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Design of a spatial sampling scheme considering the spatial autocorrelation of crop acreage included in the sampling units

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Journal of Integrative Agriculture 2018年第9期17卷 2096-2106页

作者： WANG Di ZHOU Qing-bo YANG Peng chen zhong-xin Key Laboratory of Agricultural Remote Sensing Ministry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences

Information on crop acreage is important for formulating national food polices and economic planning. Spatial sampling, a combination of traditional sampling methods and remote sensing and geographic information system （GIS） technology, provides an efficient way to estimate crop acreage at the regional scale. Traditional sampling methods require that the sampling units should be independent of each other, but in practice there is often spatial autocorrelation among crop acreage contained in the sampling units. In this study, using Dehui County in Jilin Province, China, as the study area, we used a thematic crop map derived from Systeme Probatoire d＇Observation de la Terre （SPOT-5） imagery, cultivated land plots and digital elevation model data to explore the spatial autocorrelation characteristics among maize and rice acreage included in sampling units of different sizes, and analyzed the effects of different stratification criteria on the level of spatial autocorrelation of the two crop acreages within the sampling units. Moran＇s/, a global spatial autocorrelation index, was used to evaluate the spatial autocorrelation among the two crop acreages in this study. The results showed that although the spatial autocorrelation level among maize and rice acreages within the sampling units generally decreased with increasing sampling unit size, there was still a significant spatial autocorrelation among the two crop acreages included in the sampling units （Moran＇s / varied from 0.49 to 0.89）, irrespective of the sampling unit size. When the sampling unit size was less than 3000 m, the stratification design that used crop planting intensity （CPI） as the stratification criterion, with a stratum number of 5 and a stratum interval of 20% decreased the spatial autocorrelation level to almost zero for the maize and rice area included in sampling units within each stratum. Therefore, the traditional sampling methods can be used to estimate the two crop acreages. Compared with CPI, there was still a strong spatial correlation a

关键词： crop acreage spatial autocorrelation sampling unit planting intensity cultivated land fragmentation ground slope

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Effects of meteorological factors on different grades of winter wheat growth in the Huang-Huai-Hai Plain, China

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Journal of Integrative Agriculture 2016年第11期15卷 2647-2657页

作者： HUANG Qing WANG Li-min chen zhong-xin LIU Hang Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences

The sown area of winter wheat in the Huang-Huai-Hai（HHH） Plain accounts for over 65% of the total sown area of winter wheat in China. Thus, it is important to monitor the winter wheat growth condition and reveal the main factors that influence its dynamics. This study assessed the winter wheat growth condition based on remote sensing data, and investigated the correlations between different grades of winter wheat growth and major meteorological factors corresponding. First, winter wheat growth condition from sowing until maturity stage during 2011–2012 were assessed based on moderate-resolution imaging spectroradiometer（MODIS） normalized difference vegetation index（NDVI） time-series dataset. Next, correlation analysis and geographical information system（GIS） spatial analysis methods were used to analyze the lag correlations between different grades of winter wheat growth in each phenophase and the meteorological factors that corresponded to the phenophases. The results showed that the winter wheat growth conditions varied over time and space in the study area. Irrespective of the grades of winter wheat growth, the correlation coefficients between the winter wheat growth condition and the cumulative precipitation were higher than zero lag（synchronous precipitation） and one lag（pre-phenophase precipitation） based on the average values of seven phenophases. This showed that the cumulative precipitation during the entire growing season had a greater effect on winter wheat growth than the synchronous precipitation and the pre-phenophase precipitation. The effects of temperature on winter wheat growth varied according to different grades of winter wheat growth based on the average values of seven phenophases. Winter wheat with a better-than-average growth condition had a stronger correlation with synchronous temperature, winter wheat with a normal growth condition had a stronger correlation with the cumulative temperature, and winter wheat with a worse-than-average growth condition had a stronger correlation with the p

关键词： growth condition meteorological factors remote sensing spatiotemporal correlation winter wheat HuangHuai-Hai（HHH） Plain region China

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Research advances of SAR remote sensing for agriculture applications: A review

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Journal of Integrative Agriculture 2019年第3期18卷 506-525页

作者： LIU Chang-an chen zhong-xin SHAO Yun chen Jin-song Tuya Hasi PAN Hai-zhu Key Laboratory of Agricultural Remote Sensing Ministry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijing 100081P.R.China Institute of Remote Sensing and Digital Earth Chinese Academy of SciencesBeijing 100101P.R.China Laboratory of Remote Sensing Information Science Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055P.R.China

Synthetic aperture radar(SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricu

关键词： crop cropland yield soil roughness soil moisture LAI crop height scattering model quantitative remote sensing crop yield estimation SAR

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Agricultural remote sensing big data:Management and applications

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Journal of Integrative Agriculture 2018年第9期17卷 1915-1931页

作者： Yanbo Huang chen zhong-xin YU Tao HUANG Xiang-zhi GU xing-fa Crop Production Systems Research Unit Agricultural Research Service United States Department of Agriculture Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences

Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen- level （FLFL） satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level （FLTL） remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.

关键词： big data remote sensing agricultural information precision agriculture

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Early-season crop type mapping using 30-m reference time series

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Journal of Integrative Agriculture 2020年第7期19卷 1897-1911页

作者： HAO Peng-yu TANG Hua-jun chen zhong-xin MENG Qing-yan KANG Yu-peng Key Laboratory of Agricultural Remote Sensing Minstry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agrcultural SciencesBejing 100081P.R China Shenzhen Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying Mapping and Geo-Information&Shenzhen Key Laboratory of Spatial Smart Sensing and ServicesShenzhen UniversityShenzhen 518060P.R.China Institute of Remote Sensing and Digital Earth Chinese Academy of SciencesBejjing 100101P.R.China

Early-season crop type mapping could provide important information for crop growth monitoring and yield prediction,but the lack of ground-surveyed training samples is the main challenge for crop type identification.Although reference time series based method(RBM)has been proposed to identify crop types without the use of ground-surveyed training samples,the methods are not suitable for study regions with small field size because the reference time series are mainly generated using data set with low spatial resolution.As the combination of Landsat data and Sentinel-2 data could increase the temporal resolution of 30-m image time series,we improved the RBM by generating reference normalized difference vegetation index(NDVI)/enhanced vegetation index(EVI)time series at 30-m resolution(30-m RBM)using both Landsat and Sentinel-2 data,then tried to estimate the potential of the reference NDVI/EVI time series for crop identification at early season.As a test case,we tried to use the 30-m RBM to identify major crop types in Hengshui,China at early season of 2018,the results showed that when the time series of the entire growing season were used for classification,overall classification accuracies of the 30-m RBM were higher than 95%,which were similar to the accuracies acquired using the ground-surveyed training samples.In addition,cotton,spring maize and summer maize distribution could be accurately generated 8,6 and 8 weeks before their harvest using the 30-m RBM;but winter wheat can only be accurately identified around the harvest time phase.Finally,NDVI outperformed EVI for crop type classification as NDVI had better separability for distinguishing crops at the green-up time phases.Comparing with the previous RBM,advantage of 30-m RBM is that the method could use the samples of the small fields to generate reference time series and process image time series with missing value for early-season crop casification;while,samples collected from multiple years should be futher used so that the reference time series could co

关键词： early season Landsat Sentinel-2 reference time series crop classification Hengshui

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Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation

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Journal of Integrative Agriculture 2017年第10期16卷 2283-2299页

作者： LI He JIANG Zhi-wei chen zhong-xin REN Jian-qiang LIU Bin Hasituya Key Laboratory of Agricultural Remote Sensing Ministry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences National Meteorological Information Center China Meteorological Administration

To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble Kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.

关键词： winter wheat yield estimates crop model data assimilation ensemble Kalman filter uncertainty leaf area index

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High resolution crop intensity mapping using harmonized Landsat-8 and Sentinel-2 data

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Journal of Integrative Agriculture 2019年第12期18卷 2883-2897页

作者： HAO Peng-yu TANG Hua-jun chen zhong-xin YU Le WU Ming-quan Key Laboratory of Agricultural Remote Sensing Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijing 100081P.R.China Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying Mapping and Geo Information/Shenzhen Key Laboratory of Spatial Smart Sensing and ServicesShenzhen UniversityShenzhen 518060P.R.China Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084P.R.China State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100101P.R.China

An increase in crop intensity could improve crop yield but may also lead to a series of environmental problems, such as depletion of ground water and increased soil salinity. The generation of high resolution(30 m) crop intensity maps is an important method used to monitor these changes, but this is challenging because the temporal resolution of the 30-m image time series is low due to the long satellite revisit period and high cloud coverage. The recently launched Sentinel-2 satellite could provide optical images at 10–60 m resolution and thus improve the temporal resolution of the 30-m image time series. This study used harmonized Landsat Sentinel-2(HLS) data to identify crop intensity. The sixth polynomial function was used to fit the normalized difference vegetation index(NDVI) and enhanced vegetation index(EVI) curves. Then, 15-day NDVI and EVI time series were then generated from the fitted curves and used to generate the extent of croplands. Lastly, the first derivative of the fitted VI curves were used to calculate the VI peaks;spurious peaks were removed using artificially defined thresholds and crop intensity was generated by counting the number of remaining VI peaks. The proposed methods were tested in four study regions, with results showing that 15-day time series generated from the fitted curves could accurately identify cropland extent. Overall accuracy of cropland identification was higher than 95%. In addition, both the harmonized NDVI and EVI time series identified crop intensity accurately as the overall accuracies, producer’s accuracies and user’s accuracies of non-cropland, single crop cycle and double crop cycle were higher than 85%. NDVI outperformed EVI as identifying double crop cycle fields more accurately.

关键词： crop inten sity time series sixth polyno mial fu nction harm on ized Lan dsat-8 and Sen tinel-2

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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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Characterizing Spatial Patterns of Phenology in Cropland of China Based on Remotely Sensed Data

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Agricultural Sciences in China 2010年第1期9卷 101-112页

作者： WU Wen-bin YANG Peng TANG Hua-jun ZHOU Qing-bo chen zhong-xin Ryosuke Shibasaki Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences/Key Laboratory of Resources Remote Sensing and Digital Agriculture Ministry of Agriculture Beijing 100081 P.R.China Center for Spatial Information Science University of Tokyo Tokyo 153-8505 Japan

This study used time-series of global inventory modeling and mapping studies （GIMMS） normalized difference vegetation index （NDVI） datasets at a spatial resolution of 8 km and 15-d interval to investigate the spatial patterns of cropland phenology in China. A smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination. Subsequent processing for identifying cropping systems and extracting phenological parameters, the starting date of growing season （SGS） and the ending date of growing season （EGS） was based on the smoothed NVDI time-series data. The results showed that the cropping systems in China became complex as moving from north to south of China. Under these cropping systems, the SGS and EGS for the first growing season varied largely over space, and those regions with multiple cropping systems generally presented a significant advanced SGS and EGS than the regions with single cropping patterns. On the contrary, the phenological events of the second growing season including both the SGS and EGS showed little difference between regions. The spatial patterns of cropping systems and phenology in Chinese cropland were highly related to the geophysical environmental factors. Several anthropogenic factors, such as crop variety, cultivation levels, irrigation, and fertilizers, could profoundly influence crop phenological status. How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies.

关键词： phenology NDVI time-series cropping systems the starting date of growing season （SGS） the ending date of growing season （EGS） spatial pattern

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结构功能主义视角下高等教育普职协同创新研究

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高等职业教育探索 2024年第2期23卷 48-56页

作者：康诚轩李保忠陈新忠武汉大学湖北武汉430072 华中科技大学湖北武汉430074

统筹高等教育普职协同创新,是发挥人才引领驱动作用的必经之路,也是强化现代化建设人才支撑的内在要求。目前两类高等教育协同创新虽呈现良好的发展态势,但基于结构功能主义视角,仍存在适应功能尚未完全实现、目标达成功能发挥相对滞后... 详细信息

统筹高等教育普职协同创新,是发挥人才引领驱动作用的必经之路,也是强化现代化建设人才支撑的内在要求。目前两类高等教育协同创新虽呈现良好的发展态势,但基于结构功能主义视角,仍存在适应功能尚未完全实现、目标达成功能发挥相对滞后、整合功能释放效能偏低、潜在模式维持功能触发负面效应的问题。为深入推进高等教育普职协同创新,需满足社会需求来增强适应、培养全面人才来夯实目标、建立协同机制来强化整合、营造文化环境来加强维持,不断加快二者协同创新的步伐。

关键词：结构功能主义普通高等教育职业高等教育普职协调协同创新高质量发展

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