| Remote sensing estimation model of cotton leaf SPAD value at the whole growth period |
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| DOI：10.7606/j.issn.1000-7601.2017.05.07 |
| Key Words: hyperspectral remote sensing estimation model PLSR SPAD value the whole growth period |
| Author Name | Affiliation | | MA Wen-jun | College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | CHANG Qing-rui | College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | TIAN Ming-lu | College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | BAN Song-tao | College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China |
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| Abstract: |
| Chlorophyll concentration is an important parameter to evaluate cotton’s growth conditions. So it is significant to estimate chlorophyll content for monitoring of cotton growth information. The materials of this research was the cotton in field in Wei-bei plateau region. Firstly the SPAD value was measured with SPAD-502 in field, and the spectral reflectance of canopy was measured with SVC Handheld spectrometer. Then the correlation was analyzed between the SPAD value and single narrow band raw reflectance, or the first derivative spectral reflectance, or spectral indices combined from different band. The prediction model was established with 5 representative spectral indices. At the same time, the simulation model of remote sensing of canopy SPAD value at the whole growth period in cotton was estimated based on PLSR method. Finally, the highest precision model was filtered out by testing. The result showed that the model based on various spectral indices with PLSR method obtained the most satisfing results for the estimation of chlorophyll concentration, R2 of the estimation model is 0.733, R2 of the verification model was up to 0.737. The remote sensing models at the whole growth stage in cotton built with PLSR method based on important spectral indices provides a basis for monitoring cotton crop growing trend and forecasting production with reliable forecast. |
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