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An optimized non-linear vegetation index for estimating leaf area index in winter wheat

التفاصيل البيبلوغرافية
العنوان: An optimized non-linear vegetation index for estimating leaf area index in winter wheat
المؤلفون: Gege Hou, Wei Feng, Tiancai Guo, Yangyang Wang, Xingxu Ren, Yonghua Wang, Li He, Yapeng Wu, Wandai Liu
المصدر: Precision Agriculture. 20:1157-1176
بيانات النشر: Springer Science and Business Media LLC, 2019.
سنة النشر: 2019
مصطلحات موضوعية: Spectral index, Winter wheat, 0211 other engineering and technologies, 04 agricultural and veterinary sciences, 02 engineering and technology, Enhanced vegetation index, Reflectivity, Combinatorics, Crop production, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Leaf area index, Vegetation Index, General Agricultural and Biological Sciences, Crop management, 021101 geological & geomatics engineering, Mathematics
الوصف: Using hyperspectral remote sensing technology to monitor leaf area index (LAI) in a timely, fast and non-destructive manner is essential for accurate quantitative crop management. The relationships between existing vegetation indices (VIs) and LAI usually tend to saturate under dense canopies in crop production. The purpose of this study was to propose a new VI in which the estimating saturation is greatly weakened, and prediction accuracy is improved under conditions of high LAI in winter wheat (Triticum aestivum L.). The quantitative relationship between ground-based canopy spectral reflectance and LAI in wheat was investigated. The results showed that the optimized band combination, namely, the form of non-linear vegetation index (NLI) was more sensitive to changes in LAI. When λ(x1) = 798 nm and λ(y2) = 728 nm, the band combination NLI (798,728) had the highest R2 of 0.757. Among the common VIs, the modified triangular vegetation index 2 (MTVI2), the ratio spectral index [RSI (760,730)] and the 2-band enhanced vegetation index (EVI2) gave superior performance (R2 > 0.710) in terms of LAI estimation, but were worse than NLI (798,728). Inspired by the modified non-linear vegetation index (MNLI), NLI (798,728) was further optimized to become a novel optimized non-linear vegetation index (ONLI), which can be calculated by the formula $${{\left( { 1 { + 0} . 0 5} \right) \, \times \, \left( { 0. 6\, \times \,R_{ 7 9 8}^{2} \, - \,R_{ 7 2 8} } \right)} \mathord{\left/ {\vphantom {{\left( { 1 { + 0} . 0 5} \right) \, \times \, \left( { 0. 6\, \times \,R_{ 7 9 8}^{2} \, - \,R_{ 7 2 8} } \right)} { \left( { 0. 6\, \times \,R_{ 7 9 8}^{2} \, + \,R_{ 7 2 8} { + 0} . 0 5} \right)}}} \right. \kern-0pt} { \left( { 0. 6\, \times \,R_{ 7 9 8}^{2} \, + \,R_{ 7 2 8} { + 0} . 0 5} \right)}}$$ . The unified ONLI model gave an R2 of 0.779 and root mean square error (RMSE) of 1.013 across all datasets. These results indicate that the novel ONLI has strong adaptability to various cultivation conditions and can provide a good estimate of LAI in winter wheat.
تدمد: 1573-1618
1385-2256
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::50d56ac84e392bc0bc7b3cce44e71be3
https://doi.org/10.1007/s11119-019-09648-8
حقوق: OPEN
رقم الأكسشن: edsair.doi...........50d56ac84e392bc0bc7b3cce44e71be3
قاعدة البيانات: OpenAIRE
الوصف
تدمد:15731618
13852256