Next Article in Journal
Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data
Next Article in Special Issue
Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning
Previous Article in Journal
Mapping Urban Impervious Surfaces by Using Spectral Mixture Analysis and Spectral Indices
Previous Article in Special Issue
Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery
Article

Evaluating Different Non-Destructive Estimation Methods for Winter Wheat (Triticum aestivum L.) Nitrogen Status Based on Canopy Spectrum

1
Key Laboratory of Agricultural Water Resources, The Innovative Academy of Seed Design, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, 286 Huaizhong Road, Shijiazhuang 050021, China
2
Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 95; https://doi.org/10.3390/rs12010095
Received: 18 November 2019 / Revised: 23 December 2019 / Accepted: 24 December 2019 / Published: 26 December 2019
(This article belongs to the Special Issue Remote Sensing for Precision Nitrogen Management)
Compared to conventional laboratory testing methods, crop nitrogen estimation methods based on canopy spectral characteristics have advantages in terms of timeliness, cost, and practicality. A variety of rapid and non-destructive estimation methods based on the canopy spectrum have been developed on the scale of space, sky, and ground. In order to understand the differences in estimation accuracy and applicability of these methods, as well as for the convenience of users to select the suitable technology, models for estimation of nitrogen status of winter wheat were developed and compared for three methods: drone equipped with a multispectral camera, soil plant analysis development (SPAD) chlorophyll meter, and smartphone photography. Based on the correlations between observed nitrogen status in winter wheat and related vegetation indices, green normalized difference vegetation index (GNDVI) and visible atmospherically resistant index (VARI) were selected as the sensitive vegetation indices for the drone equipped with a multispectral camera and smartphone photography methods, respectively. The correlation coefficients between GNDVI, SPAD, and VARI were 0.92 ** and 0.89 **, and that between SPAD and VARI was 0.90 **, which indicated that three vegetation indices for these three estimation methods were significantly related to each other. The determination coefficients of the 0–90 cm soil nitrate nitrogen content estimation models for the drone equipped with a multispectral camera, SPAD, and smartphone photography methods were 0.63, 0.54, and 0.81, respectively. In the estimation accuracy evaluation, the method of smartphone photography had the smallest root mean square error (RMSE = 9.80 mg/kg). The accuracy of the smartphone photography method was slightly higher than the other two methods. Due to the limitations of these models, it was found that the crop nitrogen estimation methods based on canopy spectrum were not suitable for the crops under severe phosphate deficiency. In addition, in estimation of soil nitrate nitrogen content, there were saturation responses in the estimation indicators of the three methods. In order to introduce these three methods in the precise management of nitrogen fertilizer, it is necessary to further improve their estimation models. View Full-Text
Keywords: canopy spectrum; non-destructive nitrogen status diagnosis; drone; multispectral camera; SPAD; smartphone photography canopy spectrum; non-destructive nitrogen status diagnosis; drone; multispectral camera; SPAD; smartphone photography
Show Figures

Graphical abstract

MDPI and ACS Style

Li, H.; Zhang, Y.; Lei, Y.; Antoniuk, V.; Hu, C. Evaluating Different Non-Destructive Estimation Methods for Winter Wheat (Triticum aestivum L.) Nitrogen Status Based on Canopy Spectrum. Remote Sens. 2020, 12, 95. https://doi.org/10.3390/rs12010095

AMA Style

Li H, Zhang Y, Lei Y, Antoniuk V, Hu C. Evaluating Different Non-Destructive Estimation Methods for Winter Wheat (Triticum aestivum L.) Nitrogen Status Based on Canopy Spectrum. Remote Sensing. 2020; 12(1):95. https://doi.org/10.3390/rs12010095

Chicago/Turabian Style

Li, Hongjun, Yuming Zhang, Yuping Lei, Vita Antoniuk, and Chunsheng Hu. 2020. "Evaluating Different Non-Destructive Estimation Methods for Winter Wheat (Triticum aestivum L.) Nitrogen Status Based on Canopy Spectrum" Remote Sensing 12, no. 1: 95. https://doi.org/10.3390/rs12010095

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop