Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters
Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events. Leaf water content (LWC) is an important waterlogging indicator, and hyperspectral remote sensing provides a non-destructive, real-time and reliable method to det...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-10-01
|
| Series: | Journal of Integrative Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095311920633068 |
| _version_ | 1818833235747012608 |
|---|---|
| author | Fei-fei YANG Tao LIU Qi-yuan WANG Ming-zhu DU Tian-le YANG Da-zhong LIU Shi-juan LI Sheng-ping LIU |
| author_facet | Fei-fei YANG Tao LIU Qi-yuan WANG Ming-zhu DU Tian-le YANG Da-zhong LIU Shi-juan LI Sheng-ping LIU |
| author_sort | Fei-fei YANG |
| collection | DOAJ |
| description | Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events. Leaf water content (LWC) is an important waterlogging indicator, and hyperspectral remote sensing provides a non-destructive, real-time and reliable method to determine LWC. Thus, based on a pot experiment, winter wheat was subjected to different gradients of waterlogging stress at the jointing stage. Leaf hyperspectral data and LWC were collected every 7 days after waterlogging treatment until the winter wheat was mature. Combined with methods such as vegetation index construction, correlation analysis, regression analysis, BP neural network (BPNN), etc., we found that the effect of waterlogging stress on LWC had the characteristics of hysteresis and all waterlogging stress led to the decrease of LWC. LWC decreased faster under severe stress than under slight stress, but the effect of long-term slight stress was greater than that of short-term severe stress. The sensitive spectral bands of LWC were located in the visible (VIS, 400–780 nm) and short-wave infrared (SWIR, 1 400–2 500 nm) regions. The BPNN Model with the original spectrum at 648 nm, the first derivative spectrum at 500 nm, the red edge position (λr), the new vegetation index RVI (437, 466), NDVI (437, 466) and NDVI’ (747, 1 956) as independent variables was the best model for inverting the LWC of waterlogging in winter wheat (modeling set: R2=0.889, RMSE=0.138; validation set: R2=0.891, RMSE=0.518). These results have important theoretical significance and practical application value for the precise control of waterlogging stress. |
| first_indexed | 2024-12-19T02:15:42Z |
| format | Article |
| id | doaj.art-d5217dd460e4410d95b810df7ce0e54b |
| institution | Directory Open Access Journal |
| issn | 2095-3119 |
| language | English |
| last_indexed | 2024-12-19T02:15:42Z |
| publishDate | 2021-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Integrative Agriculture |
| spelling | doaj.art-d5217dd460e4410d95b810df7ce0e54b2022-12-21T20:40:25ZengElsevierJournal of Integrative Agriculture2095-31192021-10-01201026132626Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parametersFei-fei YANG0Tao LIU1Qi-yuan WANG2Ming-zhu DU3Tian-le YANG4Da-zhong LIU5Shi-juan LI6Sheng-ping LIU7Key Laboratory of Agri-information Service Technology, Ministry of Agriculture and Rural Affairs/Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops/Agricultural College, Yangzhou University, Yangzhou 225009, P.R.ChinaCollege of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, P.R.ChinaKey Laboratory of Agri-information Service Technology, Ministry of Agriculture and Rural Affairs/Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops/Agricultural College, Yangzhou University, Yangzhou 225009, P.R.ChinaKey Laboratory of Agri-information Service Technology, Ministry of Agriculture and Rural Affairs/Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.ChinaKey Laboratory of Agri-information Service Technology, Ministry of Agriculture and Rural Affairs/Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China; Correspondence LI Shi-juan, Tel: +86-10-82109916Key Laboratory of Agri-information Service Technology, Ministry of Agriculture and Rural Affairs/Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China; Correspondence LIU Sheng-ping, Tel: +86-10-82109348Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events. Leaf water content (LWC) is an important waterlogging indicator, and hyperspectral remote sensing provides a non-destructive, real-time and reliable method to determine LWC. Thus, based on a pot experiment, winter wheat was subjected to different gradients of waterlogging stress at the jointing stage. Leaf hyperspectral data and LWC were collected every 7 days after waterlogging treatment until the winter wheat was mature. Combined with methods such as vegetation index construction, correlation analysis, regression analysis, BP neural network (BPNN), etc., we found that the effect of waterlogging stress on LWC had the characteristics of hysteresis and all waterlogging stress led to the decrease of LWC. LWC decreased faster under severe stress than under slight stress, but the effect of long-term slight stress was greater than that of short-term severe stress. The sensitive spectral bands of LWC were located in the visible (VIS, 400–780 nm) and short-wave infrared (SWIR, 1 400–2 500 nm) regions. The BPNN Model with the original spectrum at 648 nm, the first derivative spectrum at 500 nm, the red edge position (λr), the new vegetation index RVI (437, 466), NDVI (437, 466) and NDVI’ (747, 1 956) as independent variables was the best model for inverting the LWC of waterlogging in winter wheat (modeling set: R2=0.889, RMSE=0.138; validation set: R2=0.891, RMSE=0.518). These results have important theoretical significance and practical application value for the precise control of waterlogging stress.http://www.sciencedirect.com/science/article/pii/S2095311920633068winter wheathyperspectral remote sensingleaf water contentnew vegetation indexBP neural network |
| spellingShingle | Fei-fei YANG Tao LIU Qi-yuan WANG Ming-zhu DU Tian-le YANG Da-zhong LIU Shi-juan LI Sheng-ping LIU Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters Journal of Integrative Agriculture winter wheat hyperspectral remote sensing leaf water content new vegetation index BP neural network |
| title | Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters |
| title_full | Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters |
| title_fullStr | Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters |
| title_full_unstemmed | Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters |
| title_short | Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters |
| title_sort | rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters |
| topic | winter wheat hyperspectral remote sensing leaf water content new vegetation index BP neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2095311920633068 |
| work_keys_str_mv | AT feifeiyang rapiddeterminationofleafwatercontentformonitoringwaterlogginginwinterwheatbasedonhyperspectralparameters AT taoliu rapiddeterminationofleafwatercontentformonitoringwaterlogginginwinterwheatbasedonhyperspectralparameters AT qiyuanwang rapiddeterminationofleafwatercontentformonitoringwaterlogginginwinterwheatbasedonhyperspectralparameters AT mingzhudu rapiddeterminationofleafwatercontentformonitoringwaterlogginginwinterwheatbasedonhyperspectralparameters AT tianleyang rapiddeterminationofleafwatercontentformonitoringwaterlogginginwinterwheatbasedonhyperspectralparameters AT dazhongliu rapiddeterminationofleafwatercontentformonitoringwaterlogginginwinterwheatbasedonhyperspectralparameters AT shijuanli rapiddeterminationofleafwatercontentformonitoringwaterlogginginwinterwheatbasedonhyperspectralparameters AT shengpingliu rapiddeterminationofleafwatercontentformonitoringwaterlogginginwinterwheatbasedonhyperspectralparameters |