed. The relation coefficients of TVMPDI and SPEI were above 0.5. The correlations between TVMPDI and drought indices, including the Vegetation Health Index (VHI) and Scale Drought Conditions Index (SDCI), also had a good performance. The correlation between the meteorological drought indices (SPI and SPEI) and TVMPDI were not as good as for the TVPDI, but the temporal correlation between the TVMPDI and GPP was greater than that between the TVPDI and GPP. This indicates that the TVMPDI is more suitable for monitoring agricultural drought than the TVPDI. In addition, historical drought monitoring had values that were consistent with those of the actual situation. The trend of the TVMPDI showed that drought in the study area was alleviated from 2003 to 2018. Furthermore, GPP was negatively correlated with SPEI (r = −0.4) and positively correlated with Soil Moisture (SM) drought index (TVMPDI, SMCI) (r = 0.4) in the eastern part of the study area, which suggests that SM, rather than precipitation, could promote the growth of vegetation in the region. A correct understanding of the role of soil information in drought comprehensive indices may monitor meteorological drought and agricultural drought more accurately. [ABSTRACT FROM AUTHOR]

Since the implementation of the great western development strategy in 2000, the ecological environment in the western region of China has been significantly improved. In order to explore the temporal and spatial characteristics of vegetation coverage in the western region, this paper adopted the method of Maximum Value Composite (MVC) to obtain the mean Normalized Difference Vegetation Index (NDVI) of vegetation on the basis of the Moderate-resolution Imaging Spector audiometer (MODIS) data of 2000/2005/2010/2015/2018. Thereafter, the spatio-temporal differentiation characteristics of vegetation in western China were analyzed. The results show that: (1) According to the time characteristics of vegetation coverage in the western region, the average annual NDVI value of vegetation coverage in the growing season in the western region fluctuated between 0.12 and 0.15, among which that of 2000 to 2010 fluctuated more greatly but did not show obvious change trend. (2) Based on Sen trend and Mann-Kendall test analysis, the area of vegetation coverage improvement in the western region from 2000 to 2018 was larger than that of significant vegetation degradation. (3) From the perspective of global autocorrelation coefficient, Moran's I values were all positive from 2000 to 2018, which indicates that the vegetation coverage in the west showed strong positive autocorrelation in each period. According to the average value and coefficient of variation of vegetation coverage, the vegetation coverage was lower in 2000, its internal variation was smaller, and the vegetation coverage increased with time. According to the local spatial autocorrelation analysis, the vegetation coverage levels in different regions varied greatly. (4) The standard deviation ellipse method was used to study the spatial distribution and directional transformation of vegetation. It makes the result more intuitive, and the three levels of gravity center shift, direction shift, and angle shift were considered: the vegetation growth condition in the spatial aggregation area improved in 2015; the standard deviation ellipses in 2000 and 2018 overlapped and shifted eastward, which indicates that the vegetation coverage conditions in the two years were similar and got ameliorated. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LUCC) has been increasingly recognized as having important effects on climate systems. Paddy fields, one kind of artificial wetland, have seen a significant increase in the Sanjiang Plain, China since 2000 and have become the most typical LUCC at the regional scale. Against this background, in this paper, we discuss the effects of this artificial wetland increase on surface temperature, in addition to its driving mechanisms. Firstly, the spatiotemporal variations of land surface temperature (LST) and its two driving variables (albedo and latent heat flux (LE)) in the Sanjiang Plain are analyzed and assessed based on remote sensing observation information from 2001 to 2015. Our results from both spatial distribution difference and time series analysis show that paddy field expansion led to day-time cooling and night-time warming over the study area. However, the LST changes show different characteristics and magnitudes in the spring (May to June) compared to the other months of the growing season (July to September). The daytime cooling trend is found to be −0.3842 K/year and the warming trend at night 0.1988 K/year during the period 2001 to 2015, resulting in an overall cooling effect in May and June. In July–September, the LST changes have the same sign but a smaller magnitude, with a −0.0686 K/year temperature trend seen for the day-time and a 0.0569 K/year increase for the night-time. As a consequence, a pronounced decrease in the diurnal temperature range is detected in the growing season, especially in spring. Furthermore, albedo and LE are demonstrated to be very sensitive to land use changes, especially in the earlier periods of the growing season. Correlation analysis between LST and albedo and LE also indicates the dominant role played by evapotranspiration in paddy fields in regulating local temperature. [ABSTRACT FROM AUTHOR]

Previous research has documented a tight positive relationship between vegetation activity and growing season air temperature in China's temperate zone (TC). However, this relationship may change over time following alternations in other environmental factors. Using the linear regression analysis and the moving windows based on partial correlation analysis method, the temporal variations of responses of vegetation NDVI to rising air temperature during 1982–2015 in the TC were examined. The results showed that the interannual partial correlation between NDVI and air temperature (RNDVI−T, include RNDVI−Tmean, RNDVI−Tmax, and RNDVI−Tmin, represents the partial correlation between NDVI and Tmean, Tmax, and Tmin, respectively) for the growing season (GS) in a 17−year moving window showed a significant decreasing trend during the last 34 years, mainly due to decreasing RNDVI−T in summer and autumn. The area with a significant decrease of RNDVI−Tmean, RNDVI−Tmax, and RNDVI−Tmin for the GS approximately accounted for 52.36%, 45.63%, and 49.98% of the TC, respectively. For the seasonal patterns of RNDVI−T, the regions with a significant downward trend in all seasons were higher than those with a significant upward trend. We also found a more significant and accelerating decrease of RNDVI−T for warm years compared to cold years, implying a decoupling or even a reverse correlation between NDVI and air temperature with continuous climate warming over the TC. Overall, our study provided evidence that the impact of Tmean, Tmax, and Tmin on vegetation activities exhibited a weakening trend and cautioned using results from interannual time scales to constrain the decadal response of vegetation growth to future global warming. [ABSTRACT FROM AUTHOR]

This article scientifically studies the direct impact of climate problems on the time transition of reference crop evapotranspiration in the Xinjiang Autonomous Region of China from 1957 to 2017, which is conducive to formulating irrigation scheduling and adaptive capacity countermeasures. The objective of this study is to investigate the impacts of climate change on ETo for the cotton growing seasons. The meteorological data were collected from 48 meteorological stations in the region and analyzed using the Mann–Kendall test and linear trend. The results show the following points: (1) the ETo decreases from low to high elevations, and with the increase in northern latitude. (2) The annual mean ETo and average values of ETo during the growing seasons for cotton exhibited two abrupt changes in the period 1957–2017, with the first abrupt change in 1995 to 1999 and the second abrupt change in 2006 to 2011. (3) The ETo in Xinjiang of China demonstrates a decreasing trend during 1957–1996; a significant decreasing trend during 1997–2008; and a significant increasing trend during 2009–2017. [ABSTRACT FROM AUTHOR]

In recent decades, vegetation coverage and land use/land cover (LULC) have constantly changed, especially in southwest China. Therefore, it is necessary to conduct in-depth research into the temporal–spatial variation patterns of vegetation greening, LULC, and gross primary productivity (GPP). Here, we used remote sensing to analyze the spatial and temporal variation in the normalized difference vegetation index (NDVI) and GPP in the growing season under different LULCs in southwest China. Results showed: (1) From 2000–2019, the forest area in southwest China had increased by 2.1%, while the area of cropland and grassland had decreased by 3.2% and 5.5%, respectively. Furthermore, there are significant differences in spatial variation patterns. (2) NDVI and GPP in the growing season showed a general increasing trend (p < 0.01); vegetation coverage is dominated by high coverage to highest coverage and medium coverage to high coverage transfer. (3) Under different LULCs, the migration directions of NDVI and GPP were different. The center of gravity migration of highest and medium coverage shifted to the southeast by 1.69° and to the northwest by 1.81°, respectively. The results showed the ecosystem evolution and will help to guide the maintenance measure of ecosystem balance and sustainable development. [ABSTRACT FROM AUTHOR]

The Qinling Mountains are located on the dividing line between the north and the south of China. Mt. Taibai, the study site, is the highest peak in the Qinling Mountains and also the highest peak in eastern mainland China. At Mt. Taibai, several dendroecological studies have been conducted on the relationship between tree-ring indices and climatic factors using traditional statistical methods. In this study, the response of Larix chinensis Beissn radial growth to climatic factors was explored in the treeline area of Mt. Taibai using the process-based Vaganov–Shashkin-Lite (VSL) model for the first time. The conclusions were obtained according to the analysis of the L. chinensis tree-ring samples collected from both the northern and southern slopes of Mt. Taibai. The VSL model showed that temperature limits L. chinensis growth during the entire growing season, while the model did not indicate precipitation as a limiting factor. The model showed significant positive correlations between the simulated and observed tree-ring chronologies for 1959–2013, excluding the uppermost sample site on the northern slope. However, the model performance deteriorated with increasing altitude, which may be due to the decreased sensitivity of radial growth to climatic factors above the treeline. [ABSTRACT FROM AUTHOR]

Vegetation dynamics and their responses to climate change are of significant spatial and temporal heterogeneity. The Sichuan–Tibet Railway (STR) is a major construction project of the 14th Five-Year Plan for Economic and Social Development of the People's Republic of China that is of great significance to promoting the social and economic development of Sichuan–Tibet areas. The planned railway line crosses areas with a complex geological condition and fragile ecological environment, where the regional vegetation dynamics are sensitive to climate change, topographic conditions and human activities. So, analyzing the vegetation variations in the complex vertical ecosystem and exploring their responses to hydrothermal factors are critical for providing technical support for the ecological program's implementation along the route of the planned railway line. Based on MOD13Q1 Normalized Difference Vegetation Index (NDVI) data for the growing season (May to October) during 2001–2020, a Theil-Sen trend analysis, Mann–Kendall test, Hurst exponent analysis and partial correlation analysis were used to detect the vegetation dynamics, predict the vegetation sustainability, examine the relationship between vegetation change and hydrothermal factors, regionalize the driving forces for vegetation growth and explore the interannual variation pattern of driving factors. The growing season NDVI along the Ya'an–Linzhi section of the STR showed a marked rate of increase (0.0009/year) during the past 20 years, and the vegetation's slight improvement areas accounted for the largest proportion (47.53%). Among the three hydrothermal parameters (temperature, precipitation and radiation), the correlation between vegetation growth and the temperature was the most significant, and the vegetation response to precipitation was the most immediate. The vegetation changes were affected by the combined impact of climatic and non-climatic factors, and the proportion of hydrothermal factors' combined driving force slightly increased during the study period. Based on the Hurst exponent, the future vegetation sustainability of the area along the Ya'an–Linzhi section of the STR faces a risk of degradation, and more effective conservations should be implemented during the railway construction period to protect the regional ecological environment. [ABSTRACT FROM AUTHOR]

Ordos City lies in a semiarid-arid farm pastoral region in north China. In recent years, as an ecological security barrier and ecological vulnerable and sensitive region, the intensification of human activities and climate change in the foreseeable future have aroused people's attention to ecological security issues. Based on the meteorological data of MOD13Q1 and the socioeconomic activity data in the same period, the variation characteristics of annual and seasonal vegetation NDVI, the response of vegetation NDVI to climate change, and the impact of human activities on vegetation NDVI in Ordos City from 2000 to 2020 were obtained by using trend analysis, sustainability analysis, and path analysis. The results showed that: (1) Ordos City vegetation NDVI showed an obvious growth trend, except for the interannual growth rate (0.0075/a), the growth rate in autumn increased faster than in other seasons (0.0067/a); (2) the future trend of vegetation NDVI in Ordos City is mainly anti-continuous improvement. If no effective measures are taken, the vegetation NDVI will degrade in the future; (3) precipitation was the main climate factor in the vegetation growth compared with temperature. An increase in precipitation promoted vegetation growth directly, while an increase in temperature showed a negative effect by reducing precipitation in the whole growing season; (4) the utilization pattern greatly affects the spatial distribution of vegetation NDVI, and stable land use can improve the utilization of vegetation. [ABSTRACT FROM AUTHOR]

Vegetation plays an important role in linking water, atmosphere, and soil. The dynamic change in vegetation is an important indicator for the regulation of the terrestrial carbon balance and climate change. This study applied trend analysis, detrended correlation analysis, and the Hierarchical Partitioning Algorithm (HPA) to GIMMS NDVI3g data, meteorological data, and natural vegetation types for the period 1983 to 2015 to analyze the temporal and spatial changes in NDVI during the growing season and its driving factors in the arid region of northwestern China. The results showed that: (1) the growing season length (GSL) was delayed, with a regional trend of 8 d/33 a, due to a significant advancement in the start of the growing season (SOS, −7 d/33 a) and an insignificant delay to the end of growing season (EOS, 2 d/33 a). (2) The regional change in NDVI was mainly driven by temperature and precipitation, contributing to variations in NDVI of forest of 36% and 15%, respectively, and in the NDVI of grassland, of 35% and 21%, respectively. In particular, changes to forested land and medium-coverage grassland (Mgra) were closely related to temperature and precipitation, respectively. (3) The spatial distribution of the mean NDVI of forest was closely related with precipitation, temperature, and solar radiation, with these meteorological variables explaining 20%, 15%, and 10% of the variation in NDVI, respectively. Precipitation and solar radiation explained 29% and 17% of the variation in the NDVI of grassland, respectively. The study reveals the spatial–temporal evolution and driving mechanism of the NDVI of natural vegetation in the arid region of Northwest China, which can provide theoretical and data support for regional vegetation restoration and conservation. [ABSTRACT FROM AUTHOR]

Climate conditions can significantly alter the vegetation net primary productivity (NPP) in many of Earth's ecosystems, although specifics of NPP–climate condition interactions, especially time-lag responses on seasonal scales, remain unclear in ecologically sensitive forest–grassland ecotones. Based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) and meteorological datasets, we analyzed the relationship between NPP and precipitation, temperature, and drought during the growing season (April–August), considering the time-lag effect (0–5 months) at the seasonal scale in Hulunbuir, Inner Mongolia, China from 2000 to 2018. The results revealed a delayed NPP response to precipitation and drought throughout the growing season. In April, the precipitation in the 4 months before (i.e., the winter of the previous year) explained the variation in NPP. In August, the NPP in some areas was influenced by the preceding 1~2 months of drought. The time-lag effect varied with vegetation type and soil texture at different spatial patterns. Compared to grass and crop, broadleaf forest and meadow exhibited a longer legacy of precipitation during the growing season. The length of the time-lag effects of drought on NPP increased with increasing soil clay content during the growing season. The interaction of vegetation types and soil textures can explain 37% of the change in the time-lag effect of the NPP response to PPT on spatial pattern. Our findings suggested that preceding precipitation influences vegetation growth at the early stages of growth, while preceding drought influences vegetation growth in the later stages of growth. The spatial pattern of the time lag was significantly influenced by interaction between vegetation type and soil texture factors. This study highlights the importance of considering the time-lag effects of climate conditions and underlying drivers in further improving the prediction accuracy of NPP and carbon sinks in temperate semiarid forest–grassland ecotones. [ABSTRACT FROM AUTHOR]

Vegetation phenology is an important indicator of vegetation dynamics. The boreal forest ecosystem is the main part of terrestrial ecosystem in the Northern Hemisphere and plays an important role in global carbon balance. In this study, the dynamic threshold method combined with the ground-based phenology observation data was applied to extract the forest phenological parameters from MODIS NDVI time-series. Then, the spatiotemporal variation of forest phenology is discussed and the relationship between phenological change and climatic factors was concluded in the northeast China from 2011 to 2020. The results indicated that the distribution of the optimal extraction threshold has spatial heterogeneity, and the changing rate was 3% and 2% with 1° increase in latitude for SOS (the start of the growing season) and EOS (the end of the growing season). This research also notes that the SOS had an advanced trend at a rate of 0.29 d/a while the EOS was delayed by 0.47 d/a. This variation of phenology varied from different forest types. We also found that the preseason temperature played a major role in effecting the forest phenology. The temperature in winter of the previous year had a significant effect on SOS in current year. Temperature in autumn of the current year had a significant effect on EOS. [ABSTRACT FROM AUTHOR]

Heat stress during flowering is a critical limitation for summer maize production. However, the incidence of heat varies with years and locations, and it poses a great risk to successful maize reproduction and kernel setting. Therefore, it is essential to provide a sound quantification of heat occurrence in relation to maize growth and development. Here, we analyzed the characteristics of heat occurrence based on climate data for over 60 years on Huaibei Plain, China. The effective accumulated temperature showed a slight interannual variation. The average maximum temperature ( T ¯ max ) during flowering was 32 °C–33 °C, which was approximately 2 °C higher than that over the whole growing season. The probability (P) for the daily Tmax > 33 °C during flowering was closer to 50% and this maximum temperature ranged between 33 °C and 37 °C. The five levels from normal to extreme heat for Tmax were defined. Across the six studied sites, the mild level heat stress accounted for most of incidents (P, 25–50%), followed by moderate (P, 13–25%) and severe (P, 0.5–13%), and the minimum for extreme heat stress (P, 0.5%). Four phases bracketing flowering during maize development were given, i.e., 1 week prior to anthesis, 1 week during anthesis, 1 week for anthesis-silking, and 1week post silking. There was a greater probability for heat stress incidents from anthesis to silking compared to the other developmental stages. Additionally, maize grain yield slightly increased with the increase in Tmax to 33 °C, but it declined as Tmax surpassed 33 °C. In conclusion, the pattern and characteristics of heat stress were quantified bracketing maize flowering. These findings assist to advise summer maize cropping strategies on the semi-arid and semi-humid Huaibei Plain, China or similar climate and cropping regions. [ABSTRACT FROM AUTHOR]

Drought has been approved to affect the process of terrestrial ecosystems from different organizational levels, including individual, community, and ecosystem levels; however, which traits play the dominant role in the resistance of plant to drought is still unclear. The experiment was conducted in semi-arid temperate grassland and included six paired control and drought experimental plots. The drought treatment was completely removed from precipitation treatments from 20 June to 30 August 2013. At the end of the growing season in 2013, we removed the rain cover for ecosystem recovery in 2014. The results demonstrated that drought treatment increased the coverage of and abundance Heteropappus altaicus, Potentilla bifurca, and Artemisia scoparia by 126.2–170.0% and 63.4–98.9%, but decreased that of Artemisia frigida, Dontostemon dentatus, and Melissilus ruthenicu by 46.2–60.2% and 49.6–60.1%. No differences in coverage and abundance of Agropyron cristatum, Stipa kiylovii, and Cleistogenes squarrosa were found between control and drought treatment. The coverage and abundance of Stipa kiylovii have exceeded the original level before the drought stress, but Heteropappus altaicus still had not recovered in the first year after the disturbance. Our findings indicate that plant functional traits are important for the understanding of the resistance and resilience of plants to drought stress, which can provide data support for grassland management. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is essential for agricultural production and crop water management. The recent climate change affecting the spatiotemporal variation of ET0 in eastern China continues to still be less understood. For this purpose, the latest observed data from 77 meteorological stations in Anhui province were utilized to determine the spatiotemporal variations of ET0 by the use of the Penman–Monteith FAO 56 (PMF-56) model. Furthermore, the Theil–Sen estimator and the Mann–Kendall (M–K) test were adopted to analyze the trends of ET0 and meteorological factors. Moreover, the differential method was employed to explore the sensitivity of ET0 to meteorological factors and the contributions of meteorological factors to ET0 trends. Results show that the ET0 decreased significantly before 1990, and then increased slowly. The ET0 is commonly higher in the north and lower in the south. ET0 is most sensitive to relative humidity (RH), except in summer. However, in summer, net radiation (Rn) is the most sensitive factor. During 1961–1990, Rn was the leading factor annually, during the growing season and summer, while wind speed (u2) played a leading role in others. All meteorological factors provide negative contributions to ET0 trends, which ultimately lead to decreasing ET0 trends. During 1991–2019, the leading factor of ET0 trends changed to the mean temperature (Ta) annually, during the growing season, spring and summer, and then to Rn in others. Overall, the negative contributions from u2 and Rn cannot offset the positive contributions from Ta and RH, which ultimately lead to slow upward ET0 trends. The dramatic drop in the amount of u2 that contributes to the changes in ET0 in Region III is also worth noting. [ABSTRACT FROM AUTHOR]

Grassland classification is crucial for grassland management. One commonly used method utilizes remote sensing vegetation indices (VIs) to map grassland classes at various scales. However, most grassland classifications were conducted as case studies in a small area due to lack of field data sources. At a small scale, classification is reliable; however, great uncertainty emerges when extended to other areas. In this study, large amounts of field observations (more than 30,000 aerial photos) were obtained using unmanned aerial vehicle photography in Inner Mongolia, China, during the peak period of grassland growth in 2018 and 2019. Then, four machine learning classification algorithms were constructed based on characteristic indices of MODIS NDVI in the growing season to map grassland classes of Inner Mongolia. Finally, the spatial distribution and temporal variation of temperate grassland classes were analyzed. Results showed that: (1) Among all characteristic indices, the maximum, average, and sum of MODIS NDVI from July to September during 2015 to 2019 greatly affected grassland classification. (2) The random forest method exhibited the best performance with overall accuracy and kappa coefficient being 72.17% and 0.62, respectively. (3) Compared with the grassland class mapped in the 1980s, 30.98% of grassland classes have been transformed. Our study provides a technological basis for effective and accurate classification of the temperate steppe class and a theoretical foundation for sustainable development and restoration of the temperate steppe ecosystem. [ABSTRACT FROM AUTHOR]

Crop type classification with satellite imageries is widely applied in support of crop production management and food security strategy. The abundant supply of these satellite data is accelerating and blooming the application of crop classification as satellite data at 10 m to 30 m spatial resolution have been made accessible easily, widely and free of charge, including optical sensors, the wide field of viewer (WFV) onboard the GaoFen (GF, high resolution in English) series from China, the MultiSpectral Instrument (MSI) onboard Sentinel 2 (S2) from Europe and the Operational Land Imager (OLI) onboard Landsat 8 (L8) from USA, thanks to the implementation of the open data policy. There are more options in using the satellite data as these three data sources are available. This paper explored the different capability of these three data sources for the crop type mapping in the same area and within the same growing season. The study was executed in a flat and irrigated area in Northwest China. Nine types of crop were classified using these three kinds of time series of data sources in 2017 and 2018, respectively. The same suites of the training samples and validation samples were applied for each of the data sources. Random Forest (RF) was used as the classifier for the crop type classification. The confusion error matrix with the OA, Kappa and F1-score was used to evaluate the accuracy of the classifications. The result shows that GF-1 relatively has the lowest accuracy as a consequence of the limited spectral bands, but the accuracy is at 93–94%, which is still excellent and acceptable for crop type classification. S2 achieved the highest accuracy of 96–98%, with 10 available bands for the crop type classification at either 10 m or 20 m. The accuracy of 97–98% for L8 is in the middle but the difference is small in comparison with S2. Any of these satellite data may be used for the crop type classification within the growing season, with a very good accuracy if the training datasets were well tuned. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R2 = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R2 = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature. [ABSTRACT FROM AUTHOR]

Due to the lack of available long-term climatic records, data related to past autumn precipitation variability throughout Northwest China, especially high-frequency variation, remains limited. In this study, it was found that inter-annual changes in Qinghai spruce radial growth were significantly and negatively (p < 0.05) correlated to inter-annual differences in precipitation during the late growing season (September-October) at all sampling sites in the upper reaches of the Heihe River. Based on the growth-climate relationship, a reconstruction of the inter-annual variation in September-October precipitation from 1839 was developed. With the help of this reconstruction, we successfully captured increased inter-annual variations in September-October precipitation during 1855-1861, 1892-1902, and 1969-1986. Furthermore, we found that increased precipitation variation might be related to variation in the westerlies and instability in the relationship between the regional westerly and precipitation. Moreover, our reconstructed data significantly correlated to the drought index (i.e., October SPEI_01) and moisture-related parameters (i.e., September-October cloud fraction), suggesting that our reconstructed data could be used as an indicator of the drought condition in the Heihe River Basin during the late growing season. [ABSTRACT FROM AUTHOR]

Lake evaporation is a critical component of the hydrological cycle. Quantifying lake evaporation in hyper-arid regions by measurement and estimation can both provide reliable potential evaporation (ET0) reference and promote a deeper understanding of the regional hydrological process and its response towards changing climate. We placed a floating E601 evaporation pan on East Juyan Lake, which is representative of arid regions' terminal lakes, to measure daily evaporation and conducted simultaneous bankside synoptic observation during the growing season of 2013-2015. A semi-empirical evaporation model derived from Dalton model was parameterized and validated with measured data. The model was then used to estimate lake evaporation during 2002-2015. According to in situ measurements, maximum, minimum and mean lake evaporation were 8.1, 3.7 and 6.5 mm/day, and growing season evaporation was 1183.3 mm (~80% of the annual amount). Adding up non-growing season evaporation that we converted from Φ20 pan evaporation at Ejina weather station, the annual mean lake evaporation, 1471.3 mm, was representative of lower Heihe River's ET0. Model inter-comparison implied our model performed well both in simplicity and accuracy and has potential utilization in a data-sparse area. In 2002-2015, estimated mean daily evaporation was 6.5 mm/day and growing season evaporation was 1233.7 mm. Trend analysis of estimated evaporation proved the evaporation paradox's existence in this hyper-arid region and validated complementary relationship theory's adaptability. [ABSTRACT FROM AUTHOR]

An understanding of the leaf and root traits associated with high nitrogen (N) absorption and utilization is very important in the development of N-efficient varieties in crop breeding programs. However, in rice, the relations between these traits and nitrogen use efficiencies (NUEs) have yet to be identified. Therefore, the responses of leaf and root traits and yields of the three main japonica rice cultivars in northern China were examined at a range of N application rates. The rice cultivars, Shennong 265 (SN265), Tiejing 11 (TJ11), and Fengjing (FJ), were grown in the field at six N rates (0, 60, 120, 180, 240, and 300 kg ha−1) applied during two growing seasons (2018 and 2019). With the increase in N rate, the grain yield of SN265 and TJ11 increased, whereas the grain yield of FJ first increased and then decreased. The grain yield of FJ was higher than that of SN265 and TJ11 at lower N rates, whereas the grain yields of SN265 and TJ11 were higher than that of FJ at higher N rates. The NUEs decreased with the increase in N rates. At lower N rates, the ability of rice to obtain N was the main limitation on rice yield, but with the increase in N rates, the limitation on yield due to N absorption capacity gradually weakened. However, at higher N rates, rice yields were restricted by the ability to utilize N. Compared with TJ11 or FJ, SN265 had higher N utilization capacity primarily because of its higher Rubisco activity, NR activity, GS activity, and ROA. Compared with SN265 or TJ11, FJ had a higher N absorption capacity that was attributed to its larger root architecture, which might have also been one of the reasons for lower N utilization. Therefore, by improving N absorption and utilization-related leaf and root traits, high yields and NUEs can be achieved in rice production at different N application rates. [ABSTRACT FROM AUTHOR]

The natural vegetation in arid areas of northwest China is strongly dependent on the availability of groundwater. Significantly, capillary water plays an essential role in regulating the ecological groundwater level in the multilayered structure of the vadose zone. The soil-column test and field survey in the lower reaches of the Shiyang River Basin were conducted to investigate the influence of the multi-layered structure of the vadose zone on maintaining the ecological effect of groundwater. Based on the field survey, the results show that the depth of groundwater is 3.0 m, and the rising height of capillary water is 140 cm. In the soil-column test, the height of the wetting front of the column was 125 cm. During the water releasing test, the water held by the vadose zone was 182.54 mm, which would have maintained Haloxylon's survival in a growing season. Therefore, the multi-layered structure of the vadose zone extends the ecological groundwater depth and consequently enhances the ecological function of groundwater. Importantly, with a lower groundwater level, the clay soil layer within the rising height range of the original capillary water would hold more water and maintain a higher water content for a certain period to supply surface vegetation. [ABSTRACT FROM AUTHOR]

Wind speed (u) is a significant constraint in the evapotranspiration modeling over the highly heterogeneous regional surface due to its high temporal-spatial variation. In this study, a satellite-based Wind Speed Avoiding Priestley–Taylor (WAPT) algorithm was proposed to estimate the regional actual evapotranspiration by employing a u-independent theoretical trapezoidal space to determine the pixel Priestley–Taylor (PT) parameter Φ. The WAPT model was comprehensively evaluated with hydro-meteorological observations in the arid Heihe River Basin in northwestern China. The results show that the WAPT model can provide reliable latent heat flux estimations with the root-mean-square error (RMSE) of 46.0 W/m2 across 2013–2018 for 5 long-term observation stations and the RMSE of 49.6 W/m2 in the growing season in 2012 for 21 stations with intensive observations. The estimation by WAPT has a higher precision in the vegetation growing season than in the non-growing season. The estimation by WAPT has a closer agreement with the ground observations for vegetation-covered surfaces (e.g., corn and wetland) than that for dry sites (e.g., Gobi, desert, and desert steppe). [ABSTRACT FROM AUTHOR]

Understanding the changes in xylem and phloem formation of trees and their relationship along latitudes are important for evaluating and predicting how fragile forests may respond to climate change; however, corresponding studies are still relatively scarce. This study investigated the intra-annual dynamics of xylem and phloem formation of Picea crassifolia and their relationship at two latitudes of arid and semi-arid forests in China. The results showed that both xylem and phloem formation varied at different latitudes. Xylem formation at the low-latitude site (Luoshan) started two weeks earlier than that at the high-latitude site (Helanshan) but ended one week later, resulting in an extended growing season at the low-latitude site. Phloem formation preceded cambium activity and xylogenesis at both sites by 24.6 days in Luoshan, which had warmer conditions, and by 17.3 days in Helanshan. In Luoshan, compared to Helanshan, there occurred significantly more enlarging and wall thickening cells, during (relatively wet) June–August, but significantly fewer enlarging and wall thickening cells as well as total xylem cells, during (relatively dry) April–May. Sample trees produced significantly fewer early phloem cells during the early growing season (April–May) in Luoshan, but generated significantly more late phloem cells during the late growing season in Helanshan. Additionally, different trade-offs between xylem and phloem formation were observed at different sites. The longer duration of early phloem formation might have shortened the duration of xylem lignification in Helanshan; in Luoshan, the date that late phloem reached its maximum growth rate was significantly positively correlated with the date when xylem lignification ended. The results revealed the plasticity of xylem and phloem formation under changing environmental conditions and a complex and site-specific relationship between xylem and phloem formation. These findings could help us better understand and predict the future growth of arid and semi-arid forests in China in response to climate change. [ABSTRACT FROM AUTHOR]