Land surface phenology (LSP) is beneficial to understand ecosystem response to climate change, vegetation and crop type discrimination, and ecological modeling. However, the existing efforts based on coarse resolution data (& GE;500 m) cannot perform well in regions with higher spatial heterogeneity and multi-cropping system, such as China. Given that the majority of 10 m/30 m-based phenological research has focused on North America and Europe, developing spatiotemporally explicit LSP data in China is imperative. More importantly, the existing 30 m LSP products are mainly suitable for vegetation types with a single vegetation cycle, but cannot work well for biomes with complex seasonality (e.g., multiple growth cycles). Here we first harmonized three vegetation indices, i.e., the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2), and land surface water index (LSWI) from Landsat-7/8 and Sentinel-2 imagery on the Google Earth Engine (GEE) platform. We then developed a new 30 m LSP algorithm that unified different phenological cycle-seeking processes per vegetation type and improved the existing algorithm. Furthermore, we used the algorithm to estimate the LSP product (LSP30CHN) for 2016-2019 across China, suitable for all vegetation types. The validation results showed a reasonably high accuracy (R2 > 0.6, RMSE < 15 days, mostly) of the LSP30CHN data against multisources in-situ observational (e.g., PhenoCam) and satellite-retrieved vegetation phenology data. Moreover, LSP30CHN data showed a consistent pattern but finer spatial details with the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) phenology product (MCD12Q2) at the homogenous area. We also found that phenological differences between LSP30CHN and MCD12Q2 increased with surface fragmentation, suggesting the potential of LSP30CHN to delineate phenological information on more fragmented landscapes. In contrast, the 500 m LSP data cannot provide such details in the regions with mixed cropping structures (e.g., corn, rice, and soybean) and multiple cropping index (e.g., single- and double-cropping systems). This study offers high accuracy of the LSP map for China, valuable for finer phenology-based services such as field-level crop management and agricultural phenology monitoring. It opens up new insights about exploring large-scale refined agricultural management and ecological assessment for other regions with complicated, fragmented landscapes and vegetation seasonality.

Large-scale land surface phenology (LSP) information has been developed from remote sensing-based vegetation indices (VIs) data. However, there are considerable discrepancies and uncertainties in the LSP data products for the start and end of growing seasons (SOS; EOS) as different vegetation indices and algorithms are used. Here, we used the TROPOspheric Monitoring Instrument (TROPOMI) solar-induced chlorophyll fluorescence (SIF) data to estimate SOS and EOS in the Tibetan Plateau, a global hotspot of vegetation response to climate change. We compared SIF-based phenological metrics to those derived from VIs (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRv)), and gross primary production (GPP) simulated by the vegetation photosynthesis model (VPM). We found relatively small discrepancies in SOS between SIF and VIs, but large differences in EOS. Thus, the length of the growing season derived from VIs was as much as two months longer than that estimated by SIF. These results were consistent across three products, bidirectional reflectance distribution function (BRDF) adjusted (MCD43), standard MODIS (MOD09), and TROPOMI products. The EOS discrepancy remained after excluding two mismatches (solar illumination and viewing angle) between satellite sensors. We also found that VIs-based EOS occurred below the freezing point, while SIF-based EOS occurred above the freezing point, suggesting that SIF-based EOS is more physiologically meaningful. Our study proposed that the late VIs-based EOS was caused in part by the effect of changes in soil background on VIs and VIs-based EOS. Our results highlight the need to re-evaluate current LSP data products derived from reflectance-based VIs and to develop new vegetation phenology data products using emitted energy such as SIF.

As a prominent part of global and regional terrestrial carbon (C) pools, increases in forest biomass C sinks can be attributed to either forest areal expansion (FAE) or increased biomass C density (IBCD). Accurate estimates of the relative contributions of FAE and IBCD to forest C sequestration can improve our understanding of forest C cycling processes and will help to formulate rational afforestation policies to cope with global warming. In this study, the Continuous Biomass Expansion Factor (CBEF) model and Forest Identity concept were used to map the spatiotemporal variation of the relative contribution of FAE and IBCD to the C sequestration of forest (natural and planted forests) in China and seven regions during the past 40 years. Our results suggest that: (1) total forest biomass C density and stocks of forest increased from 35.41 Mg C ha(-1) and 4128.50 Tg C to 43.95 Mg C ha(-1) and 7906.23 Tg C in China from 1977 to 2018, respectively; (2) for all forests, the IBCD has been a smaller contributor to C sinks than FAE in China from 1977 to 2018 (33.27 vs. 66.73%); (3) the contribution of FAE to C sinks is greater than that of IBCD in planted forests (63.99 vs. 36.01%), while in natural forests, IBCD has a larger contribution than FAE (57.82 vs. 42.18%) from 1977 to 2018 and the relative contribution of FAE has exceeded IBCD in the last decade; and (4) these patterns varied at the regional level such that the relative contribution of FAE increased for planted forests in most regions but for natural forests, IBCD gradually reached saturation and C stocks declined in northern regions in the last decade. The results from this study suggest that total biomass C sinks will keep increasing because of the increased forest area contributed by afforestation and the relatively young trees in planted forests. This study facilitates a more comprehensive assessment of forest C budgets and improves our understanding of ecological mechanisms of forest biomass carbon stock and dynamics.

Dissolved oxygen (DO) is a direct indicator of water pollution and an important water quality parameter that affects aquatic life. Based on the fundamental theorem of surfaces in differential geometry, the present study proposes a new modeling approach to estimate DO concentrations with high accuracy by assessing the spatial correlation and heterogeneity of DO with respect to explanatory variables. Specifically, a regularization penalty term is integrated into the high-accuracy surface modeling (HASM) method by applying geographically weighted regression (GWR) with some covariates. A modified version of HASM, namely HASM_MOD, is illustrated through a case study of Poyang Lake, China, by comparing the results of HASM, a support vector machine (SVM), and cokriging. The results indicate that HASM_MOD yields the best performance, with a mean absolute error (MAE) that is 38%, 45%, and 42% lower than those of HASM, the SVM, and cokriging, respectively, by using the cross-validation method. The introduction of a regularization penalty term by using GWR with respect to covariates can effectively improve the quality of the DO estimates. The results also suggest that HASM_MOD is able to effectively estimate nonlinear and nonstationary time series and outperforms three other methods using cross-validation, with a root-mean-square error (RMSE) of 0.20 mg/L and R-2 of 0.93 for the two study sites (Sanshan and Outlet_A stations). The proposed method, HASM_MOD, provides a new way to estimate the DO concentration with high accuracy.

Quantification of the spatial pattern of forest carbon (C) sinks in high resolution is helpful to reveal the factors that affect the C cycle and provides valuable information for developing sustainable forest management policies. Here we developed a method using the data of long-term forest inventories (1977-2018) and spatially-explicit remotely sensed information from land-use maps and the Normalized Difference Vegetation Index (NDVI) datasets, to estimate the spatial and temporal variation of forest biomass C in China. At first, we calculated forest biomass C stocks using the refined Continuous Biomass Expansion Factor (CBEF) model with parameters for each forest type based on eight national forest inventories. Secondly, based on multi-temporal land-use remote sensing and national forest inventory datasets, we obtained forest coverage datasets with high resolution (1 km*1 km). Thirdly, we downscaled the forest biomass C density using the calibrated forest coverage maps and the maximum NDVI values derived from GIMMS-NDVI3g imagery. Our results showed that China's forest functioned as a C sink of 3777.73 Tg C, and the C density of forest stands increased from 35.41 Mg C ha? 1 during 1977-1981 to 43.95 Mg C ha? 1 during 2014-2018. In addition, the validation results for most of the provinces based on published inventory estimates during the eight periods showed that the forest area at the pixel scale was successfully calibrated. From this, we produced the maps with a finer resolution for a series of spatially continuous forest biomass carbon density distribution and carbon sinks. Notably eight major forest projects have accounted for 44%-51% of the forest C stocks added in China from 1977 to 2018. Our research provides new insights for understanding and monitoring the spatiotemporal variations in of forest biomass and key information to support the development of new afforestation policies moving forward.

We propose a fundamental theorem for eco-environmental surface modelling (FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling (FTESM). The Beijing-Tianjin-Hebei (BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling (HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-mean-square error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.

在高精度曲面建模方法和地球表层系统建模基本定理研究结果基础上,演绎提出了生态环境曲面建模基本定理.以京津冀地区为案例,对基于生态环境曲面建模基本定理的空间升尺度、空间降尺度、空间插值、数据融合和模型-数据同化等算法进行了实证研究,与传统算法精度进行了比较分析.结果表明,由于基于生态环境曲面建模基本定理的各种算法综合了外蕴量信息和内蕴量信息,同时运用了理论上完善的信息综合方法,使海拔高度曲面的升尺度均方根误差至少降低了9m,年平均气温未来情景的降尺度精度至少提高16％,年平均气温过去变化趋势的数据融合精度至少提高70％,年平均降雨量过去变化趋势的空间插值精度至少提高0.2％,碳储量的模型-数据同化精度提高了40％.文章最后讨论了生态曲面建模基本定理亟待解决的五大理论问题和四大应用基础问题.

植被净初级生产力(Net Primary Productivity,NPP)作为陆地生态系统碳循环的重要组成部分,直接反映植被的生产力和地表质量状况,对于研究全球气候变化具有重要意义.为更好地了解近年来中国陆地植被NPP时空分布格局,探讨其对气候因子响应的空间分异规律,利用高精度曲面建模方法(HASM),结合LPJ-GUESS动态植被过程模型和MOD17A3 NPP遥感数据产品估算2001-2015年中国陆地生态系统植被NPP的时空分布特征,并根据气候要素和土壤质地特点分区探讨其与气候因子的关系.结果表明,(1)近15年全国植被平均NPP空间分布地域性明显,总体上沿水热梯度由西北向东南呈逐渐增加的趋势.其中西北沙漠和青藏高原腹地的植被NPP值最小,在100 g·m-2(以C计,下同)以下,西北内陆向东南沿海过渡带植被NPP值在300-600 g·m-2之间,四川盆地大部分地区以及海南省和东北平原局部植被NPP值在700 g·m-2以上.(2)近15年全国植被NPP均值为376 g·m-2·a-1,变化范围为1.9-1131 g·m-2·a-1.青藏区面积和植被NPP总量占全国的比例最大,均为23％,甘新区面积占全国的23％,年均NPP总量只占全国的10％;西南区、东北区和长江中下游区植被丰富,年均NPP总量占比大于面积占比.其他地区面积和年均NPP总量占全国的比例相当.(3)近15年全国有65.29％的区域植被NPP呈增加趋势,最大增速为72.64 g·m-2·a-1,平均增速为2.29 g·m-2·a-1,以青藏部分地区、黄土高原区、海南大部和台湾北部地区增幅最大.(4)近15年来全国有79.26％的地区年平均NPP与年平均降水之间呈显著正相关,而年平均NPP与年平均温度这一比例仅为8.71％,分布在青藏高原部分地区.

Quantifying the impact of urbanization on extreme climate events is significant for ecosystem responses, flood control, and urban planners. This study aimed to examine the urbanization effects on a suite of 36 extreme temperature and precipitation indices for the Beijing-Tianjin-Hebei (BTH) region by classifying the climate observations into three different urbanization levels. A total of 176 meteorological stations were used to identify large cities, small and medium-size cities and rural environments by applying K-means cluster analysis combined with spatial land use, nighttime light remote sensing, socio-economic data and Google Earth. The change trends of the extreme events during 1980–2015 were detected by using Mann-Kendall (MK) statistical test and Sen's slope estimator. Urbanization effects on those extreme events were calculated as well. Results indicated that the cool indices generally showed decreasing trends over the time period 1980–2015, while the warm indices tended to increase. Larger and more significant changes occurred with indices related to the daily minimum temperature. The different change rates of temperature extremes in urban, suburban and rural environments were mainly about the cool and warm night indices. Urbanization in medium-size cities tended to have a negative effect on cool indices, while the urbanization in large cities had a positive effect on warm indices. The significant difference of urbanization effect between large and medium-size cities lay in the daily maximum temperature. Results also demonstrated the scale effect of the urbanization on the extreme temperature events. However, the results showed little evidence of the urban effect on extreme precipitation events in the BTH region. This paper explored the changes in temperature and precipitation extremes and qualified the urbanization effects on those extreme events in the BTH region. The findings of this research can provide new insights into the future urban agglomeration development projects.

In the context of global warming, significant differences in temperature changes occur in different regions. Obtaining a more accurate temperature distribution is important for studying regional climate change. A high accuracy surface modeling (HASM) was introduced using the air temperature output from the weather research and forecasting (WRF) model as the driving field, and observation data from meteorological stations as the accuracy control conditions to obtain 30 years of high accuracy temperature fields in the Heihe River Basin. Verified by ground-based observations, the WRF model has a limited ability to simulate temperature conditions and performs worse in low altitude areas and in winter. The fusion results of the HASM decrease the mean absolute error from 2.21 to 0.9 degrees C and decrease the root mean square error from 2.58 to 1.16 degrees C, thus significantly increasing the accuracy compared with the original simulation from the WRF model and resulting in a more reasonable temperature analysis. The Mann-Kendall test shows clear warming in the Heihe River Basin, but there is a mutation year of 1998 in winter and annual average. The space-time distributions of and variations in the temperature from the HASM results are also analysed by dividing the study period into three subperiods. The analysis shows consistent warming except for the winter 2001-2010 period compared with the winter 1991-2000 period, in which the northeast area of the lower reaches of the Heihe River Basin was cool. Temperature changes in the Heihe River Basin also show that the sparsely populated Gobi Desert in the north and the southern high mountains show extreme sensitivity to climate change.

Forests are a major contributor of terrestrial ecosystem carbon pools. Accurate estimates of forest biomass carbon sinks can improve our understanding of carbon cycles and help in developing sustainable forest management policies in the face of climate change. In this study, we update estimates of the biomass carbon stocks of China's forests based on seven forest inventory datasets from 1977 to 2013 and carbon fraction coefficients of 46 tree species in the continuous biomass expansion factor (CBEF) model. Our findings suggest that (1) China's forest stands acted as an average biomass carbon sink of 99.07 Tg C year(-1); and (2) biomass carbon stocks increased by 72.62% from 1977 to 2013 and recently reached 7.27 Pg C, driven by forest area expansion and forest growth. The biomass carbon density of forest stands (canopy coverage > 20%) increased from 38.18 to 44.52 Mg C ha(-1) during the study period, with higher carbon densities in natural compared to planted forests, and the gap increasing with forest age. The largest increases in the biomass of carbon stocks of forest stands occurred in the eastern and northern regions. Our results suggest that biomass carbon stocks of natural forests account for about 85% in the most recent inventory and that the total biomass carbon stocks of forest stands in China will keep increasing in the future because of the large area of planted forests with young and middle-aged forest growth. The results from this study can help with comprehensive investigations of forest carbon budgets and the calibration and validation of simulation model results.

The changing climate is shifting the seasonality of photosynthesis in vegetation, including the start (SOS), end (EOS), and length (LOS) of the growing season, and the peak photosynthesis timing (PPT). While the SOS, EOS, and LOS have been widely investigated, the PPT of grasslands - as a proxy for the response of seasonal plant photosynthesis to climate change - has been overlooked. In this study, we propose a hybrid generalized additive model (HGAM) method to extract PPT using the Vegetation Photosynthesis Model (VPM)-based gross primary production (GPP) product, and we examine the dynamics, drivers, and consequences of PPT changes in temperate and alpine grasslands in China over 2000-2016. We found that the PPTs in temperate and alpine grasslands have exhibited advancing (with -0.68 days yr(-1), p < 0.05) and delaying (with 0.29 days yr(-1), p = 0.158) trends, respectively. In addition, preseason precipitation and soil moisture were positively correlated with the PPT in temperate and alpine grasslands, respectively, while the preseason temperature consistently controlled the PPT changes in both grasslands. Furthermore, we found that an earlier PPT was associated with higher annual production in the temperate grasslands but not in the alpine grasslands in China. The divergent PPT patterns indicated the varied adaptation characteristics to climatic constraints in the temperate and alpine grasslands and also caused different consequences on carbon uptake. This study highlights the importance of PPT in understanding the spatiotemporal dynamics of vegetation photosynthesis and the carbon cycle under a changing climate.

理解土地利用方式转变过程影响生态系统生物地球化学循环及温室气体排放的机理,并利用模型模拟土地利用方式转变过程对温室气体通量的影响是一项长期、艰巨的科学任务.本研究基于国际上广泛应用的生物地球化学过程模型(DNDC模型),结合气象、土壤和管理措施等数据,模拟了旱田转水田土地利用方式转变后稻田CH4、CO2和N2O三种温室气体的通量和常年种植水稻的稻田温室气体通量,并将模拟值与观测值进行比较.结果 表明:DNDC模型能够较好地模拟新、老稻田温室气体通量的季节变化,但对老稻田温室气体的排放通量模拟效果(R2＞0.89,n=40,P＜0.01)优于新转稻田(R2 ＞0.79,n=265,P＜0.01),且对CH4和CO2的模拟效果优于对N2O的模拟效果;根据田间观测数据,改变模型模拟土地利用方式转换前后土壤SOC浓度和pH值,并不能完全模拟土地利用变化对温室气体的影响,微生物群落在土地利用方式转变过程中可能发生较大变化,需要在模型中进一步体现.通过模型模拟土地利用方式改变对温室气体排放的影响,可为国家温室气体、碳排放清单的编制及管理政策的制定提供参考依据.

: Agricultural production activities are one of the main sources of atmospheric greenhouse gases. Different crop cultivation systems and management methods will affect greenhouse gas emissions. There have been a lot of researches on the laws and control mechanisms of agricultural greenhouse gas emissions at home and abroad, but there are relatively few studies on the laws and influence mechanisms of greenhouse gas emissions. In this study, greenhouse gas fluxes new paddy fields (just converted from paddy fields to rice fields) in 2013- 2016 and old paddy(which have been rice fields for the past 20 years) fields in 2016 as well as soil micro-meteorological and other physical and chemical properties were observed using a field plot experiment at the Qianyanzhou Ecological Station of the Chinese Academy Sciences in southern China. Then, seasonal and annual variation of greenhouse gas fluxes and the main factors that impacting greenhouse gas fluxes were analyzed. Based on the analysis, the dynamic changes of greenhouse gas emissions in rice fields are predicted. The results show that in the first four years after land-use conversion, CH 4 emission fluxes in the new paddy fields were significantly lower than that in the old paddy fields, and annual emission increased with the extension of farming period, while the CH 4 emission rate of old rice fields is not obvious, whereas the difference in emission of the greenhouse gases CO 2 and N 2 O were not significant. Moreover, soil pH and the soil organic carbon content of new rice fields were lower than that of old paddy fields. The model simulation results show that shortening paddy field rotation (water- dry rotation) period (less than 7 years) can effectively reduce CH 4 and total greenhouse gas emissions in paddy fields. The results suggest that using the CH 4 emission coefficient of old rice fields may significantly overestimate CH 4 emissions from new rice fields. This study may provide new insights for understanding the impact of land- use change on greenhouse gas emissions, and provide a basis for accurate estimation of regional agricultural greenhouse gas emissions and the development of emission reduction policies.

Forests are the main body of terrestrial ecosystems; they are not only huge carbon stocks, but also have a positive effect on mitigating climate warming. Effective scientific forest carbon inventory measurement methods help to elucidate the global carbon cycle. Generally, however, due to the complex structure of forest ecosystems, the estimation of forest carbon measurements has had problems of low accuracy and high uncertainty. In recent years, a large number of methods for estimating forest carbon have been developed domestically and overseas. These methods mainly include forest vegetation and soil carbon estimation methods based on the inventory of sample plots, growth harvesting⁃based empirical model estimation and remote sensing estimation based on quantitative remote sensing and radar observations data, flux observations based on

As an important cause of global warming, CO 2 concentrations and their changes have aroused worldwide concern. Establishing explicit understanding of the spatial and temporal distributions of CO 2 concentrations at regional scale is a crucial technical problem for climate change research. High accuracy surface modeling (HASM) is employed in this paper using the output of the CO 2 concentrations from weather research and forecasting-chemistry (WRF-CHEM) as the driving fields, and the greenhouse gases observing satellite (GOSAT) retrieval XCO 2 data as the accuracy control conditions to obtain high accuracy XCO 2 fields. WRF-CHEM is an atmospheric chemical transport model designed for regional studies of CO 2 concentrations. Verified by ground- and space-based observations, WRF-CHEM has a limited ability to simulate the conditions of CO 2 concentrations. After conducting HASM, we obtain a higher accuracy distribution of the CO 2 in North China than those calculated using the classical Kriging and inverse distance weighted (IDW) interpolation methods, which were often used in past studies. The cross-validation also shows that the averaging mean absolute error (MAE) of the results from HASM is 1.12 ppmv, and the averaging root mean square error (RMSE) is 1.41 ppmv, both of which are lower than those of the Kriging and IDW methods. This study also analyses the space-time distributions and variations of the XCO 2 from the HASM results. This analysis shows that in February and March, there was the high value zone in the southern region of study area relating to heating in the winter and the dense population. The XCO 2 concentration decreased by the end of the heating period and during the growing period of April and May, and only some relatively high value zones continued to exist.

随着大数据时代的到来生态环境大数据给生态环境领域研究带来了新的机遇与发展.本文回顾了生态环境大数据在收集、监测、分析与应用方面的发展现状.虽然生态环境大数据研究相对于其他领域起步较晚,但是目前正处于蓄势待发的状态.生态环境大数据研究未来的发展趋势为:一、建立数据共享机制;二、需要跨区域的不同监测站点甚至不同观测网络进行联合观测与研究,建立从样地到区域甚至到全球多尺度的、系统的观测与研究,并且应该推进观测的标准化和规范化,进一步统一不同生态环境观测网络的观测标准,建立国际统一的观测标准和规范;三、需要开发针生态环境对大数据的统计分析软件,尤其应该重视地理信息技术与统计分析技术的结合,特别是过程模型与大数据的结合.最后,虽然生态环境大数据的应用研究相对薄弱但未来的发展空间是巨大的,未来生态环境大数据的应用主要应该体现在生态环境资源管理、生态环境动态监测、生态环境评价等方面.

As the largest carbon pool of terrestrial ecosystems,soils constitute important sources and sinks of global CO2 balance.Soil respiration is an important pathway of carbon fluxes between the atmosphere and terrestrial ecosystems.Further,soil respiration can also influence terrestrial carbon cycling through climate feedbacks,specifically temperature sensitivity (Q10)-Oil tea (Camellia oleifera) is an important native woody oil crop,and has been widely developed in south China in recent years.High-level fertilization has been widely adopted to increase growth of oil tea trees and maintain soil fertility.In this study,the effects of fertilization on soil respiration,heterotrophic respiration and temperature sensitivity in an oil tea plantation were examined.Static chambers and gas chromatography were used to collect and analyze gas samples.To differentiate soil respiration and soil heterotrophic respiration,the trenching method was used.There were four treatments in this study:oil tea plots with fertilization (OF),oil tea plots without fertilization (CK),trenching plots with fertilization (OF-T),and trenching plots without fertilization (CK-T).The results showed that fertilization had no significant effects on either soil respiration or heterotrophic respiration.Soil CO2 effluxes in different treatments (OF,CK,OF-T,CK-T) were 77.91±2.59,73.71±0.97,66.82±1.02,and 66.84±3.94 mg C m-2 h-1,respectively.The temperature sensitivity of soil CO2 effluxes was 1.77±0.01,1.75±0.03,1.96±0.01,1.79±0.03 in OF,CK,OF-T,and CK-T respectively.Fertilization significantly increased temperature sensitivity of soil heterotrophic respiration,with the Q10 values in the OF-T plots significantly higher than those in the CK-T plots.In addition,fertilization significantly increased soil NH4+-N and NO3--N contents in the surface layer (0-10cm).We also found significant linear relationships between the Q10 of soil CO2 effluxes and soil NH4+-N,as well as NO3--N contents.Our findings may provide basic data for greenhouse gas inventories and understanding their responses to climate change in a typical cash crop plantation in south China.

Surface modeling with very large data sets is challenging. An efficient method for modeling massive data sets using the high accuracy surface modeling method (HASM) is proposed, and HASM_Big is developed to handle very large data sets. A large data set is defined here as a large spatial domain with high resolution leading to a linear equation with matrix dimensions of hundreds of thousands. An augmented system approach is employed to solve the equality-constrained least squares problem (LSE) produced in HASM_Big, and a block row action method is applied to solve the corresponding very large matrix equations. A matrix partitioning method is used to avoid information redundancy among each block and thereby accelerate the model. Experiments including numerical tests and real-world applications are used to compare the performances of HASM_Big with its previous version, HASM. Results show that the memory storage and computing speed of HASM_Big are better than those of HASM. It is found that the computational cost of HASM_Big is linearly scalable, even with massive data sets. In conclusion, HASM_Big provides a powerful tool for surface modeling, especially when there are millions or more computing grid cells.

The generation of big data technology provides a new way for processing massive data related to ecological environment,as well as monitoring and analyzing global ecological environmental problems.This paper first described the basic concepts of big data,and the concept and feature of ecological environmental big data.Then,considering the system structure of general big data,the architecture of big data platform for ecological environment was designed from four key aspects,including data platform,infrastructure platform,technology platform and application service platform.After the main frame is analyzed,key technologies for building technology platform of ecological environmental big data were elaborated in four parts:data acquisition,data storage and management,data computing pattern and data analysis.Finally,in view of future development and difficulties,we put forward some thinking and proposals.