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From statistics to grids:A two-level model to simulate crop pattern dynamics

Journal of Integrative Agriculture 2022年第6期21卷 1786-1798页

作者： XIA Tian wu wen-bin ZHOU Qing-bo Peter HVERBURG YANG Peng HU Qiong YE Li-ming ZHU Xiao-juan Key Laboratory for Geographical Process Analysis&Simulation Hubei Province/College of Urban&Environmental ScienceCentral China Normal UniversityWuhan 430079P.R.China Key Laboratory of Agricultural Remote Sensing Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijing 100081P.R.China Agricultural Information Institute Chinese Academy of Agricultural SciencesBeijing 100081P.R.China Institute for Environmental Studies VU University AmsterdamAmsterdam 1085The Netherlands Department of Geology Ghent UniversityGhent 9000Belgium Commercial and Economic Law School China University of Political Science and LawBeijing 100088P.R.China

Crop planting patterns are an important component of agricultural land systems.These patterns have been significantly changed due to the combined impacts of climatic changes and socioeconomic developments.However,the extent of these changes and their possible impacts on the environment,terrestrial landscapes and rural livelihoods are largely unknown due to the lack of spatially explicit datasets including crop planting patterns.To fill this gap,this study proposes a new method for spatializing statistical data to generate multitemporal crop planting pattern datasets.This method features a two-level model that combines a land-use simulation and a crop pattern simulation.The output of the first level is the spatial distribution of the cropland,which is then used as the input for the second level,which allocates crop censuses to individual gridded cells according to certain rules.The method was tested using data from 2000 to 2019 from Heilongjiang Province,China,and was validated using remote sensing images.The results show that this method has high accuracy for crop area spatialization.Spatial crop pattern datasets over a given time period can be important supplementary information for remote sensing and thus support a wide range of application in agricultural land systems.

关键词： crop planting pattern spatialization simulation spatiotemporal change remote sensing

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中国中部区域省际贸易流量与贸易壁垒研究

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价格月刊 2024年第4期 88-94页

作者：文武斌农强澳门科技大学商学院中国澳门57200

通过分析中国省际贸易流量与贸易壁垒现状,发现中部区域的省际贸易流量与当地经济发展水平大体相契合。具体而言,中部各省份的产品及服务流出量略大于流入量,省际贸易循环以中部区域内部为集中点。当前,各区域经济发展仍然存在各区域贸... 详细信息

通过分析中国省际贸易流量与贸易壁垒现状,发现中部区域的省际贸易流量与当地经济发展水平大体相契合。具体而言,中部各省份的产品及服务流出量略大于流入量,省际贸易循环以中部区域内部为集中点。当前,各区域经济发展仍然存在各区域贸易壁垒障碍尚未解决、各区域产业集群规模尚未成型、各区域经济循环规模作用尚未发挥、各区域基础产业运作优势尚未集聚等问题。根据研究结论,提出了推进区域化合理分工、强化产业链效能,借助优势突破地域局限、健全基础设施体系,融圈入群建设城市圈、夯实产业发展基础,立足区域特色优势、推进产业体系优化升级,创新贸易金融业务模式、催化省际贸易快速增长,加快贸易技术投入与优化升级、提高产品核心竞争力等对策建议。

关键词：中部六省省际贸易贸易流量贸易壁垒

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Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

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Journal of Integrative Agriculture 2017年第2期16卷 348-359页

作者： TAO Jian-bin wu wen-bin ZHOU Yong WANG Yu JIANG Yan Key Laboratory of Agri-informatics Ministry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/School of Urban and Environmental Sciences Central China Normal University

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.

关键词： time-series MODIS data phenological feature peak before wintering winter wheat mapping

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Mapping regional cropping patterns by using GF-1 WFV sensor data

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Journal of Integrative Agriculture 2017年第2期16卷 337-347页

作者： SONG Qian ZHOU Qing-bo wu wen-bin HU Qiong LU Miao LIU Shu-bin Key Laboratory of Agri-informatics Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing 100081 P.R. China Remote Sensing Technology Center Heilongjiang Academy of Agricultural Sciences Harbin 150086 P.R.China

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.

关键词： crop mapping GF-1 object-based Random Forest

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Remote sensing for agricultural applications

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Journal of Integrative Agriculture 2017年第2期16卷 239-241页

作者： zhengwei yang wu wen-bin liping di berk ustundag research and development division usda national agricultural statistics service VA22030USA institute of agricultural resources and regional planning chinese academy of agricultural sciences Beijing 1 00081PR.China center for spatial information science and systems george mason university VA 22030USA agricultural and environmental informatics application&research center/faculty of informatics and computer engineering istanbul technical university Istanbul 34469Turkey

Agricultural geospatial information is critical for agricultural policy formulation and decision making, land use monitoring, agricultural sustainability, crop acreage and yield estimation, disaster assessment, bioenergy crop inventory, food security policy, environmental assessment, carbon accounting, and other research topics that are of vital importance to agricul- ture and economy. Remote sensing technology enables us to collect, process, and analyze remotely sensed data and to retrieve, synthesize, visualize valuable geospatial information for agriculture uses. Specifically, remote sensing technology empowers capability for large scale field level or regional assessment and monitoring of crop land cover,

关键词： Remote sensing for agricultural applications MODIS RUSLE NDVI data

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Estimating the crop leaf area index using hyperspectral remote sensing

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Journal of Integrative Agriculture 2016年第2期15卷 475-491页

作者： LIU Ke ZHOU Qing-bo wu wen-bin XIA Tian TANG Hua-jun Key Laboratory of Agri-Informatics Ministry of Agriculture Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences College of Urban & Environmental Sciences Central China Normal University

The leaf area index（LAI） is an important vegetation parameter,which is used widely in many applications.Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies.During the last two decades,hyperspectral remote sensing has been employed increasingly for crop LAI estimation,which requires unique technical procedures compared with conventional multispectral data,such as denoising and dimension reduction.Thus,we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques.First,we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation.Second,we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types：approaches based on statistical models,physical models（i.e.,canopy reflectance models）,and hybrid inversions.We summarize and evaluate the theoretical basis and different methods employed by these approaches（e.g.,the characteristic parameters of LAI,regression methods for constructing statistical predictive models,commonly applied physical models,and inversion strategies for physical models）.Thus,numerous models and inversion strategies are organized in a clear conceptual framework.Moreover,we highlight the technical difficulties that may hinder crop LAI estimation,such as the ＂curse of dimensionality＂ and the ill-posed problem.Finally,we discuss the prospects for future research based on the previous studies described in this review.

关键词： hyperspectral inversion leaf area index LAI retrieval

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Comparative analysis of GF-1,HJ-1,and Landsat-8 data for estimating the leaf area index of winter wheat

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Journal of Integrative Agriculture 2017年第2期16卷 266-285页

作者： LI He CHEN Zhong-xin JIANG Zhi-wei wu wen-bin REN Jian-qiang LIU bin Tuya Hasi Key Laboratory of Agri-informatics Ministry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences National Meteorological Information Center China Meteorological Administration

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 hig

关键词： GF-1 WFV H J-1 CCD Landsat-80LI leaf area index PROSAIL vegetation indices

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Perspective of Chinese GF-1 high-resolution satellite data in agricultural remote sensing monitoring

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Journal of Integrative Agriculture 2017年第2期16卷 242-251页

作者： ZHOU Qing-bo YU Qiang-yi LIU Jia wu wen-bin TANG Hua-jun Key Laboratory of Agri-informatics Ministry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences College of Urban & Environmental Sciences Central China Normal University

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 fo

关键词： GF-1 high resolution agricultural monitoring remote sensing CHARMS

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Systematic Synthesis of Impacts of Climate Change on China's Crop Production System

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Journal of Integrative Agriculture 2014年第7期13卷 1413-1417页

作者： TANG Hua-jun wu wen-bin YANG Peng LI Zheng-guo Key Laboratory of Agri-Informatics Ministry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences Key Laboratory of Agri-Informatics Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Key Laboratory of Agri-Informatics Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Key Laboratory of Agri-Informatics Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences

Global climate change is real and already taking place. The most recent Fifth Assessment Report of the Intergov- ernmental Panel on Climate Change （IPCC AR5） stated that global land and ocean surface temperature increased by 0.85℃ over the period of 1880 to 2012 （IPCC Climate Change 2013）. China is among the most affected countries by global climate change.

关键词： Systematic Synthesis of Impacts of Climate Change on China’s Crop Production System

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Influence of Climate and Socio-Economic Factors on the Spatio-Temporal Variability of Soil Organic Matter: A Case Study of Central Heilongjiang Province, China

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Journal of Integrative Agriculture 2014年第7期13卷 1486-1500页

作者： SHI Shu-qin CAO Qi-wen YAO Yan-min TANG Hua-jun YANG Peng wu wen-bin XU Heng-zhou LIU Jia LI Zheng-guo School of Management Tianjin Polytechnic University Key Laboratory of Agri-Informatics Ministry of Agriculture/Institute of Agricultural Resources & Regional Planning Chinese Academy of Agricultural Sciences College of Management Tianjin University

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 ha

关键词： soil organic matter climate socio-economic factors GWR Heilongjiang Province

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