Leaf Area Index Inversion of Summer Maize at Multiple Growth Stages based on BP Neural Network

Jun Liu; Qingyan Meng; Xiaosan Ge; Shunxi Liu; Xu Chen; Yunxiao Sun

doi:10.11873/j.issn.1004-0323.2020.1.0174

Remote Sensing Technology and Application 鈥衡�� 2020, Vol. 35 鈥衡�� Issue (1): 174-184.DOI: 10.11873/j.issn.1004-0323.2020.1.0174

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Leaf Area Index Inversion of Summer Maize at Multiple Growth Stages based on BP Neural Network

Jun Liu¹(),Qingyan Meng^2,³(),Xiaosan Ge¹,Shunxi Liu⁴,Xu Chen^2,³,Yunxiao Sun^2,³

^1. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
^2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
^3. Sanya Institute of Remote Sensing, Sanya 572029, China
^4. China Land Surveying & Planning Institute, Beijing 100035, China

Received:2018-08-23 Revised:2019-11-15 Online:2020-04-01 Published:2020-02-20
Contact: Qingyan Meng

鍩轰簬BP绁炵粡缃戠粶鐨勫鐜夌背澶氱敓鑲叉湡鍙堕潰绉寚鏁板弽婕旂爺绌�

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Abstract

Abstract:

Leaf Area Index (LAI) is an important vegetation structure parameter in biogeochemical cycling. In view of the lack of LAI inversion in the multiple growth period of summer maize based on GF-1 WFV satellite images in China, this study constructs a BP neural network model (BP1 model and BP2 model) based on different hidden layers, and compares and analyzes the accuracy of the inversion between the BP1 model, BP2 model and 6 statistical models (NDVI銆丷VI銆丏VI銆丒VI銆丼AVI銆丄RVI). Based on the measured data, BP1 model and BP2 model are used to map the LAI dynamic changes of summer maize. The results show that LAI has good correlation with 6 common statistical models, and the fitting degree of the NDVI exponential equation regression model is the best. The overallR ² of BP neural network model is slightly smaller than the statistical model, while RMSE is less than the statistical model, and the errors with the measured value is smaller than the statistical model. So both the statistical model and the BP neural network model have advantages and disadvantages. The BP2 model is superior to the BP1 model inR ² and RMSE, and can obtain more accurate inversion values, and the overall prediction accuracy of BP2 is higher. Based on the BP neural network simulation of summer maize growth period inversion, the LAI value presents a slow increase to the gradual decrease of S type change process, which is basically in line with the crop growth rules. The study combines with the BP neural network model established by different hidden layers to provide a method for the application of GF-1 satellite in the application of crop leaf area index multiple growth period inversion.

Key words: Summer maize, Leaf Area Index(LAI), BP neural network model, Statistical model, Multiple growth period

鎽樿锛�

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鍏抽敭璇�: 澶忕帀绫�, 鍙堕潰绉寚鏁�, BP绁炵粡缃戠粶妯″瀷, 缁熻妯″瀷, 澶氱敓鑲叉湡

CLC Number:

TP79

Jun Liu,Qingyan Meng,Xiaosan Ge,Shunxi Liu,Xu Chen,Yunxiao Sun. Leaf Area Index Inversion of Summer Maize at Multiple Growth Stages based on BP Neural Network[J]. Remote Sensing Technology and Application, 2020, 35(1): 174-184.

鍒樹繆,瀛熷簡宀�,钁涘皬涓�,鍒橀『鍠�,闄堟棴,瀛欎簯鏅�. 鍩轰簬BP绁炵粡缃戠粶鐨勫鐜夌背澶氱敓鑲叉湡鍙堕潰绉寚鏁板弽婕旂爺绌禰J]. 閬ユ劅鎶�鏈笌搴旂敤, 2020, 35(1): 174-184.

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Leaf Area Index Inversion of Summer Maize at Multiple Growth Stages based on BP Neural Network

鍩轰簬BP绁炵粡缃戠粶鐨勫鐜夌背澶氱敓鑲叉湡鍙堕潰绉寚鏁板弽婕旂爺绌�

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