Deriving Maximum Light Use Efficiency From Crop Growth Model and Satellite Data to Improve Crop Biomass Estimation | |
Dong, Taifeng1; Liu, Jiangui1; Qian, Budong1; Jing, Qi1; Croft, Holly2; Chen, Jingming2; Wang, Jinfei3; Huffman, Ted1; Shang, Jiali1; Chen, Pengfei1,4 | |
刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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2017 | |
卷号 | 10期号:1页码:104-117 |
关键词 | Crop biomass Landsat-8 operational land imager (OLI) light use efficiency (LUE) maize winter wheat |
ISSN号 | 1939-1404 |
DOI | 10.1109/JSTARS.2016.2605303 |
通讯作者 | Huffman, Ted(Ted.Huffman@agr.gc.ca) ; Shang, Jiali(Jiali.Shang@agr.gc.ca) |
英文摘要 | Maximum light use efficiency (LUEmax) is an important parameter in biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable crop LUEmax from satellite remote sensing data. LUEmax at the plot level was retrieved first by assimilating field measured green leaf area index and biomass into a crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. LUEmax was then mapped using the best regression model from a VI. The influence factors on LUEmax variability were also assessed. Contrary to a fixed LUEmax, our results suggest that LUEmax is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level LUEmax and VIs, particularly the two-band enhanced vegetation index for winter wheat (Triticum aestivum) and the green chlorophyll index for maize (Zea mays) at the milk stage, provided a potential to derive LUEmax from remote sensing observations. To evaluate the quality of LUEmax derived from remote sensing data, biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant LUEmax and the derived variable LUEmax. Significant improvements in biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable LUEmax. This study offers a new way to derive LUEmax for a specific PEM and to improve the accuracy of biomass estimation using remote sensing. |
资助项目 | Agriculture and Agri-Food Canada ; Canadian Space Agency |
WOS关键词 | GROSS PRIMARY PRODUCTION ; LEAF-AREA INDEX ; PHOTOSYNTHETICALLY ACTIVE RADIATION ; CORN-YIELD ESTIMATION ; REMOTE-SENSING DATA ; CERES-MAIZE MODEL ; VEGETATION INDEXES ; ECOSYSTEM PRODUCTION ; PRIMARY PRODUCTIVITY ; SOYBEAN CROPLANDS |
WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000391719900010 |
资助机构 | Agriculture and Agri-Food Canada ; Canadian Space Agency |
内容类型 | 期刊论文 |
源URL | [http://ir.igsnrr.ac.cn/handle/311030/65086] ![]() |
专题 | 中国科学院地理科学与资源研究所 |
通讯作者 | Huffman, Ted; Shang, Jiali |
作者单位 | 1.Agr & Agri Food Canada, Ottawa Res & Dev Ctr, Ottawa, ON K1A 0C6, Canada 2.Univ Toronto, Dept Geog, Toronto, ON M5S 3G3, Canada 3.Univ Western Ontario, Dept Geog, London, ON N6A 5C2, Canada 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
推荐引用方式 GB/T 7714 | Dong, Taifeng,Liu, Jiangui,Qian, Budong,et al. Deriving Maximum Light Use Efficiency From Crop Growth Model and Satellite Data to Improve Crop Biomass Estimation[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2017,10(1):104-117. |
APA | Dong, Taifeng.,Liu, Jiangui.,Qian, Budong.,Jing, Qi.,Croft, Holly.,...&Chen, Pengfei.(2017).Deriving Maximum Light Use Efficiency From Crop Growth Model and Satellite Data to Improve Crop Biomass Estimation.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,10(1),104-117. |
MLA | Dong, Taifeng,et al."Deriving Maximum Light Use Efficiency From Crop Growth Model and Satellite Data to Improve Crop Biomass Estimation".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 10.1(2017):104-117. |
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