气候变化领域集成服务门户(CCPortal): Field spectroscopy of canopy nitrogen concentration in temperate grasslands using a convolutional neural network

CCPortal

DOI	10.1016/j.rse.2021.112353
	Field spectroscopy of canopy nitrogen concentration in temperate grasslands using a convolutional neural network
	Pullanagari R.R.; Dehghan-Shoar M.; Yule I.J.; Bhatia N.
发表日期	2021
ISSN	00344257
卷号	257
英文摘要	As an essential feature of plant autotrophy, Nitrogen (N) is the major nutrient affecting plant growth in terrestrial ecosystems, thus is of not only fundamental scientific interest, but also a crucial factor in crop productivity. Timely non-destructive monitoring of canopy nitrogen concentration (N%) demands fast and highly accurate estimation, which is often quantified using spectroscopic analyses in the 400—2500 nm spectral region. However, extracting a set of useful spectral absorption features from canopy spectra to determine N% remains challenging due to confounding canopy architecture. Deep Learning as a statistical learning technique is useful to extract biochemical information from canopy spectra. We evaluated the performance of a one-dimensional convolutional neural network (1D-CNN) and compared it with two state-of-the-art methods: partial least squares regression (PLSR) and gaussian process regression (GPR). We utilized a large and diverse in-field multi-season (autumn, winter, spring and summer) spectral database (n = 7014) over 8 years (2009–2016) of dairy and hill country farms across New Zealand to develop season specific and spectral-region specific (VNIR and/or SWIR) 1D-CNN models. Results on the independent validation dataset (not used to train the model) showed that the 1D-CNN model provided higher accuracy (R2 = 0.72; nRMSE% = 14) than PLSR (R2 = 0.54; nRMSE% = 19) and GPR (with R2 = 0.62; nRMSE% = 16). Season specific models based on 1D-CNN indicated apparent differences (14 ≤ nRMSE ≤19 for the test dataset), while the performance of all seasons combined model was remained higher for the test dataset (nRMSE% = 14). The full spectral range model showed higher accuracy than the spectral region-specific models (VNIR and SWIR alone) (15.8 ≤ nRMSE ≤18.5). Additionally, predictions derived using 1D-CNN were more precise (less uncertain) with <0.12 mean standard deviation (uncertainty intervals) than PLSR (0.31) and GPR (0.16). This study demonstrated the potential of 1D-CNN as an alternative to conventional techniques to determine the N% from canopy hyperspectral spectra. © 2021 Elsevier Inc.
英文关键词	Deep learning; Gaussian process regression; Nitrogen; One-dimensional convolutional neural network; Partial least squares regression; Prediction uncertainty; Spectroscopy
语种	英语
scopus关键词	Convolution; Deep learning; Least squares approximations; Nitrogen; Spectroscopic analysis; Statistical tests; Biochemical information; Gaussian process regression; Mean standard deviation; Nitrogen concentrations; Non-destructive monitoring; Partial least squares regressions (PLSR); Spectral absorption features; Statistical learning techniques; Convolutional neural networks; artificial neural network; crop production; detection method; field method; grassland; ground penetrating radar; seasonal variation; soil nitrogen; spatiotemporal analysis; spectroscopy; temperate environment; terrestrial ecosystem; New Zealand
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178914
作者单位	AgriFood Digital Lab, School of Food and Advanced Technology, College of Sciences, Massey University, Private Bag 11-222, Palmerston North, 4442, New Zealand; PlantTech Research Institute Ltd, 35 Grey Street, Tauranga, 3110, New Zealand
推荐引用方式 GB/T 7714	Pullanagari R.R.,Dehghan-Shoar M.,Yule I.J.,et al. Field spectroscopy of canopy nitrogen concentration in temperate grasslands using a convolutional neural network[J],2021,257.
APA	Pullanagari R.R.,Dehghan-Shoar M.,Yule I.J.,&Bhatia N..(2021).Field spectroscopy of canopy nitrogen concentration in temperate grasslands using a convolutional neural network.Remote Sensing of Environment,257.
MLA	Pullanagari R.R.,et al."Field spectroscopy of canopy nitrogen concentration in temperate grasslands using a convolutional neural network".Remote Sensing of Environment 257(2021).