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RSS FeedsRemote Sensing, Vol. 12, Pages 763: Assessing Performance of Vegetation Indices to Estimate Nitrogen Nutrition Index in Pepper (Remote Sensing)

 
 

27 february 2020 02:02:56

 
Remote Sensing, Vol. 12, Pages 763: Assessing Performance of Vegetation Indices to Estimate Nitrogen Nutrition Index in Pepper (Remote Sensing)
 


Vegetation indices (VIs) can be useful tools to evaluate crop nitrogen (N) status. To be effective, VIs measurements must be related to crop N status. The nitrogen nutrition index (NNI) is a widely accepted parameter of crop N status. The present work evaluates the performance of several VIs to estimate NNI in sweet pepper (Capsicum annuum). The performance of VIs to estimate NNI was evaluated using parameters of linear regression analysis conducted for calibration and validation. Three different sweet pepper crops were grown with combined irrigation and fertigation, in Almería, Spain. In each crop, five different N concentrations in the nutrient solution were frequently applied by drip irrigation. Proximal crop reflectance was measured with Crop Circle ACS470 and GreenSeeker handheld sensors, approximately every ten days, throughout the crops. The relative performance of VIs differed between phenological stages. Relationships of VIs with NNI were strongest in the early fruit growth and flowering stages, and less strong in the vegetative and harvest stages. The green band-based VIs, GNDVI, and GVI, provided the best results for estimating crop NNI in sweet pepper, for individual phenological stages. GNDVI had the best performance in the vegetative, flowering, and harvest stages, and GVI had the best performance in the early fruit growth stage. Some of the VIs evaluated are promising tools to estimate crop N status in sweet pepper and have the potential to contribute to improving crop N management of sweet pepper crops.


 
164 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 12, Pages 750: Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China (Remote Sensing)
Remote Sensing, Vol. 12, Pages 762: An Optimized Faster R-CNN Method Based on DRNet and RoI Align for Building Detection in Remote Sensing Images (Remote Sensing)
 
 
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