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A comparative analysis of five global cropland datasets in China

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Science China Earth Sciences 2016年第12期59卷 2307-2317页

作者： LU Miao WU WenBin ZHANG Li LIAO AnPing PENG Shu TANG HuaJun Key Laboratory of Agri-Informatics Ministry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences National Geomatics Centre of China

Accurate information of cropland area and spatial location is critical for studies of national food security, global environmental change, terrestrial ecosystem geophysics and the geochemical cycle. In this paper, we compared five global cropland datasets in circa 2010 of China from in terms of cropland area and spatial location, including Global Land30, FROM-GLC, Glob Cover, MODIS Collection 5, and MODIS Cropland. The results showed that the accuracies of cropland area and spatial location of Globe Land30 were higher than the other four products. The cropland areas of the five products varied in most of the provinces. Compared with the statistical data, the best goodness of fit was obtained using Globe Land30, followed by MODIS Collection 5 and FROM-GLC, with MODIS Cropland and Glob Cover having the poorer accuracies. Regarding the spatial location of cropland, Globe Land30 achieved the best accuracy, followed by FROM-GLC and MODIS Collection 5, with Glob Cover and MODIS Cropland having the poorer accuracies. In addition, the spatial agreement of the five datasets was reduced from agricultural production area to pastoral area and significantly affected by elevation and slope factors. Although the spatial resolution of MODIS Collection 5 was the lowest, accuracies of the cropland area and spatial location were better than those of Glob Cover and MODIS Cropland. Therefore, high spatial resolution remote sensing images can help to improve the accuracy of the dataset during land cover mapping, while it is also important to select a suitable classification method. Furthermore, in northwestern and southeastern China, spectral mixing pixels are universal because of the complicated landscape and fragmentized topography and result in uncertainty and poor consistency when using the five products. Therefore, these regions require additional attention in future cropland mapping studies.

关键词： Land cover Cropland area Spatial location Accuracy Spatial agreement

Preliminary study on qualitative and quantitative detection of norfloxacin based on terahertz spectroscopy

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International Journal of agricultural and Biological Engineering 2017年第5期10卷 262-268页

作者： Long Yuan Li Bin Beijing Research Center for Information Technology in agriculture Beijing 100097China Key Laboratory of Agri-Informatics Ministry of AgricultureBeijing 100097China Beijing key laboratory of Digital Plant Beijing 100097China

Norfloxacin is a kind of quinolone bactericide which is widely used in food industry,livestock feed and medicine area.At present,how to develop a rapid,convenient and accurate technique for qualitative and quantitative detection of norfloxacin is still a challenge.In this study,Terahertz Time-domain Spectroscopy technology(THz-TDS)was used to explore qualitative and quantitative analysis of norfloxacin.Forty-five norfloxacin samples with different levels were prepared.The pure norfloxacin powder was mixed with polyethylene(PE)powder together and made into tablets by tablet-making machine.The minimum detection concentration was 10%and the maximum detection concentration evaluated in the paper was 90%.Then,terahertz(THz)spectra of each sample were measured on K15 THz-TDS equipment.The multiple linear regressions and the partial least squares regression algorithms were used to establish a model to make quantitative analysis.From the results,two typical fingerprint peaks of norfloxacin in terahertz band were observed at 0.944 THz and 1.206 THz,and the correlation coefficient and the root mean square error of the established model reached 0.9908 and 0.0481,respectively,which showed that the model performed well for the prediction.This preliminary study indicates that THz spectroscopy has great potential for future screening applications to detect the presence of norfloxacin residues in food,livestock feed and medicine industries.

关键词： Terahertz spectroscopy norfloxacin qualitative and quantitative detection model correlation coefficient

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

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

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

Spatio-temporal changes in rice area at the northern limits of the rice cropping system in China from 1984 to 2013

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Journal of Integrative agriculture 2017年第2期16卷 360-367页

作者： LI Zhi-peng LONG Yu-qiao TANG Peng-qin TAN Jie-yang LI Zheng-guo WU Wen-bin HU Ya-nan YANG Peng Key Laboratory of Agri-Informatics Ministry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences Institute of agricultural Economics and Regional Planning Hunan Academy of Agricultural Sciences

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.

关键词： paddy rice Landsat images artificial neural network Heilongjiang Province

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

The aboveground biomass of desert steppe and its spatiotemporal variation in western Inner Mongolia

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Research in Cold and Arid Regions 2013年第3期5卷 339-346页

作者： Tian Gao Bin Xu XiuChun Yang YunXiang Jin HaiLong Ma JinYa Li HaiDa Yu Key Laboratory of Agri-Informatics Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences

A precise understanding of the aboveground biomass of desert steppe and its spatio-temporal variation is important to understand how arid ecosystems respond to climate change and to ensure that scarce grassland resources are used rationally. On the basis of 756 ground survey quadrats sampled in western Inner Mongolia steppe in 2005-2011 and remote sensing data from the Moderate Resolu- tion Imaging Spectroradiometer （MODIS）--the normalized difference vegetation index （NDVI） dataset for the period of 2001-2011--we developed a statistical model to estimate the aboveground biomass of the desert steppe and further explored the rela- tionships between aboveground biomass and climate factors. The conclusions are as follows：（1） the aboveground biomass of the steppe in the research area was 5.27 Tg （1 Tg=1012 g） on average over 11 years; between 2001 and 2011, the aboveground biomass of the western Inner Mongolia steppe exhibited fluctuations, with no significant trend over time; （2） the aboveground biomass of the steppe in the research area exhibits distinct spatial variation and generally decreases gradually from southeast to northwest; and （3） the important factor causing intemnnual variations in aboveground biomass is precipitation during the period from January to July, but we did not find a significant relationship between the aboveground biomass and the corresponding temperature changes. The precipitation in this period is also an important factor influencing the spatial distribution of aboveground biomass （R2=0.39, P〈0.001）, while the temperature might be a minor factor （R2=0.12, P〈0.01 ）. The uncertainties in our estimate are primarily due to uncertainty in converting the fresh grass yield estimates to dry weight, underestimates of the biomass of shrubs, and error in remote sensing dataset.

关键词： Inner Mongolia desert steppe normalized difference vegetation index （NDVI） aboveground biomass climate factors

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