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Seasonal dynamic pattern analysis on global FPAR derived from AVHRR GIMMS NDVI

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International Journal of Digital Earth 2012年第5期5卷 439-455页

作者： dailiang peng Bing Zhang Liangyun Liu Dongmei Chen Hongliang Fang Yong Hu Center for Earth Observation and Digital Earth Chinese Academy of SciencesBeijingChina Key Laboratory of Digital Earth Chinese Academy of SciencesBeijingChina Department of Geography Queen’s UniversityKingstonONCanada LREIS Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina

The purpose of this paper is to develop Advanced Very High Resolution Radiometer(AVHRR)Global Inventory Modelling and Mapping Studies(GIMMS)Normalised Difference Vegetation Index(NDVI;AVHRR GIMMS NDVI for short)based fraction of absorbed photosynthetically active radiation(FPAR)from 1982 to 2006 and focus on their seasonal and spatial patterns *** available relationship between FPAR and NDVI was used to calculate FPAR values from 1982 to 2006 and validated by Moderate-resolution Imaging Spectroradiometer(MODIS)FPAR ***,the seasonal dynamic patterns were analysed,as well as the driving force of climatic *** showed that there was an agreement between FPAR values from this study and those of the MODIS product in seasonal dynamic,and the spatial patterns of FPARvary with vegetation type distribution and seasonal *** time series of average FPAR revealed a strong seasonal variation,regular periodic variations from January 1982 to December 2006,and opposite patterns between the Northern and Southern *** vegetation FPARvalues were close to 0.7.A clear single-peak curve was observed between 308N and 808N?an area covered by deciduous *** the Southern Hemisphere,the time series fluctuations of FPAR averaged by 0.78 latitude zones were not clear compared to those in the Northern Hemisphere.A significant positive correlation(PB0.01)was observed between the seasonal variation of temperature and precipitation and FPAR over most other global meteorological sites.

关键词： global FPAR seasonal pattern AVHRR GIMMS NDVI

Monitoring nitrogen concentration of oilseed rape from hyperspectral data using radial basis function

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International Journal of Digital Earth 2013年第6期6卷 550-562页

作者： Fumin Wang Jingfeng Huang Yuan Wang Zhuanyu Liu dailiang peng Feifeng Cao Institute of Hydrology and Water Resources Zhejiang UniversityHangzhouChina Institute of Agricultural Remote Sensing&Information Application Zhejiang UniversityHangzhouChina Institute of Remote Sensing and Earth Sciences Hangzhou Normal UniversityHangzhouChina Center for Earth Observation and Digital Earth Chinese Academy of SciencesBeijingChina Bureau of Water Resources and Hydropower Engineering of Zhejiang Provinces HangzhouChina

Remote sensing technology is the important tool of digital earth,it can facilitate nutrient management in sustainable cropping *** the study,two types of radial basis function(RBF)neural network approaches,the standard radial basis function(SRBF)neural networks and the modified type of RBF,generalized regression neural networks(GRNN),were investigated in estimating the nitrogen concentrations of oilseed rape canopy using vegetation indices(VIs)and hyperspectral *** analyses were performed to the spectral variables and the *** Root Mean Square Error(RMSE)and determination coefficients(R2)were used to assess their predictability of nitrogen *** all spectral variables(VIs and hyperspectral reflectance),the GRNN method produced more accurate estimates of nitrogen concentrations than did the SRBF method at all ranges of nitrogen concentrations,and the better agreements between the measured and the predicted nitrogen concentration were obtained with the GRNN *** indicated that the GRNN method is prior to the SRBF method in estimation of nitrogen *** the VIs,the Modified Chlorophyll Absorption in Reflectance Index(MCARI),MCARI1510,and Transformed Chlorophyll Absorption in Reflectance Index are better than the others in estimating oilseed rape canopy nitrogen *** to the results from VIs,the hyperspectral reflectance data also gave an acceptable *** study showed that nitrogen concentrations of oilseed rape canopy could be monitored using remotely sensed data and the RBF method,especially the GRNN method,is a useful explorative tool for oilseed rape nitrogen concentration monitoring when applied on hyperspectral data.

关键词： vegetation indices hyperspectral data nitrogen concentration artifi-cial neural network radial basis function agriculture remote sensing

Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm

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Science China Earth Sciences 2020年第9期63卷 1390-1407页

作者： Yidi XU Le YU dailiang peng Jiyao ZHAO Yuqi CHENG Xiaoxuan LIU Wei LI Ran MENG Xinliang XU peng GONG Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing 100084China Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094China College of Natural Resources and Environment Huazhong Agricultural UniversityWuhan 430070China State Key Laboratory of Resources and Environmental Information Systems Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China

Annual land use land cover(LULC)change information at medium spatial resolution(*** 30 m)is required in numerous subjects,such as biophysical modelling,land management and global change *** LULC information,however,is usually not available at continental or national scale due to reasons such as insufficient remote sensing data coverage or lack of computational *** we integrate high temporal resolution and coarse spatial resolution satellite images(i.e.,Moderate Resolution Imaging Spectroradiometer(MODIS)and Global Inventory Modelling and Mapping Studies(GIMMS)normalized difference vegetation index(NDVI))with high spatial resolution datasets(China’s Land-Use/cover Datasets(CLUDs)derived from 30-meter Landsat TM/ETM+/OLI)to generate reliable annual nominal 30 m LULC maps for the whole of China between 1980 and *** also test the performance of a statistical based change detection algorithm(Breaks for Additive Seasonal and Trend),originally designed for tracking forest change,in classifying all-type LULC *** a result,a nominal 30 m annual land use/land cover datasets(CLUD-A)from 1980 to 2015 was developed for the whole *** mapping results were assessed with a change sample dataset,a regional annual validation sample set and a three-year China sample *** the detected change years,75.61%matched the exact time of conversion within±1 *** mapping results provided a detail process of urbanization,deforestation,afforestation,water and cropland dynamics over the past 36 *** consistent characterization of land change dynamics for China can be further used in scientific research and to support land management for policy-makers.

关键词： Land use land cover(LULC) Breaks for Additive Seasonal and Trend(BFAST) Change detection Annual China

MODIS observations of water color of the largest 10 lakes in China between 2000 and 2012

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International Journal of Digital Earth 2016年第8期9卷 788-805页

作者： Junsheng Li Shenglei Wang Yanhong Wu Bing Zhang Xiaoling Chen Fangfang Zhang Qian Shen dailiang peng Liqiao Tian Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingPeople’s Republic of China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanPeople’s Republic of China

Forel-Ule(FU)index of water color is an important parameter in traditional water quality *** retrieved the FU index of the largest 10 lakes in China during 2000-2012 from MODerate-resolution Imaging Spectroradiometer surface reflectance product(MOD09)*** FU index is an optical parameter,it can be derived from optical remote sensing data by direct formulas,which is invariant with region and *** on validation by in situ measured reflectance data,the FU index products are reliable,with average relative error of 7.7%.FU index can be used to roughly assess water clarity:the clearer a water body is,and the bluer it is in color,the smaller its FU index *** index can also be used to roughly classify trophic state into three classes:oligotrophic,mesotrophic,and *** analyzed the spatial,interannual,and seasonal variations of the FU index and its implications for water clarity and trophic state,and the findings are mostly consistent with the results from related *** in all,it might be a feasible way to roughly assess inland water quality by FU index in large region and over long time period.

关键词： MODIS water color Forel-Ule inland water remote sensing

An Operational Assessment Framework for Near Real-time Cropland Dynamics:Toward Sustainable Cropland Use in Mid-Spine Belt of Beautiful China

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Journal of Remote Sensing

Journal of Remote Sensing 2023年第1期3卷 247-263页

作者： Zhenrong Du Le Yu Xin Chen Xiyu Li dailiang peng Shijun Zheng pengyu Hao Jianyu Yang Huadong Guo peng Gong Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing 100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing 100084China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China University of Chinese Academy of Sciences Beijing 100049China Food and Agriculture Organization of the United Nations Viale delle Terme di Caracalla00153 RomeItaly College of Land Science and Technology China Agricultural UniversityBeijing 100193China Department of Geography Department of Earth Sciences and Institute for Climate and Carbon NeutralityUniversity of Hong KongHong Kong 999077China

Cropland monitoring is a crucial component for a broad user community from Land Use and Land Cover Change study to food security policy *** with the rich natural ecological environment and variable agricultural production conditions of Mid-Spine Belt of Beautiful China(MSBBC),this study developed a novel operational assessment framework that combined the near real-time land cover mapping platform(i.e.,FROM-GLC Plus),the FAO Agricultural Stress Index System,and the land degradation monitoring method suggested by United Nations Convention to Combat Desertification for the timely monitoring of cropland extent change,cropland conditions,and cropland *** integrated monitoring system,this framework can provide convenient access to high-spatiotemporalresolution cropland maps(30 m,dekadal)and instant(near real time)cropland *** to the monitoring results,we found that the abnormally high temperatures of summer 2022 adversely affected crop health in the southwest of ***,our results suggested that China’s ecological restoration projects made remarkable achievement in *** productivity of more than 70% of cropland in MSBBC has improved,and only~6% cropland(~3.69×10^(4) km^(2))has degraded since 2000,mainly distributed in cropland with steep slope,insufficient precipitation,and intensive ***-specific measures,such as conservation tillage,improved tillage systems,and cropland ecological projects,should be adopted for sustainable cropland use and further increase in land carrying capacity of MSBBC to achieve balanced east-west development in China.

关键词： Sustainable southwest policy

Identification of soybean in Argentina using Sentinel-2 composite images

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International Journal of Agricultural and Biological Engineering 2024年第5期17卷 266-274页

作者： Linsheng Huang Yue Chen Yuhao Pan Zihang Lou Shijun Zheng Xiaoyang Zhang Le Yu Shengwei Liu dailiang peng National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China Research Area of Ecology and Biodiversity School of Biological SciencesThe University of Hong KongPokfulamHong KongChina College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China Geospatial Sciences Center of Excellence Department of Geography&Geospatial SciencesSouth Dakota State UniversityBrookingsSD 57007USA Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing 100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing 100084China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000HenanChina

Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial distribution is critical for ensuring global food security. Sentinel-2 multispectral data and machine learning classification models are used to investigate the potential of soybean identification in the early stage of the growing season in Argentina, with the help of Google Earth Engine (GEE). The earliest time window and optimal feature set for soybean identification are explored. Results are as follows: 1) the random forest (RF) classification model demonstrated the highest level of classification accuracy compared to the backpropagation neural network (BPNN), support vector machine (SVM), and naive Bayes (NB) models;2) Soybean can be accurately identified as early as the end of February (filling stage), which is approximately one month before harvest;3) The optimal feature-subset can reduce the amount of input data by 80% while maintaining high classification accuracy. The overall accuracy (OA) of the RF classification model is 85.87%, and the relative error between the estimated soybean planting area and the agricultural statistics is 3.45%. This study provided a high-precision method for early-season identification of soybeans over large scales. The results can provide a data support for early futures trading and agricultural insurance, as well as a reference for policy-making to ensure global soybean food security.

关键词： soybean machine learning time window feature selection Sentinel-2 Google Earth Engine

OUR SHARED GREEN HOME--China is contributing to a greener Earth

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China Report ASEAN 2019年第7期4卷 52-55页

作者： peng dailiang Zhang Helin Zhang Bing the Institute of Remote Sensing and Digital Earth of the Chinese Academy of Sciences

This report references data compiled by NASA’s Aqua and Terra Earth observation satellites. From 2000 to 2017, global areas covered by greenery increased by 5 percent, of which 25 percent lie in China. In fact, China... 详细信息

关键词： report Earth happen

基于抽样调查地块实测数据的省级水稻单产遥感估算

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农业工程学报 2011年第9期27卷 106-114页

作者：彭代亮周炼清黄敬峰周斌王福民浙江大学农业遥感与信息技术应用研究所杭州310058 中国科学院对地观测与数字地球科学中心北京100094 杭州师范大学遥感与地球科学研究院杭州310036 浙江大学建筑工程学院杭州310058

利用统计局抽样调查地块实割实测标准单位面积产量数据及空间位置信息,在研究水稻种植区MOD09GA、MYD09GA EVI(enhanced vegetation index)数据结合使用可行性分析的基础上,进行湖南省省级水稻单产遥感估算。结果表明:在水稻种植区,水... 详细信息

利用统计局抽样调查地块实割实测标准单位面积产量数据及空间位置信息,在研究水稻种植区MOD09GA、MYD09GA EVI(enhanced vegetation index)数据结合使用可行性分析的基础上,进行湖南省省级水稻单产遥感估算。结果表明:在水稻种植区,水稻生长期间有超过50%、85%、95%的MOD09GA与MYD09GA EVI偏差绝对值分别小于0.03、0.08、0.1。水稻遥感估产模型以二次非线性模型或回归模型精度较高,且时相集中在水稻生长的孕穗期到抽穗期。在省级水平上,与实测值相比,基于统计局统计抽样调查地块实割实测标准单位面积产量数据的水稻单产遥感拟合结果相对误差小于2%,预测结果的相对误差接近5%,且实测值与模拟值集中分布在1∶1线附近,说明预测结果与统计值在空间分布上具有较好的一致性。

关键词：遥感估产统计测量水稻抽样调查地块

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基于能量平衡原理的FPAR遥感反演研究

遥感技术与应用 2014年第6期29卷 976-983页

作者：张健刘良云焦全军彭代亮中国科学院遥感与数字地球研究所数字地球重点实验室北京100094 西安科技大学测绘工程与技术学院陕西西安710054

光合有效辐射吸收系数(FPAR)是描述植被结构以及冠层—大气物质与能量交换过程的基本生理变量。从能量守恒的原理出发,结合非线性混合像元模型,分析了太阳入射能量中的植被冠层反射、土壤吸收分量的光谱反演方法,建立了简化的FPAR遥感... 详细信息

光合有效辐射吸收系数(FPAR)是描述植被结构以及冠层—大气物质与能量交换过程的基本生理变量。从能量守恒的原理出发,结合非线性混合像元模型,分析了太阳入射能量中的植被冠层反射、土壤吸收分量的光谱反演方法,建立了简化的FPAR遥感反演模型(FPEB)。分别应用2011和2013年西藏自冶区那曲实验数据、2011年西藏自冶区当雄实验数据和2013年内蒙古自冶区海拉尔的实验数据,对建立的FPAR遥感反演模型进行了验证,并将FPEB模型反演结果与传统的植被指数统计模型反演结果进行了对比分析,结果表明:FPEB模型的FPAR反演精度优于NDVI统计模型,且与其他基于能量平衡原理提出的反演FPAR的模型相比具有输入参数少,模型简单的优势,在空间区域和时间上具有很好的普适性。

关键词： FPAR PAR 能量平衡植被指数非线性混合像元模型