High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring. However, the major data sources of high-resolution images are not owned by China. The cost of large scale u...High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring. However, the major data sources of high-resolution images are not owned by China. The cost of large scale use of high resolution imagery data becomes prohibitive. In pace of the launch of the Chinese ＂High Resolution Earth Observation Systems＂, China is able to receive superb high-resolution remotely sensed images （GF series） that equalizes or even surpasses foreign similar satellites in respect of spatial resolution, scanning width and revisit period. This paper provides a perspective of using high resolution remote sensing data from satellite GF-1 for agriculture monitoring. It also assesses the applicability of GF-1 data for agricultural monitoring, and identifies potential applications from regional to national scales. GF-1＇s high resolution （i.e., 2 m/8 m）, high revisit cycle （i.e., 4 days）, and its visible and near-infrared （VNIR） spectral bands enable a continuous, efficient and effective agricultural dynamics monitoring. Thus, it has gradually substituted the foreign data sources for mapping crop planting areas, monitoring crop growth, estimating crop yield, monitoring natural disasters, and supporting precision and facility agriculture in China agricultural remote sensing monitoring system （CHARMS）. However, it is still at the initial stage of GF-1 data application in agricultural remote sensing monitoring. Advanced algorithms for estimating agronomic parameters and soil quality with GF-1 data need to be further investigated, especially for improving the performance of remote sensing monitoring in the fragmented landscapes. In addition, the thematic product series in terms of land cover, crop allocation, crop growth and production are required to be developed in association with other data sources at multiple spatial scales. Despite the advantages, the issues such as low spectrum resolution and image distortion associated with high spatial resolution and wide swath width, might pose challenges for GF-1 data applications and need to be addressed in future agricultural monitoring.展开更多
Using simultaneously collected remote sensing data and field measurements, this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1 （GF-1） wide fiel...Using simultaneously collected remote sensing data and field measurements, this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1 （GF-1） wide field of view （WFV） camera, environment and disaster monitoring and forecasting satellite （H J-l） charge coupled device （CCD）, and Landsat-8 opera- tional land imager （OLI） data for estimating the leaf area index （LAI） of winter wheat via reflectance and vegetation indices （VIs）. The accuracies of these LAI estimates were then assessed through comparison with an empirical model and the PROSAIL radiative transfer model. The effects of radiation calibration, spectral response functions, and spatial resolution on discrepancies in the LAI estimates between the different sensors were also analyzed. The results yielded the following observations： （1） The correlation between reflectance from different sensors is relative good, with the adjusted coefficients of determination （R2） between 0.375 to 0.818. The differences in reflectance are ranging from 0.002 to 0.054. The correlation between VIs from different sensors is high with the R2 between 0.729 and 0.933. The differences in the VIs are ranging from 0.07 to 0.156. These results show the three sensors＇ images can all be used for cross calibration of the reflectance and VIs. （2） The four VIs from the three sensors are all demonstrated to be highly correlated with LAI （R2 between 0.703 and 0.849）. The linear models associated with the 2-band enhanced vegetation index （EVI2）, which feature the highest R2 （higher than 0.746） and the lowest root mean square errors （RMSE） （less than 0.21）, were selected to estimate the winter wheat LAI. The accuracy of the estimated LAI from Landsat-8 was the highest, with the relative errors （RE） of 2.18% and an RMSE of 0.13, while the H J-1 was the lowest, with the RE of 2.43% and the RMSE of 0.15. （3） The inversion errors in the different sensors＇ LAI estimates using the PROSAIL model are small. The accuracy of the GF-1 is the highest with the RE of 3.44%, and the RMSE of 0.22, whereas that of the H J-1 is the lowest with the RE of 4.95%, and the RMSE of 0.26. （4） The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored, but the effects of spatial resolution are significant and must be taken into consideration in practical applications.展开更多
To feed the increasing world population, more food needs to be produced from agricultural land systems. Solutions to produce more food with fewer resources while minimizing adverse environmental and ecological consequ...To feed the increasing world population, more food needs to be produced from agricultural land systems. Solutions to produce more food with fewer resources while minimizing adverse environmental and ecological consequences require sustainable agricultural land use practices as supplementary to advanced biotechnology and agronomy. This review paper, from a land system perspective, systematically proposed and analyzed three interactive strategies that could possibly raise future food production under global change. By reviewing the current literatures, we suggest that cropland expansion is less possible amid iferce land competition, and it is likely to do less in increasing food production. Moreover, properly allocating crops in space and time is a practical way to ensure food production. Climate change, dietary shifts, and other socio-economic drivers, which would shape the demand and supply side of food systems, should be taken into consideration during the decision-making on rational land management in respect of sustainable crop choice and allocation. And ifnally, crop-speciifc agricultural intensiifcation would play a bigger role in raising future food production either by increasing the yield per unit area of individual crops or by increasing the number of crops sown on a particular area of land. Yet, only when it is done sustainably is this a much more effective strategy to maximize food production by closing yield and harvest gaps.展开更多
By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution a...By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.展开更多
How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness ...How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information. This study, taking Heilongjiang Province as the study area, aims to use time-series moderate resolution imaging spectroradiometer （MODIS） surface reflectance product （MOD09A1） data to evaluate the importance of spectral and temporal features for crop classification. In doing so, a feature selection strategy based on separability index （SI） was first used to rank the most important spectro-temporal features for crop classification. Ten feature scenarios with different spectral and temporal variable combinations were then devised, which were used for crop classification using the support vector machine and their accuracies were finally assessed with the same crop samples. The results show that the normalized difference tillage index （NDTI）, land surface water index （LSWl） and enhanced vegetation index （EVI） are the most informative spectral features and late August to early September is the most informative temporal window for identifying crops in Heilongjiang for the observed year 2011. Spectral diversity and time variety are both vital for crop classification, and their combined use can improve the accuracy by about 30% in comparison with single image. The feature selection technique based on SI analysis is superior for achieving high crop classification accuracy （producers＇ accuracy of 94.03% and users＇ accuracy of 93.77%） with a small number of features. Increasing temporal resolution is not necessarily important for improving the classification accuracies for crops, and a relatively high classification accuracy can be achieved as long as the images associated with key phenological phrases are retained.展开更多
Rice＇s spatial-temporal distributions, which are critical for agricultural, environ- mental and food security research, are affected by natural conditions as well as socio-eco- nomic developments. Based on multi-sour...Rice＇s spatial-temporal distributions, which are critical for agricultural, environ- mental and food security research, are affected by natural conditions as well as socio-eco- nomic developments. Based on multi-source data, an effective model named the Spatial Production Allocation Model （SPAM） which integrates arable land distribution, administrative unit statistics of crop data, agricultural irrigation data and crop suitability data, was used to get a series of spatial distributions of rice area and production with 10-km pixels at a national scale -it was applied from the early 1980s onwards and used to analyze the pattern of spatial and temporal changes. The results show that significant changes occurred in rice in China during 1980-2010. Overall, more than 50% of the rice area decreased, while nearly 70% of rice production increased in the change region during 1980-2010. Spatially, most of the increased area and production were in Northeast China, especially, in Jilin and Heilongjiang; most of the decreased area and production were located in Southeast China, especially, in regions of rapidly urbanization in Guangdong, Fujian and Zhejiang. Thus, the centroid of rice area was moved northeast approximately 230 km since 1980, and rice production about 320 km, which means rice production moved northeastward faster than rice area because of the significant rice yield increase in Northeast China. The results also show that rice area change had a decisive impact on rice production change. About 54.5% of the increase in rice pro- duction is due to the expansion of sown area, while around 83.2% of the decrease in rice production is due to contraction of rice area. This implies that rice production increase may be due to area expansion and other non-area factors, but reduced rice production could largely be attributed to rice area decrease.展开更多
Remote sensing, in particular satellite imagery, has been widely used to map cropland, analyze cropping systems, monitor crop changes, and estimate yield and production. However, although satellite imagery is useful w...Remote sensing, in particular satellite imagery, has been widely used to map cropland, analyze cropping systems, monitor crop changes, and estimate yield and production. However, although satellite imagery is useful within large scale agriculture applications （such as on a national or provincial scale）, it may not supply sufifcient information with adequate resolution, accurate geo-referencing, and specialized biological parameters for use in relation to the rapid developments being made in modern agriculture. Information that is more sophisticated and accurate is required to support reliable decision-making, thereby guaranteeing agricultural sustainability and national food security. To achieve this, strong integration of information is needed from multi-sources, multi-sensors, and multi-scales. In this paper, we propose a new framework of satellite, aerial, and ground-integrated （SAGI） agricultural remote sensing for use in comprehensive agricultural monitoring, modeling, and management. The prototypes of SAGI agriculture remote sensing are ifrst described, followed by a discussion of the key techniques used in joint data processing, image sequence registration and data assimilation. Finally, the possible applications of the SAGI system in supporting national food security are discussed.展开更多
Rice is one of the most important grain crops in Northeast China （NEC） and its cultivation is sensitive to climate change. This study aimed to explore the spatio-temporal changes in the NEC rice planting area over t...Rice is one of the most important grain crops in Northeast China （NEC） and its cultivation is sensitive to climate change. This study aimed to explore the spatio-temporal changes in the NEC rice planting area over the period of 1980-2010 and to analyze their relationship to climate change. To do so, the CLUE-S （conversion of land use and its effects at small region extent） model was ifrst updated and used to simulate dynamic changes in the rice planting area in NEC to understand spatio-temporal change trends during three periods： 1980-1990, 1990-2000 and 2000-2010. The changing results in individual periods were then linked to climatic variables to investigate the climatic drivers of these changes. Results showed that the NEC rice planting area expanded quickly and increased by nearly 4.5 times during 1980-2010. The concentration of newly planted rice areas in NEC constantly moved northward and the changes were strongly dependent on latitude. This conifrmed that climate change, increases in temperature in particular, greatly inlfuenced the shift in the rice planting area. The shift in the north limit of the NEC rice planting area generally followed a 1＆#176;C isoline migration pattern, but with an obvious time-lag effect. These ifndings can help policy makers and crop producers take proper adaptation measures even when exposed to the global warming situation in NEC.展开更多
With consecutive occurrences of drought disasters in China in recent years, it is important to estimate their potential impacts on regional crop production. In this study, we detect the impacts of drought on wheat and...With consecutive occurrences of drought disasters in China in recent years, it is important to estimate their potential impacts on regional crop production. In this study, we detect the impacts of drought on wheat and maize yield and their changes at a 0.5＆#176;＆#215;0.5＆#176; grid scale in the wheat-maize rotation planting area in the North China Plain under the A1B climate change scenario using the Decision Support System for Agrotechnology Transfer （DSSAT） model and the outputs of the regional climate modeling system-Providing Regional Climates for Impacts Studies （PRECIS）. Self-calibrating palmer drought severity index was used as drought recognition indicator. Two time slices used for the study were the baseline （1961-1990） and 40 years of 2011-2050. The results indicate that the potential planting region for double crop system of wheat-maize would expend northward. The statistic conclusions of crop simulations varied considerably between wheat and maize. In disaster-affected seasons, wheat yield would increase in the future compared with baseline yields, whereas in opposite for maize yield. Potential crop yield reductions caused by drought would be lower for wheat and higher for maize, with a similar trend found for the ratio of potential crop yield reductions for both crops. It appears that the negative impact of drought on maize was larger than that on wheat under climate change A1B scenario.展开更多
The successful launched Gaofen satellite no. 1 wide field-of-view （GF-1 WFV） camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Be...The successful launched Gaofen satellite no. 1 wide field-of-view （GF-1 WFV） camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Bei＇an City, Northeast China as the study area, aims to investigate the potential of GF-1 WFV images for crop identification and explore how to fully use its spectral, textural and temporal information to improve classification accuracy. In doing so, an object-based and Random Forest （RF） algorithm was used for crop mapping. The results showed that classification based on an optimized single temporal GF-1 image can achieve an overall accuracy of about 83%, and the addition of textural features can im- prove the accuracy by 8.14%. Moreover, the multi-temporal GF-1 data can produce a classification map of crops with an overall accuracy of 93.08% and the introduction of textural variables into multi-temporal GF-1 data can only increase the accuracy by about 1%, which suggests the importance of temporal information of GF-1 for crop mapping in comparison with single temporal data. By comparing classification results of GF-1 data with different feature inputs, it is concluded that GF-1 WFV data in general can meet the mapping efficiency and accuracy requirements of regional crop. But given the unique spectral characteristics of the GF-1 WFV imagery, the use of textual and temporal information is needed to yield a satisfactory accuracy.展开更多
Rice area has been expanding rapidly during the past 30 years under the influence of global change in northeastern China, which is the northernmost region of rice cultivation in China. However, the spatio-temporal dyn...Rice area has been expanding rapidly during the past 30 years under the influence of global change in northeastern China, which is the northernmost region of rice cultivation in China. However, the spatio-temporal dynamic changes in rice area are still unclear, although they may have important policy implications for environmental protection and adaptation to climate change. In this study, we aimed to identify the dynamic changes of the rice area in Heilongjiang Province of northeastern China by extracting data from multiple Landsat images. The study used ground quadrats selected from Google Earth and the extraction of a confusion matrix to verify the accuracy of extraction. The overall accuracy of the extracted rice area was higher than 95% as a result of using the artificial neural network （ANN） classification method. The results showed that the rice area increased by approximately 2.4×10^6 ha during the past 30 years at an annual rate of 8.0×10^4 ha, and most of the increase occurred after 2000. The central latitude of the rice area shifted northwards from 46 to 47°N during the study period, and moved eastwards from 130 to 133°E. The rice expansion area accounted for 98% of the total change in rice area, and rice loss was notably rare. The rice expansion was primarily from dryland. In addition, rice cultivation in marshland and grassland played a minor role in the rice expansion in this region.展开更多
Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are st...Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are still lacking. Based on the cross-entropy theory, a spatial production allocation model （SPAM） has been developed for presenting spa- tio-temporal dynamics of maize cropping system in Northeast China during 1980-2010. The simulated results indicated that （1） maize sown area expanded northwards to 48~N before 2000, after that the increased sown area mainly occurred in the central and southern parts of Northeast China. Meanwhile, maize also expanded eastwards to 127°E and lower elevation （less than 100 m） as well as higher elevation （mainly distributed between 200 m and 350 m）; （2） maize yield has been greatly promoted for most planted area of Northeast China, espe- cially in the planted zone between 42°N and 48°N, while the yield increase was relatively homogeneous without obvious longitudinal variations for whole region; （3） maize planting density increased gradually to a moderately high level over the investigated period, which reflected the trend of aggregation of maize cultivation driven by market demand.展开更多
Although climate change impacts and agricultural adaptations have been studied extensively, how smallholder farmers perceive climate change and adapt their agricultural activities is poorly understood. Survey-based da...Although climate change impacts and agricultural adaptations have been studied extensively, how smallholder farmers perceive climate change and adapt their agricultural activities is poorly understood. Survey-based data （presents farmers＇ personal perceptions and adaptations to climate change） associated with external biophysical-socioeconomic data （presents real-world climate change） were used to develop a farmer-centered framework to explore climate change impacts and agricultural adaptations at a local level. A case study at Bin County （1980s-2010s）, Northeast China, suggested that increased annual average temperature （0.6＆#176;C per decade） and decreased annual precipitation （46 mm per decade, both from meteorological datasets） were correctly perceived by 76 and 66.9%, respectively, of farmers from the survey, and that a longer growing season was conifrmed by 70%of them. These reasonably correct perceptions enabled local farmers to make appropriate adaptations to cope with climate change：Longer season alternative varieties were found for maize and rice, which led to a signiifcant yield increase for both crops. The longer season also affected crop choice：More farmers selected maize instead of soybean, as implicated from survey results by a large increase in the maize growing area. Comparing warming-related factors, we found that precipitation and agricultural disasters were the least likely causes for farmers＇ agricultural decisions. As a result, crop and variety selection, rather than disaster prevention and infrastructure improvement, was the most common ways for farmers to adapt to the notable warming trend in the study region.展开更多
Over the past few decades, built-up land in China has increasingly expanded with rapid urbanization, industrialization and rural settlements construction. The expansions encroached upon a large amount of cropland, pla...Over the past few decades, built-up land in China has increasingly expanded with rapid urbanization, industrialization and rural settlements construction. The expansions encroached upon a large amount of cropland, placing great challenges on national food security. Although the impacts of urban expansion on cropland have been intensively illustrated, few attentions have been paid to differentiating the effects of growing urban areas, rural settlements, and industrial/transportation land. To fill this gap and offer comprehensive implications on framing policies for cropland protection, this study investigates and compares the spatio-temporal patterns of cropland conversion to urban areas, rural settlements, and industrial/transportation land from 1987 to 2010, based on land use maps interpreted from remote sensing imagery. Five indicators were developed to analyze the impacts of built-up land expansion on cropland in China. We find that 42,822 km2 of cropland were converted into built-up land in China, accounting for 43.8% of total cropland loss during 1987-2010. Urban growth showed a greater impact on cropland loss than the expansion of rural settlements and the expansion of industrial/transportation land after 2000. The contribution of rural settlement expansion decreased; however, rural settlement saw the highest percentage of traditional cropland loss which is generally in high quality. The contribution of industrial/transportation land expansion increased dramatically and was mainly distributed in major food production regions. These changes were closely related to the economic restructuring, urban-rural transformation and government policies in China. Future cropland conservation should focus on not only finding a reasonable urbanization mode, but also solving the ＂hollowing village＂ problem and balancing the industrial transformations.展开更多
Based on county-level crop statistics and other ancillary information, spatial distri- bution of maize in the major maize-growing areas （latitudes 39°-48°N） was modelled for the period 1980-2010 by using a...Based on county-level crop statistics and other ancillary information, spatial distri- bution of maize in the major maize-growing areas （latitudes 39°-48°N） was modelled for the period 1980-2010 by using a cross-entropy-based spatial allocation model. Maize extended as far north as the northern part of the Lesser Khingan Mountains during the period, and the area sown to maize increased by about 5 million ha. More than half of the increase occurred before 2000, and more than 80% of it in the climate transitional zone, where the annual ac- cumulated temperature （AAT） was 2800-3400 ℃.d. Regions with AAT of 3800-4000 ℃-d became more important, accounting for more than 25% of the increase after 2000. The ex- pansion of maize was thus closely related to warming, although some variation in the distri- bution was noticed across zones in relation to the warming, indicating that maize in northeast China may have adapted successfully to the warming by adjusting its spatial distribution to match the changed climate.展开更多
In the study, an improved approach was proposed to identify the contribution shares of three group factors that are climate, technology and input, social economic factors by which the grain production is shaped. In or...In the study, an improved approach was proposed to identify the contribution shares of three group factors that are climate, technology and input, social economic factors by which the grain production is shaped. In order to calibrate the method, Jiangxi Province, one of the main paddy rice producers in China was taken as an example. Based on 50 years （1961-2010） meteorological and statistic data, using GIS and statistical analysis tools, the three group factors that in certain extent impact China＇s paddy rice production have been analyzed quantitatively. The individual and interactive contribution shares of each factor group have been identiifed via eta square （η2）. In the paper, two group ordinary leasr square （OLS） models, paddy models and climate models, have been constructed for further analysis. Each model group consists of seven models, one full model and six partial models. The results of paddy models show that climate factors individually and interactively contribute 11.42-15.25%explanatory power to the variation of paddy rice production in the studied province. Technology and input factors contribute 16.17%individually and another 8.46%interactively together with climate factors, totally contributing about 25%. Social economic factors contribute about 7%of which 4.65%is individual contribution and 2.49%is interactive contribution together with climate factors. The three factor groups individually contribute about 23%and interactively contribute additional 41%to paddy rice production. In addition every two of the three factor groups also function interactively and contribute about 22%. Among the three factor groups, technology and input are the most important factors to paddy rice production. The results of climate models support the results of paddy models, and display that solar radiation （indicated by sunshine hour variable） is the dominate climate factor for paddy rice production.展开更多
For the scientific management of farmland, it is significant to understand the spatio-temporal variability of soil organic matter and to study the influences of related factors. Using geostatistical theory, GIS spatia...For the scientific management of farmland, it is significant to understand the spatio-temporal variability of soil organic matter and to study the influences of related factors. Using geostatistical theory, GIS spatial analysis, trend analysis and a Geographically Weighted Regression （GWR） model, this study analyzed the response of soil organic matter to climate and socio-economic factors in central Heilongjiang Province during the past 25 years. Second soil survey data of China for 1979-1985, 2005 field sampling data, climate observations and socio-economic data for 1980-2005 were analyzed. First, soil organic matter in 2005 was spatially interpolated using the Co-Kriging method along with auxiliary data sets of soil type and pH. The spatio-temporal variability was then studied by comparison with the 1980s second soil census data. Next, the temporal trends in climate and socio-economic factors over the past 25 years were investigated. Finally, we examined the variation of the response of soil organic matter to climate and socio-economic factors using the GWR model spatially and temporally. The model showed that 53.82% area of the organic matter content remained constant and 29.39% has decreased during the past 25 years. The impact of precipitation on organic matter content is mainly negative, with increasing absolute values of the regression coefficient. The absolute value of regression coefficient of annual average temperature has decreased, and more areas are now under its negative effects. In addition, the areas of positive regression coefficient of annual sunshine hours have northward shifted, with the increasing absolute value of positive coefficient and decreasing absolute value of negative coefficient. The areas of positive regression coefficient of mechanized farming as a socio-economic factor have westward shifted, with the increasing absolute value of negative coefficient and decreasing absolute value of positive coefficient. The area of regions with the positive regression coefficient of irrigation has expanded. The regions with positive regression coefficient of fertilizer use have shrinked. The positive regression coefficient of mulch film consumption has significantly increased. The regression coefficient of pesticide consumption was mainly positive in the west of the study area, while it was negative to the east. Generally, GWR model is capable to investigate the influence of both climatic and socio-economic factors, avoided the insufficiency of other research based on the single perspective of climatic or socio-economic factors. Therefore, we can conclude that GWR model could provide methodological support for global change research and serve as basic reference for cultivated land quality improvement and agricultural decision making.展开更多
Sensitivity analysis （SA） is an effective tool for studying crop models; it is an important link in model localization and plays an important role in crop model calibration and application. The objectives were to （...Sensitivity analysis （SA） is an effective tool for studying crop models; it is an important link in model localization and plays an important role in crop model calibration and application. The objectives were to （i） determine influential and non-influential parameters with respect to above ground biomass （AGB）, canopy cover （CC）, and grain yield of winter wheat in the Beijing area based on the AquaCrop model under different water treatments （rainfall, normal irrigation, and over-irrigation）; and （ii） generate an AquaCrop model that can be used in the Beijing area by setting non-influential parameters to fixed values and adjusting influential parameters according to the SA results. In this study, field experiments were conducted during the 2012-2013,2013-2014, and 2014-2015 winter wheat growing seasons at the National Precision Agriculture Demonstration Research Base in Beijing, China. The extended Fourier amplitude sensitivity test （EFAST） method was used to perform SA of the AquaCrop model using 42 crop parameters, in order to verify the SA results, data from the 2013-2014 growing season were used to calibrate the AquaCrop model, and data from 2012-2013 and 2014-2015 growing seasons were val- idated. For AGB and yield of winter wheat, the total order sensitivity analysis had more sensitive parameters than the first order sensitivity analysis. For the AGB time-series, parameter sensitivity was changed under different water treatments; in comparison with the non-stressful conditions （normal irrigation and over-irrigation）, there were more sensitive parameters under water stress （rainfall）, while root development parameters were more sensitive. For CC with time-series and yield, there were more sensitive parameters under water stress than under no water stress. Two parameters sets were selected to calibrate the AquaCrop model, one group of parameters were under water stress, and the others were under no water stress, there were two more sensitive parameters （growing degree-days （GDD） from sowing to the maximum rooting depth （root） and the maximum effective rooting depth （rtx）） under water stress than under no water stress. The results showed that there was higher accuracy under water stress than under no water stress. This study provides guidelines for AquaCrop model calibration and application in Beijing, China, as well providing guidance to simplify the AquaCrop model and improve its precision, especially when many parameters are used.展开更多
A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochast...A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochastic combined （DSC） approach. Although the development of the framework was based on the characterization of the variation patterns of a global dataset, the methodology could be applied to any monthly absolute temperature record. Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal, involving polynomial functions and the Fourier method, respectively, while stochastic processes were employed to account for any remaining patterns in the temperature signal, involving seasonal autoregressive integrated moving average （SARIMA） models. A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years. The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors, suggesting that DSC models, when coupled with other ecoenvironmental models, can be used as a supplemental tool for short-term （10-year） environmental planning and decision making.展开更多
Land use and its dynamics have attracted considerable scientific attention for their significant ecological and socioeconomic implications.Many studies have investigated the past changes in land use,but efforts explor...Land use and its dynamics have attracted considerable scientific attention for their significant ecological and socioeconomic implications.Many studies have investigated the past changes in land use,but efforts exploring the potential changes in land use and implications under future scenarios are still lacking.Here we simulate the future land use changes and their impacts on ecosystem services in Northeast China(NEC) over the period of 2000–2050 using the CLUE–S(Conversion of Land Use and its Effects at Small regional extent) model under the scenarios of ecological security(ESS),food security(FSS) and comprehensive development(CDS).The model was validated against remote sensing data in 2005.Overall,the accuracy of the CLUE–S model was evaluated at 82.5%.Obtained results show that future cropland changes mainly occur in the Songnen Plain and the Liaohe Plain,forest and grassland changes are concentrated in the southern Lesser Khingan Mountains and the western Changbai Mountains,while the Sanjiang Plain will witness major changes of the wetlands.Our results also show that even though CDS is defined based on the goals of the regional development plan,the ecological service value(ESV) under CDS is RMB 2656.18 billion in 2050.The ESV of CDS is lower compared with the other scenarios.Thus,CDS is not an optimum scenario for eco-environmental protection,especially for the wetlands,which should be given higher priority for future development.The issue of coordination is also critical in future development.The results can help to assist structural adjustments for agriculture and to guide policy interventions in NEC.展开更多
基金financed by the National Natural Science Foundation of China (41501111 and 41271112)the National Non-profit Institute Research Grant of Chinese Academy of Agricultural Sciences (CAAS) (IARRP-2015-10)
文摘High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring. However, the major data sources of high-resolution images are not owned by China. The cost of large scale use of high resolution imagery data becomes prohibitive. In pace of the launch of the Chinese ＂High Resolution Earth Observation Systems＂, China is able to receive superb high-resolution remotely sensed images （GF series） that equalizes or even surpasses foreign similar satellites in respect of spatial resolution, scanning width and revisit period. This paper provides a perspective of using high resolution remote sensing data from satellite GF-1 for agriculture monitoring. It also assesses the applicability of GF-1 data for agricultural monitoring, and identifies potential applications from regional to national scales. GF-1＇s high resolution （i.e., 2 m/8 m）, high revisit cycle （i.e., 4 days）, and its visible and near-infrared （VNIR） spectral bands enable a continuous, efficient and effective agricultural dynamics monitoring. Thus, it has gradually substituted the foreign data sources for mapping crop planting areas, monitoring crop growth, estimating crop yield, monitoring natural disasters, and supporting precision and facility agriculture in China agricultural remote sensing monitoring system （CHARMS）. However, it is still at the initial stage of GF-1 data application in agricultural remote sensing monitoring. Advanced algorithms for estimating agronomic parameters and soil quality with GF-1 data need to be further investigated, especially for improving the performance of remote sensing monitoring in the fragmented landscapes. In addition, the thematic product series in terms of land cover, crop allocation, crop growth and production are required to be developed in association with other data sources at multiple spatial scales. Despite the advantages, the issues such as low spectrum resolution and image distortion associated with high spatial resolution and wide swath width, might pose challenges for GF-1 data applications and need to be addressed in future agricultural monitoring.
基金supported by the National Natural Science Foundation of China (41371396,41401491 and 41471364)the Introduction of International Advanced Agricultural Science and Technology,Ministry of Agriculture,China (948 Program,2011-G6)the Agricultural Scientific Research Fund of Outstanding Talents and the Open Fund for the Key Laboratory of Agri-informatics,Ministry of Agriculture,China (2013009)
文摘Using simultaneously collected remote sensing data and field measurements, this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1 （GF-1） wide field of view （WFV） camera, environment and disaster monitoring and forecasting satellite （H J-l） charge coupled device （CCD）, and Landsat-8 opera- tional land imager （OLI） data for estimating the leaf area index （LAI） of winter wheat via reflectance and vegetation indices （VIs）. The accuracies of these LAI estimates were then assessed through comparison with an empirical model and the PROSAIL radiative transfer model. The effects of radiation calibration, spectral response functions, and spatial resolution on discrepancies in the LAI estimates between the different sensors were also analyzed. The results yielded the following observations： （1） The correlation between reflectance from different sensors is relative good, with the adjusted coefficients of determination （R2） between 0.375 to 0.818. The differences in reflectance are ranging from 0.002 to 0.054. The correlation between VIs from different sensors is high with the R2 between 0.729 and 0.933. The differences in the VIs are ranging from 0.07 to 0.156. These results show the three sensors＇ images can all be used for cross calibration of the reflectance and VIs. （2） The four VIs from the three sensors are all demonstrated to be highly correlated with LAI （R2 between 0.703 and 0.849）. The linear models associated with the 2-band enhanced vegetation index （EVI2）, which feature the highest R2 （higher than 0.746） and the lowest root mean square errors （RMSE） （less than 0.21）, were selected to estimate the winter wheat LAI. The accuracy of the estimated LAI from Landsat-8 was the highest, with the relative errors （RE） of 2.18% and an RMSE of 0.13, while the H J-1 was the lowest, with the RE of 2.43% and the RMSE of 0.15. （3） The inversion errors in the different sensors＇ LAI estimates using the PROSAIL model are small. The accuracy of the GF-1 is the highest with the RE of 3.44%, and the RMSE of 0.22, whereas that of the H J-1 is the lowest with the RE of 4.95%, and the RMSE of 0.26. （4） The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored, but the effects of spatial resolution are significant and must be taken into consideration in practical applications.
基金supported and financed by the National Basic Research Program of China(973 Program,2010CB951504)the National Natural Science Foundation of China(41271112)the National Non-Profit Institute Research Grant of Chinese Academy of Agricultural Sciences,China(IARRP-2014-2)
文摘To feed the increasing world population, more food needs to be produced from agricultural land systems. Solutions to produce more food with fewer resources while minimizing adverse environmental and ecological consequences require sustainable agricultural land use practices as supplementary to advanced biotechnology and agronomy. This review paper, from a land system perspective, systematically proposed and analyzed three interactive strategies that could possibly raise future food production under global change. By reviewing the current literatures, we suggest that cropland expansion is less possible amid iferce land competition, and it is likely to do less in increasing food production. Moreover, properly allocating crops in space and time is a practical way to ensure food production. Climate change, dietary shifts, and other socio-economic drivers, which would shape the demand and supply side of food systems, should be taken into consideration during the decision-making on rational land management in respect of sustainable crop choice and allocation. And ifnally, crop-speciifc agricultural intensiifcation would play a bigger role in raising future food production either by increasing the yield per unit area of individual crops or by increasing the number of crops sown on a particular area of land. Yet, only when it is done sustainably is this a much more effective strategy to maximize food production by closing yield and harvest gaps.
基金supported by the open research fund of the Key Laboratory of Agri-informatics,Ministry of Agriculture and the fund of Outstanding Agricultural Researcher,Ministry of Agriculture,China
文摘By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.
基金financially supported by the Non-Profit Research Grant of the National Administration of Surveying,Mapping and Geoinformation of China (201512028)the National Natural Science Foundation of China (41271112)
文摘How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information. This study, taking Heilongjiang Province as the study area, aims to use time-series moderate resolution imaging spectroradiometer （MODIS） surface reflectance product （MOD09A1） data to evaluate the importance of spectral and temporal features for crop classification. In doing so, a feature selection strategy based on separability index （SI） was first used to rank the most important spectro-temporal features for crop classification. Ten feature scenarios with different spectral and temporal variable combinations were then devised, which were used for crop classification using the support vector machine and their accuracies were finally assessed with the same crop samples. The results show that the normalized difference tillage index （NDTI）, land surface water index （LSWl） and enhanced vegetation index （EVI） are the most informative spectral features and late August to early September is the most informative temporal window for identifying crops in Heilongjiang for the observed year 2011. Spectral diversity and time variety are both vital for crop classification, and their combined use can improve the accuracy by about 30% in comparison with single image. The feature selection technique based on SI analysis is superior for achieving high crop classification accuracy （producers＇ accuracy of 94.03% and users＇ accuracy of 93.77%） with a small number of features. Increasing temporal resolution is not necessarily important for improving the classification accuracies for crops, and a relatively high classification accuracy can be achieved as long as the images associated with key phenological phrases are retained.
基金National Natural Science Foundation, No.41171382 National Basic Program of China （973 Program）, No.2010CB951502
文摘Rice＇s spatial-temporal distributions, which are critical for agricultural, environ- mental and food security research, are affected by natural conditions as well as socio-eco- nomic developments. Based on multi-source data, an effective model named the Spatial Production Allocation Model （SPAM） which integrates arable land distribution, administrative unit statistics of crop data, agricultural irrigation data and crop suitability data, was used to get a series of spatial distributions of rice area and production with 10-km pixels at a national scale -it was applied from the early 1980s onwards and used to analyze the pattern of spatial and temporal changes. The results show that significant changes occurred in rice in China during 1980-2010. Overall, more than 50% of the rice area decreased, while nearly 70% of rice production increased in the change region during 1980-2010. Spatially, most of the increased area and production were in Northeast China, especially, in Jilin and Heilongjiang; most of the decreased area and production were located in Southeast China, especially, in regions of rapidly urbanization in Guangdong, Fujian and Zhejiang. Thus, the centroid of rice area was moved northeast approximately 230 km since 1980, and rice production about 320 km, which means rice production moved northeastward faster than rice area because of the significant rice yield increase in Northeast China. The results also show that rice area change had a decisive impact on rice production change. About 54.5% of the increase in rice pro- duction is due to the expansion of sown area, while around 83.2% of the decrease in rice production is due to contraction of rice area. This implies that rice production increase may be due to area expansion and other non-area factors, but reduced rice production could largely be attributed to rice area decrease.
基金supported by the Opening Project of the Key Laboratory of Agri-Informatics,Ministry of Agriculture of China(2012004)the National Basic Research Program of China(973 Program,2010CB951500)+2 种基金the Innovation Project of Chinese Academy of Agricultural Sciencesthe National Natural Science Foundation of China(41301365)the National High-Tech R&D Program of China(863 Program,2013AA12A401)
文摘Remote sensing, in particular satellite imagery, has been widely used to map cropland, analyze cropping systems, monitor crop changes, and estimate yield and production. However, although satellite imagery is useful within large scale agriculture applications （such as on a national or provincial scale）, it may not supply sufifcient information with adequate resolution, accurate geo-referencing, and specialized biological parameters for use in relation to the rapid developments being made in modern agriculture. Information that is more sophisticated and accurate is required to support reliable decision-making, thereby guaranteeing agricultural sustainability and national food security. To achieve this, strong integration of information is needed from multi-sources, multi-sensors, and multi-scales. In this paper, we propose a new framework of satellite, aerial, and ground-integrated （SAGI） agricultural remote sensing for use in comprehensive agricultural monitoring, modeling, and management. The prototypes of SAGI agriculture remote sensing are ifrst described, followed by a discussion of the key techniques used in joint data processing, image sequence registration and data assimilation. Finally, the possible applications of the SAGI system in supporting national food security are discussed.
基金supported and financed by the National Basic Research Program of China(973 Program,2010CB951504)the National Natural Science Foundation of China(41201089 and 41271112)
文摘Rice is one of the most important grain crops in Northeast China （NEC） and its cultivation is sensitive to climate change. This study aimed to explore the spatio-temporal changes in the NEC rice planting area over the period of 1980-2010 and to analyze their relationship to climate change. To do so, the CLUE-S （conversion of land use and its effects at small region extent） model was ifrst updated and used to simulate dynamic changes in the rice planting area in NEC to understand spatio-temporal change trends during three periods： 1980-1990, 1990-2000 and 2000-2010. The changing results in individual periods were then linked to climatic variables to investigate the climatic drivers of these changes. Results showed that the NEC rice planting area expanded quickly and increased by nearly 4.5 times during 1980-2010. The concentration of newly planted rice areas in NEC constantly moved northward and the changes were strongly dependent on latitude. This conifrmed that climate change, increases in temperature in particular, greatly inlfuenced the shift in the rice planting area. The shift in the north limit of the NEC rice planting area generally followed a 1＆#176;C isoline migration pattern, but with an obvious time-lag effect. These ifndings can help policy makers and crop producers take proper adaptation measures even when exposed to the global warming situation in NEC.
基金supported by the National Basic Research Program of China(2010CB951502)the Key Technologies R&D Program of China during the 12th Five-Year Plan period(2013BAC09B04)
文摘With consecutive occurrences of drought disasters in China in recent years, it is important to estimate their potential impacts on regional crop production. In this study, we detect the impacts of drought on wheat and maize yield and their changes at a 0.5＆#176;＆#215;0.5＆#176; grid scale in the wheat-maize rotation planting area in the North China Plain under the A1B climate change scenario using the Decision Support System for Agrotechnology Transfer （DSSAT） model and the outputs of the regional climate modeling system-Providing Regional Climates for Impacts Studies （PRECIS）. Self-calibrating palmer drought severity index was used as drought recognition indicator. Two time slices used for the study were the baseline （1961-1990） and 40 years of 2011-2050. The results indicate that the potential planting region for double crop system of wheat-maize would expend northward. The statistic conclusions of crop simulations varied considerably between wheat and maize. In disaster-affected seasons, wheat yield would increase in the future compared with baseline yields, whereas in opposite for maize yield. Potential crop yield reductions caused by drought would be lower for wheat and higher for maize, with a similar trend found for the ratio of potential crop yield reductions for both crops. It appears that the negative impact of drought on maize was larger than that on wheat under climate change A1B scenario.
基金financially supported by the Opening Foundation of the Key Laboratory of Agricultural Information Technology,Ministry of Agriculture,China (2014009)the Non-Profit Research Grant of the National Administration of Surveying,Mapping and Geoinformation of China (201512028)+1 种基金the National Natural Science Fo undation of China (41271112)the Youth Foundation of Heilongjiang Academy of Agricultural Science,China (QN024)
文摘The successful launched Gaofen satellite no. 1 wide field-of-view （GF-1 WFV） camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Bei＇an City, Northeast China as the study area, aims to investigate the potential of GF-1 WFV images for crop identification and explore how to fully use its spectral, textural and temporal information to improve classification accuracy. In doing so, an object-based and Random Forest （RF） algorithm was used for crop mapping. The results showed that classification based on an optimized single temporal GF-1 image can achieve an overall accuracy of about 83%, and the addition of textural features can im- prove the accuracy by 8.14%. Moreover, the multi-temporal GF-1 data can produce a classification map of crops with an overall accuracy of 93.08% and the introduction of textural variables into multi-temporal GF-1 data can only increase the accuracy by about 1%, which suggests the importance of temporal information of GF-1 for crop mapping in comparison with single temporal data. By comparing classification results of GF-1 data with different feature inputs, it is concluded that GF-1 WFV data in general can meet the mapping efficiency and accuracy requirements of regional crop. But given the unique spectral characteristics of the GF-1 WFV imagery, the use of textual and temporal information is needed to yield a satisfactory accuracy.
基金financial support for our initial and ongoing research from the National Natural Science Foundation of China (41101170 and 41201184)
文摘Rice area has been expanding rapidly during the past 30 years under the influence of global change in northeastern China, which is the northernmost region of rice cultivation in China. However, the spatio-temporal dynamic changes in rice area are still unclear, although they may have important policy implications for environmental protection and adaptation to climate change. In this study, we aimed to identify the dynamic changes of the rice area in Heilongjiang Province of northeastern China by extracting data from multiple Landsat images. The study used ground quadrats selected from Google Earth and the extraction of a confusion matrix to verify the accuracy of extraction. The overall accuracy of the extracted rice area was higher than 95% as a result of using the artificial neural network （ANN） classification method. The results showed that the rice area increased by approximately 2.4×10^6 ha during the past 30 years at an annual rate of 8.0×10^4 ha, and most of the increase occurred after 2000. The central latitude of the rice area shifted northwards from 46 to 47°N during the study period, and moved eastwards from 130 to 133°E. The rice expansion area accounted for 98% of the total change in rice area, and rice loss was notably rare. The rice expansion was primarily from dryland. In addition, rice cultivation in marshland and grassland played a minor role in the rice expansion in this region.
基金Foundation： National Natural Science Foundation of China, No.41171328, No.41201184, No.41101537 National Basic Program of China, No.2010CB951502
文摘Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are still lacking. Based on the cross-entropy theory, a spatial production allocation model （SPAM） has been developed for presenting spa- tio-temporal dynamics of maize cropping system in Northeast China during 1980-2010. The simulated results indicated that （1） maize sown area expanded northwards to 48~N before 2000, after that the increased sown area mainly occurred in the central and southern parts of Northeast China. Meanwhile, maize also expanded eastwards to 127°E and lower elevation （less than 100 m） as well as higher elevation （mainly distributed between 200 m and 350 m）; （2） maize yield has been greatly promoted for most planted area of Northeast China, espe- cially in the planted zone between 42°N and 48°N, while the yield increase was relatively homogeneous without obvious longitudinal variations for whole region; （3） maize planting density increased gradually to a moderately high level over the investigated period, which reflected the trend of aggregation of maize cultivation driven by market demand.
基金financed by the National Basic Research Program of China(2010CB951504)the National Natural Science Foundation of China(41271112 and 40930101)the National Nonprofit Institute Research Grant of CAAS(IARRP-2014-16)
文摘Although climate change impacts and agricultural adaptations have been studied extensively, how smallholder farmers perceive climate change and adapt their agricultural activities is poorly understood. Survey-based data （presents farmers＇ personal perceptions and adaptations to climate change） associated with external biophysical-socioeconomic data （presents real-world climate change） were used to develop a farmer-centered framework to explore climate change impacts and agricultural adaptations at a local level. A case study at Bin County （1980s-2010s）, Northeast China, suggested that increased annual average temperature （0.6＆#176;C per decade） and decreased annual precipitation （46 mm per decade, both from meteorological datasets） were correctly perceived by 76 and 66.9%, respectively, of farmers from the survey, and that a longer growing season was conifrmed by 70%of them. These reasonably correct perceptions enabled local farmers to make appropriate adaptations to cope with climate change：Longer season alternative varieties were found for maize and rice, which led to a signiifcant yield increase for both crops. The longer season also affected crop choice：More farmers selected maize instead of soybean, as implicated from survey results by a large increase in the maize growing area. Comparing warming-related factors, we found that precipitation and agricultural disasters were the least likely causes for farmers＇ agricultural decisions. As a result, crop and variety selection, rather than disaster prevention and infrastructure improvement, was the most common ways for farmers to adapt to the notable warming trend in the study region.
基金National Major Science and Technology Program for Water Pollution Control and Treatment,No.2017ZX07101001
文摘Over the past few decades, built-up land in China has increasingly expanded with rapid urbanization, industrialization and rural settlements construction. The expansions encroached upon a large amount of cropland, placing great challenges on national food security. Although the impacts of urban expansion on cropland have been intensively illustrated, few attentions have been paid to differentiating the effects of growing urban areas, rural settlements, and industrial/transportation land. To fill this gap and offer comprehensive implications on framing policies for cropland protection, this study investigates and compares the spatio-temporal patterns of cropland conversion to urban areas, rural settlements, and industrial/transportation land from 1987 to 2010, based on land use maps interpreted from remote sensing imagery. Five indicators were developed to analyze the impacts of built-up land expansion on cropland in China. We find that 42,822 km2 of cropland were converted into built-up land in China, accounting for 43.8% of total cropland loss during 1987-2010. Urban growth showed a greater impact on cropland loss than the expansion of rural settlements and the expansion of industrial/transportation land after 2000. The contribution of rural settlement expansion decreased; however, rural settlement saw the highest percentage of traditional cropland loss which is generally in high quality. The contribution of industrial/transportation land expansion increased dramatically and was mainly distributed in major food production regions. These changes were closely related to the economic restructuring, urban-rural transformation and government policies in China. Future cropland conservation should focus on not only finding a reasonable urbanization mode, but also solving the ＂hollowing village＂ problem and balancing the industrial transformations.
基金National Natural Science Foundation of China,No.41171328,No.41201184,No.41101170
文摘Based on county-level crop statistics and other ancillary information, spatial distri- bution of maize in the major maize-growing areas （latitudes 39°-48°N） was modelled for the period 1980-2010 by using a cross-entropy-based spatial allocation model. Maize extended as far north as the northern part of the Lesser Khingan Mountains during the period, and the area sown to maize increased by about 5 million ha. More than half of the increase occurred before 2000, and more than 80% of it in the climate transitional zone, where the annual ac- cumulated temperature （AAT） was 2800-3400 ℃.d. Regions with AAT of 3800-4000 ℃-d became more important, accounting for more than 25% of the increase after 2000. The ex- pansion of maize was thus closely related to warming, although some variation in the distri- bution was noticed across zones in relation to the warming, indicating that maize in northeast China may have adapted successfully to the warming by adjusting its spatial distribution to match the changed climate.
基金financed by the National Basic Research Program of China(2010CB951502)
文摘In the study, an improved approach was proposed to identify the contribution shares of three group factors that are climate, technology and input, social economic factors by which the grain production is shaped. In order to calibrate the method, Jiangxi Province, one of the main paddy rice producers in China was taken as an example. Based on 50 years （1961-2010） meteorological and statistic data, using GIS and statistical analysis tools, the three group factors that in certain extent impact China＇s paddy rice production have been analyzed quantitatively. The individual and interactive contribution shares of each factor group have been identiifed via eta square （η2）. In the paper, two group ordinary leasr square （OLS） models, paddy models and climate models, have been constructed for further analysis. Each model group consists of seven models, one full model and six partial models. The results of paddy models show that climate factors individually and interactively contribute 11.42-15.25%explanatory power to the variation of paddy rice production in the studied province. Technology and input factors contribute 16.17%individually and another 8.46%interactively together with climate factors, totally contributing about 25%. Social economic factors contribute about 7%of which 4.65%is individual contribution and 2.49%is interactive contribution together with climate factors. The three factor groups individually contribute about 23%and interactively contribute additional 41%to paddy rice production. In addition every two of the three factor groups also function interactively and contribute about 22%. Among the three factor groups, technology and input are the most important factors to paddy rice production. The results of climate models support the results of paddy models, and display that solar radiation （indicated by sunshine hour variable） is the dominate climate factor for paddy rice production.
基金financed by the National Basic Research Program of China(2010CB951502)the National Natural Science Foundation of China(41101537,40930101,41201184 and 71203157)
文摘For the scientific management of farmland, it is significant to understand the spatio-temporal variability of soil organic matter and to study the influences of related factors. Using geostatistical theory, GIS spatial analysis, trend analysis and a Geographically Weighted Regression （GWR） model, this study analyzed the response of soil organic matter to climate and socio-economic factors in central Heilongjiang Province during the past 25 years. Second soil survey data of China for 1979-1985, 2005 field sampling data, climate observations and socio-economic data for 1980-2005 were analyzed. First, soil organic matter in 2005 was spatially interpolated using the Co-Kriging method along with auxiliary data sets of soil type and pH. The spatio-temporal variability was then studied by comparison with the 1980s second soil census data. Next, the temporal trends in climate and socio-economic factors over the past 25 years were investigated. Finally, we examined the variation of the response of soil organic matter to climate and socio-economic factors using the GWR model spatially and temporally. The model showed that 53.82% area of the organic matter content remained constant and 29.39% has decreased during the past 25 years. The impact of precipitation on organic matter content is mainly negative, with increasing absolute values of the regression coefficient. The absolute value of regression coefficient of annual average temperature has decreased, and more areas are now under its negative effects. In addition, the areas of positive regression coefficient of annual sunshine hours have northward shifted, with the increasing absolute value of positive coefficient and decreasing absolute value of negative coefficient. The areas of positive regression coefficient of mechanized farming as a socio-economic factor have westward shifted, with the increasing absolute value of negative coefficient and decreasing absolute value of positive coefficient. The area of regions with the positive regression coefficient of irrigation has expanded. The regions with positive regression coefficient of fertilizer use have shrinked. The positive regression coefficient of mulch film consumption has significantly increased. The regression coefficient of pesticide consumption was mainly positive in the west of the study area, while it was negative to the east. Generally, GWR model is capable to investigate the influence of both climatic and socio-economic factors, avoided the insufficiency of other research based on the single perspective of climatic or socio-economic factors. Therefore, we can conclude that GWR model could provide methodological support for global change research and serve as basic reference for cultivated land quality improvement and agricultural decision making.
基金supported by the National Natural Science Foundation of China(41571416)the Natural Science Foundation of Beijing,China(4152019)the Beijing Academy of Agricultural and Forestry Sciences Innovation Capacity Construction Specific Projects,China(KJCX20150409)
文摘Sensitivity analysis （SA） is an effective tool for studying crop models; it is an important link in model localization and plays an important role in crop model calibration and application. The objectives were to （i） determine influential and non-influential parameters with respect to above ground biomass （AGB）, canopy cover （CC）, and grain yield of winter wheat in the Beijing area based on the AquaCrop model under different water treatments （rainfall, normal irrigation, and over-irrigation）; and （ii） generate an AquaCrop model that can be used in the Beijing area by setting non-influential parameters to fixed values and adjusting influential parameters according to the SA results. In this study, field experiments were conducted during the 2012-2013,2013-2014, and 2014-2015 winter wheat growing seasons at the National Precision Agriculture Demonstration Research Base in Beijing, China. The extended Fourier amplitude sensitivity test （EFAST） method was used to perform SA of the AquaCrop model using 42 crop parameters, in order to verify the SA results, data from the 2013-2014 growing season were used to calibrate the AquaCrop model, and data from 2012-2013 and 2014-2015 growing seasons were val- idated. For AGB and yield of winter wheat, the total order sensitivity analysis had more sensitive parameters than the first order sensitivity analysis. For the AGB time-series, parameter sensitivity was changed under different water treatments; in comparison with the non-stressful conditions （normal irrigation and over-irrigation）, there were more sensitive parameters under water stress （rainfall）, while root development parameters were more sensitive. For CC with time-series and yield, there were more sensitive parameters under water stress than under no water stress. Two parameters sets were selected to calibrate the AquaCrop model, one group of parameters were under water stress, and the others were under no water stress, there were two more sensitive parameters （growing degree-days （GDD） from sowing to the maximum rooting depth （root） and the maximum effective rooting depth （rtx）） under water stress than under no water stress. The results showed that there was higher accuracy under water stress than under no water stress. This study provides guidelines for AquaCrop model calibration and application in Beijing, China, as well providing guidance to simplify the AquaCrop model and improve its precision, especially when many parameters are used.
基金This research was supported by the Ministry of Science and Technology of China，National Basic Research Program of China (Grant No.2010CB951504).The authors acknowledge support from the Flemish Interuniversity Council，the Ghent University Laboratory of Soil Science for the writing of this paper
文摘A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochastic combined （DSC） approach. Although the development of the framework was based on the characterization of the variation patterns of a global dataset, the methodology could be applied to any monthly absolute temperature record. Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal, involving polynomial functions and the Fourier method, respectively, while stochastic processes were employed to account for any remaining patterns in the temperature signal, involving seasonal autoregressive integrated moving average （SARIMA） models. A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years. The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors, suggesting that DSC models, when coupled with other ecoenvironmental models, can be used as a supplemental tool for short-term （10-year） environmental planning and decision making.
基金Agricultural Outstanding Talents Research Foundation of Ministry of Agriculture(MOA)Key Laboratory of Agri–Informatics Foundation of MOA No.2015001+1 种基金Natural Science Foundation of Hubei Province No.2016CFB558The Fundamental Research Funds for the Central Universities,No.CCNU15A05058
文摘Land use and its dynamics have attracted considerable scientific attention for their significant ecological and socioeconomic implications.Many studies have investigated the past changes in land use,but efforts exploring the potential changes in land use and implications under future scenarios are still lacking.Here we simulate the future land use changes and their impacts on ecosystem services in Northeast China(NEC) over the period of 2000–2050 using the CLUE–S(Conversion of Land Use and its Effects at Small regional extent) model under the scenarios of ecological security(ESS),food security(FSS) and comprehensive development(CDS).The model was validated against remote sensing data in 2005.Overall,the accuracy of the CLUE–S model was evaluated at 82.5%.Obtained results show that future cropland changes mainly occur in the Songnen Plain and the Liaohe Plain,forest and grassland changes are concentrated in the southern Lesser Khingan Mountains and the western Changbai Mountains,while the Sanjiang Plain will witness major changes of the wetlands.Our results also show that even though CDS is defined based on the goals of the regional development plan,the ecological service value(ESV) under CDS is RMB 2656.18 billion in 2050.The ESV of CDS is lower compared with the other scenarios.Thus,CDS is not an optimum scenario for eco-environmental protection,especially for the wetlands,which should be given higher priority for future development.The issue of coordination is also critical in future development.The results can help to assist structural adjustments for agriculture and to guide policy interventions in NEC.
