数字农科院2.0

Exploring the Optimized Leaf Area Index Retrieval Strategy Based on the Look-up Table Approach for Decametric-Resolution Images

文献类型： 外文期刊

作者： Wang, Qi;Zhang, Zhewei;Wu, Tongzhou;Jin, Wenjie;Meng, Ke;Song, Qian;Wang, Cong;Yin, Gaofei;Xu, Baodong

作者机构：

关键词： leaf area index (LAI);Landsat-8;look-up table (LUT)-based inversion;look-up table (LUT)-based inversion;Landsat-8;optimization strategy;optimization strategy;PROSAIL;PROSAIL;optimization strategy;PROSAIL

期刊名称： IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

ISSN： 0196-2892

年卷期： 2024 年 62 卷

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

摘要： Leaf area index (LAI) is a pivotal biophysical parameter for characterizing canopy structure and monitoring vegetation growth. Although the look-up table (LUT) method has been widely employed for LAI retrieval, the optimization of key retrieval processes remains to be explored. Here, we proposed a generic optimization strategy for LUT-based inversion based on Landsat -8 imagery and global ground LAI measurements. Specifically, based on the LUT generated by the PROSAIL model, LAI inversion was optimized by introducing several functions, including band selection, artificial noise addition, cost function (CF) substitution, and multiple solutions. Furthermore, the optimized LUT-based inversion method was compared to the Simplified Level 2 Product Prototype Processor (SL2P) method and the ground-measurement-derived (GMD) regression method to comprehensively evaluate its performance over various vegetation types. Results showed that the combination of Red, near-infrared (NIR), and shortwave infrared-1 (SWIR1) bands was well suited to capture LAI dynamics. In terms of accuracy and efficiency, the best performance was achieved by the optimal band combination and retrieval parameter settings (i.e., root-mean-square error (RMSE) as CF, noise level of 20%, and multiple solutions of 5%), with the RMSE and R-2 of 0.817 and 0.740, respectively. In addition, the optimized LUT-based inversion was superior to SL2P method in accuracy and to GMD regression method in efficiency. Overall, the optimized LUT-based inversion strategy can be applied for estimating decametric-resolution LAI with high accuracy over different regions and observation dates at a global scale, exhibiting high adaptability and generalization capability, especially for crops, and requiring no ground LAI measurements.

分类号：

  • 相关文献

[1]Crop specific inversion of PROSAIL to retrieve green area index (GAI) from several decametric satellites using a Bayesian framework. Jingwen Wang,Raul Lopez-Lozano,Marie Weiss,Samuel Buis,Wenjuan Li,Shouyang Liu,Frédéric Baret,Jiahua Zhang. 2022

[2]Precision-engineered mRNA vaccines: antigen design, structural optimization, and programmable delivery for emerging pathogens. Yanyu Zhang,Junchi Li,Zihan Wang,Yu Kuang,Songli Li,Xiaojia Wang. 2025

[3]Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data. Duan, Si-Bo,Wu, Hua,Tang, Bo-Hui,Duan, Si-Bo,Zhao, Enyu,Duan, Si-Bo,Li, Zhao-Liang,Li, Zhao-Liang,Ma, Lingling,Zhao, Enyu,Li, Chuanrong. 2014

[4]Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method. Liang, Liang,Zhao, Shuhe,Liang, Liang,Lin, Hui,Zhang, Lianpeng,Wang, Lijuan,Liang, Liang,Di, Liping,Zhang, Chao,Deng, Meixia,Qin, Zhihao,Zhao, Shuhe,Zhang, Chao,Liu, Zhixiao.

[5]Comparison of fractional vegetation cover estimations using dimidiate pixel models and look- up table inversions of the PROSAIL model from Landsat 8 OLI data. Ding, Yanling,Zhang, Hongyan,Li, Zhenwang,Xin, Xiaoping,Zheng, Xingming,Zhao, Kai,Zheng, Xingming,Zhao, Kai. 2016

[6]Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Liang, Liang,Zhang, Lianpeng,Lin, Hui,Liang, Liang,Zhao, Shuhe,Liang, Liang,Di, Liping,Deng, Meixia,Qin, Zhihao.

[7]Combining UAV Multispectral Imaging and PROSAIL Model to Estimate LAI of Potato at Plot Scale. Shuang Li,Yongxin Lin,Ping Zhu,Liping Jin,Chunsong Bian,Jiangang Liu. 2024

[8]基于多时相OLI数据的宁夏大尺度水稻面积遥感估算. 刘佳,王利民,姚保民,杨福刚,杨玲波,王小龙,曹怀堂. 2017

[9]随机森林方法在玉米-大豆精细识别中的应用. 王利民,刘佳,杨玲波,杨福刚,富长虹. 2018

[10]多源中高分辨率影像协同下时间合成窗口对农作物识别的影响. 童婉婷,魏浩东,杨靖雅,金文捷,宋茜,胡琼,尹高飞,徐保东. 2024

[11]Monitoring Plastic-Mulched Farmland by Landsat-8 OLI Imagery Using Spectral and Textural Features. Hasituya,Chen, Zhongxin,Wang, Limin,Wu, Wenbin,Li, He,Jiang, Zhiwei. 2016

[12]Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm. Rozenstein, Offer,Karnieli, Arnon,Qin, Zhihao,Derimian, Yevgeny. 2014

[13]Cross-sensor data reconstruction for optical remote sensing gap-filling with attention-enhanced multi-scale fusion network. Xi Wang,Songchao Chen,Chang Zhou,Si Bo Duan,Zhou Shi. 2025

[14]Estimation of regional crop yield by assimilating multi-temporal TM images into crop growth model. Yang, Peng,Zhou, Qingbo,Chen, Zhongxin,Zha, Yan,Wu, Wenbin,Shibasaki, Ryosuke. 2006

[15]Jointly Assimilating MODIS LAI and ET Products Into the SWAP Model for Winter Wheat Yield Estimation. Huang, Jianxi,Ma, Hongyuan,Su, Wei,Zhang, Xiaodong,Huang, Jianxi,Ma, Hongyuan,Su, Wei,Zhang, Xiaodong,Huang, Yanbo,Fan, Jinlong,Wu, Wenbin. 2015

[16]Comparisons of MODIS LAI products and LAI estimates derived from Landsat TM. Yang, Peng,Chen, Zhongxin,Zhou, Qingbo,Zha, Yan,Wu, Wenbin,Shibasaki, Ryosuke. 2006

[17]Assessment of the MODIS LAI Product Using Ground Measurement Data and HJ-1A/1B Imagery in the Meadow Steppe of Hulunber, China. Li, Zhenwang,Tang, Huan,Xin, Xiaoping,Zhang, Baohui,Wang, Dongliang. 2014

[18]A model with leaf area index and apple size parameters for 2.4 GHz radio propagation in apple orchards. Guo, Xiu-ming,Guo, Xiu-ming,Yang, Xin-ting,Chen, Mei-xiang,Li, Ming,Wang, Yan-an.

[19]Evaluation of MODIS land cover and LAI products in cropland of North China plain using in situ measurements and landsat TM images. Yang, Peng,Shibasaki, Ryosuke,Wu, Wenbin,Zhou, Qingbo,Chen, Zhongxin,Zha, Yan,Shi, Yun,Tang, Huajun. 2007

[20]Evaluation of Winter Wheat Yield Simulation Based on Assimilating LAI Retrieved from Networked Optical and SAR Remotely Sensed Images into the WOFOST Model. Shangrong Wu,Jianqiang Ren,Zhongxin Chen,Peng Yang,He Li,Jia Liu. 2021

作者其他论文 更多>>