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A remote sensing model FOR DETERMINING CHLOROPHYLL CONTENT AND ITS DISTRIBUTION USING LANDSAT IMAGES

Acta Botanica Sinica 1997年第9期39卷 821-825页

作者：张仁华孙晓敏朱治林中国科学院地理研究所北京100101

The uncertainty in the estimatation of chlorophyll content with the use of normalized difference vegetation index (NDVI) has been described. To determine the chlorophyll content, model 1 for LANDSAT and model 2 for NOAA AVHRR wavebands were presented and have been verified by field experiments. model 1 was also validated by the distribution of chlorophyll content using LANDSAT images around the Yucheng remote sensing experimental station. Using these models to estimate the chlorophyll content in the vegetation community is benefitiated by the increased precision and decreased uncertainty.

关键词： Chlorophyll remote sensing model Vegetation index

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Improved simulation of winter wheat yield in North China Plain by using PRYM-Wheat integrated dry matter distribution coefficient

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Journal of Integrative Agriculture 2024年第4期23卷 1381-1392页

作者： Xuan Li Shaowen Wang Yifan Chen Danwen Zhang Shanshan Yang Jingwen Wang Jiahua Zhang Yun Bai Sha Zhang Research Center for remote sensing and Digital Earth College of Computer Science and TechnologyQingdao UniversityQingdao266071China Center for Geospatial Information Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Geographical Sciences Hebei Normal UniversityShijiazhuang 050024China

The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in ***,most remote sensing process models use the“biomass×harvest index(HI)”method to simulate regional-scale winter wheat ***,spatiotemporal differences in HI contribute to inaccuracies in yield simulation at the regional ***-series dry matter partition coefficients(Fr)can dynamically reflect the dry matter partition of winter *** this study,Fr equations were fitted for each organ of winter wheat using site-scale *** equations were then coupled into a process-based and remote sensingdriven crop yield model for wheat(PRYM-Wheat)to improve the regional simulation of winter wheat yield over the North China Plain(NCP).The improved PRYM-Wheat model integrated with the fitted Fr equations(PRYM-Wheat-Fr)was validated using data obtained from provincial yearbooks.A 3-year(2000-2002)averaged validation showed that PRYM-Wheat-Fr had a higher coefficient of determination(R^(2)=0.55)and lower root mean square error(RMSE=0.94 t ha^(-1))than PRYM-Wheat with a stable HI(abbreviated as PRYM-Wheat-HI),which had R^(2) and RMSE values of 0.30 and 1.62 t ha^(-1),*** PRYM-Wheat-Fr model also performed better than PRYM-Wheat-HI for simulating yield in verification years(2013-2015).In conclusion,the PRYM-Wheat-Fr model exhibited a better accuracy than the original PRYM-Wheat model,making it a useful tool for the simulation of regional winter wheat yield.

关键词： dry matter partition remote sensing model winter wheat yield North China Plain

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维普期刊数据库

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A global terrestrial ecosystem respiration dataset(2001-2010)estimated with MODIS land surface temperature and vegetation indices

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Big Earth Data 2020年第2期4卷 142-152页

作者： Jinlong Ai Shuyuan Xiao Hui Feng Huan Wang Gensuo Jia Yonghong Hu Department of Biology and Information Engineering Yiyang Vocational&Technical CollegeYiyangChina Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina

This paper describes how a validated semi-empirical,but physiologically based,remote sensing model-Ensemble_all-was upscaled using MODIS land surface temperature data(MOD11C2),enhanced vegetation indices(MOD13C1)and land-cover data(MCD12C1)to produce a global terrestrial ecosystem respiration data set(Reco)for January 2001-December *** temporal resolution of this data set is 1 month,the spatial resolution is 0.05°,and the range is from 55°S to 65°N and 180°W to 180°E(crop and natural vegetation mosaic is not included).After crossvalidating our data set using in-situ observations as well as Reco outputs from an empirical variable_Q10 model,a LPJ_S1 process model and a machine learning method model,we found that our data set performed well in detecting both temporal and spatial patterns in Reco’s simulation in most ecosystems across the *** data set can be found at http://***/10.11922/sciencedb.934.

关键词： Terrestrial ecosystem respiration MODIS data product up-scaling remote sensing model

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Study on the Correlation Between Chlorophyll Maximum and remote sensing Data

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Journal of Ocean University of China 2006年第3期5卷 213-218页

作者： XIU Peng LIU Yuguang College of Physical and Environmental Oceanography Physical Oceanography LaboratoryOcean University of China Qingdao 266003 P. R. China

Based on the in situ optical measurements in the Bohai Sea of China, which belongs to a typical case-2 water area, we studied the characteristics of DCM (deep chlorophyll maximum) such as its spatial distribution, vertical profile, etc. We found that when the depth of the chlorophyll maximum is comparatively small, even in turbid coastal water regions, there is always a good correlation between the concentrations of chlorophyll maximum and the satellite-received signals in blue-green spectral bands; the correlation is even better than that between the surface chlorophyll concentrations and the satellite-received signals. The strong correlation existing even in turbid coastal water regions indicates that an ocean color model to retrieve the concentration of DCM can be constructed for coastal waters if a comprehensive knowledge of the vertical distribution of chlorophyll concentration in the Bohai Sea of China is available.

关键词： ocean color remote sensing model deep chlorophyll maximum case-2 water

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