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The Monitoring Analysis for the Drought in China by Using an Improved MPI Method

Journal of Integrative Agriculture 2012年第6期11卷 1048-1057,1058页

作者： MAO Ke-biao MA Ying XIA Lang tang hua-jun HAN Li-juan 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 Center for Land Resource Research in Northwest China Shaanxi Normal University Xi'an 710062 P.R.China A-World Consulting Hong Kong Logistics Association Hong Kong PR.China State Key Laboratory of Remote Sensing Science Institute of Remote Sensing Applications of Chinese Academy of Sciences/Beijing Normal University Beijing 100101 P.R.China National Meteorological Center Beijing 100081 P.R. China

MPI （microwave polarization index） method can use different frequencies at vertical polarization to retrieve soil moisture from TMI （tropical microwave imager） data, which is mainly suitable for bare soil. This paper makes an improvement for MPI method which makes it suitable for surface covered by vegetation. The MPI by using single frequency at different polarizations is used to discriminate the bare soil and vegetation which overcomes the difficulty in previous algorithms by using optical remote sensing data, and then the revision is made according to the different land surface types. The validation by using ground measurement data indicates that revision for different land surface types can improve the retrieval accuracy. The average error is about 24.5% by using the ground truth data obtained from ground observation stations, and the retrieval error is about 13.7% after making a revision by using ground measurement data from local observation stations for different surface types. The improved MPI method and precipitation are used to analyze the drought in Southwest China, and the analysis indicates the soil moisture retrieved by improved MPI method can be used to monitor the drought.

关键词： drought soil moisture climate change microwave remote sensing

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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 *** sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop *** 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 ***,we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing ***,we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI ***,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 *** 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 ***,we highlight the technical difficulties that may hinder crop LAI estimation,such as the ＂curse of dimensionality＂ and the ill-posed ***,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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Modeling Soil Organic Carbon Storage and Its Dynamics in Croplands of China

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Agricultural Sciences in China 2010年第5期9卷 704-712页

作者： tang hua-jun QIU Jian-jun WANG Li-gang LI Hu LI Chang-sheng Eric van Ranst Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing 100081 P.R.China Institute for the Study of Earth Oceans and Space University of New Hampshire Durham NH 03824 USA Department of Geology and Soil Science (WE13)/Laboratory of Soil Science Ghent University Gent B-9000 Belgium

Soil organic carbon （SOC） is one of the centre issues related to not only soil fertility but also environmental safety. Assessing SOC dynamics in croplands has been a challenge in China for long due to the lack of appropriate methodologies and data sources. As an alternative approach for studying SOC dynamics, process-based models are adopted to meet the needs. In this paper, a process-based model, DeNitrification-DeComposition （DNDC）, was applied to quantify the SOC storage and the spatial distribution in croplands of China in 2003, with the support of a newly compiled county-level soil/ climate/land use database. The simulated results showed that the total SOC storage in the top layer （0-30 cm） of the 1.18 × 10^8 ha croplands of China is 4.7-5.2 Pg C in 2003 with an average value of 4.95 Pg C. The SOC storage in the northeastern provinces （1.3 Pg C） accounts for about 1/4 of the whole national totals due to their dominantly fertile soils with high organic matter content. SOC density ranges from 3.9 to 4.4 kg C m 2, with an average of 4.2 kg C m^-2, a level is much lower than the world average level. The model results also indicated that high rates of SOC losses occurred in the croplands with the most common cropping patterns in China as like single soybean 〉 maize 〉 paddy 〉 cotton 〉 winter wheat and corn rotation. The results reported in this paper showed that there was still a great potential for improving SOC status in most croplands of China by adopting proper farming practices and land-use pattern. Therefore, long-term policy to protect SOC is urgently needed.

关键词： China cropland DNDC model soil organic carbon

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Modeling the Impacts of Soil Organic Carbon Content of Croplands on Crop Yields in China

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Agricultural Sciences in China 2009年第4期8卷 464-471页

作者： QIU Jian-jun WANG Li-gang LI HU tang hua-jun LI Chang-sheng Eric Van Ranst Key Laboratory of Resources Remote Sensing and Digital Agriculture Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences (CAAS) Beijing 100081 P.R. China Institute for the Study of Earth Ocean and Space University of New Hampshire Durham NH 03824 USA Department of Geology and Soil Science (WEI3) Laboratory of Soil Science Ghent University Krijgslaan 281 (S8) B-9000 Gent Belgium

This study quantified the impacts of soil organic carbon （SOC） content on the grain yield of crops using a biogeochemical model （DNDC, denitrification-decomposition）. Data on climate, soil properties, and farming management regimes of cropping systems were collected from six typical agricultural zones （northeast, north, northwest, mid-south, east and southwest regions of China, respectively） and integrated into a GIS database to support the model runs. According to the model, if the initial SOC content in the cropland was increased by 1 g C kg^-1, the crop yield may be increased by 176 kg ha^-1 for maize in the northeast region, 454 kg ha^-1 for a maize-wheat rotation in the north region, 328 kg ha^-1 for maize in the northwest region, 185 kg ha^-1 for single-rice in the mid-south region, 266 kg ha^-1 for double-rice in east region, and 229 kg ha^-1 for rice and wheat rotation in southwest region. There is a great potential for enhancing the crop yield by improving the SOC content in each region of China.

关键词： soil organic carbon grain yield carbon sequestration DNDC model

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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 *** 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 *** 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 *** 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 *** 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 ***,NDVI outperformed EVI for crop type classification as NDVI had better separability for distinguishing crops at the green-up time *** 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

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

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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 resolutio

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

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Contribution of Drought to Potential Crop Yield Reduction in a Wheat-Maize Rotation Region in the North China Plain

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Journal of Integrative Agriculture 2014年第7期13卷 1509-1519页

作者： HU Ya-nan LIU Ying-jie tang hua-jun XU Yin-long PAN Jie Institute of Environment and Sustainable Development in Agriculture 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 Public Meteorological Service Center Chinese Meteorological Administration

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.

关键词： drought yield DSSAT A1B scenario climate change

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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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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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The breakfast imperative： The changing context of global food security

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Journal of Integrative Agriculture 2016年第6期15卷 1179-1185页

作者： YE Li-ming Jean-Paul Malingreau tang hua-jun Eric Van Ranst Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural SciencesBeijing 100081P.R.China CAAS-UGent Joint Laboratory of Global Change and Food Security Beijing 100081P.R.China Department of Geology and Soil Science(WE13) Ghent University9000 GentBelgium Earth and Life Institute Universite Catholique de Louvain1348 Louvain-la-NeuveBelgium

The debate on global food security has regained vigor since the food crisis of 2008, when a sudden spike in the prices of staple food commodities dramatically demonstrated that securing the supply and accessibility of food for a world of nine billion people in 2050 cannot be taken for grant- ed （Godfray etal. 2010; Swinnen and Squicciarini 2012;

关键词： The breakfast imperative The changing context of global food security

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