A Comparison of Two Approaches for Estimating the Wheat Nitrogen Nutrition Index Using Remote Sensing

Remote Sensing

Pengfei Chen¹  Anton Vrieling² 

[1] State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, Datun Road A11, Chaoyang District, Beijing 100101, China; E-Mail:;State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, Datun Road A11, Chaoyang District, Beijing 100101, China; E-Mail

关键词: NNI;    remote sensing;    nitrogen nutrition diagnosis;    winter wheat;   

DOI  :  10.3390/rs70404527

来源: mdpi

【 摘 要 】

Remote predictions of the nitrogen nutrition index (NNI) are useful for precise nitrogen (N) management in the field. Several studies have recommended two methods for estimating the NNI, which are classified as mechanistic and semi-empirical methods in this study. However, no studies have been conducted to thoroughly analyze and compare these two methods. Using winter wheat as an example, this study compared the performances of these two methods for estimating the NNI to determine which method is more suitable for practical use. Field measurements were conducted to determine the above ground biomass, N concentration and canopy spectra during different wheat growth stages in 2012. Nearly 120 samples of data were collected and divided into different calibration and validation datasets (containing data from single or multi-growth stages). Based on the above datasets, the performances of the two NNI estimation methods were compared, and the influences of phenology on the methods were analyzed. All models that used the mechanistic method with different calibration datasets performed well when validated by validation datasets containing single growth or multi-growth stage data. The validation results had R² values between 0.82 and 0.94, root mean square error (RMSE) values between 0.05 and 0.17, and RMSE% values between 5.10% and 14.41%. Phenology had no effect on this type of NNI estimation method. However, the semi-empirical method was influenced by phenology. The performances of the models established using this method were determined by the type of data used for calibration. Thus, the mechanistic method is recommended as a better method for estimating the NNI. By combining proper N management strategies, it can be used for precise N management.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.