气候变化领域集成服务门户(CCPortal): Hyperspectral imagery to monitor crop nutrient status within and across growing seasons

CCPortal

DOI	10.1016/j.rse.2021.112303
	Hyperspectral imagery to monitor crop nutrient status within and across growing seasons
	Liu N.; Townsend P.A.; Naber M.R.; Bethke P.C.; Hills W.B.; Wang Y.
发表日期	2021
ISSN	00344257
卷号	255
英文摘要	Imaging spectroscopy provides the opportunity to monitor nutrient status of vegetation. In crops, prior studies have generally been limited in scope, either to a small wavelength range (e.g., 400–1300 nm), a small number of crop cultivars, a single growth stage or single growing season. Methods that are not time- or site-specific are needed to use imaging spectroscopy for routine monitoring of crop status. Using data from four cultivars of potatoes (Solanum tuberosum L.), three growth stages and two growing seasons, we demonstrate the capacity of full-range (400–2350 nm) imaging spectroscopy to quantify nutrient status (petiole nitrate, whole leaf and vine total nitrogen) and predict tuber yield in potatoes across cultivars, growth stages and growing seasons. We specifically tested the capabilities of: (1) ordinary least-squares regression (OLSR) using traditional hyperspectral vegetation indices (VIs); (2) partial least-squares regression (PLSR) using full spectrum (400–2350 nm), VNIR- (visible-to-near infrared: 400–1300 nm) or SWIR-only (shortwave infrared: 1400–2350 nm) wavelengths; (3) predictive models developed for one potato type or planting season on withheld data from a different type or season. Our results show that OLSR models produced poor predictions with data from all dates pooled together (validation R2 < 0.01). Single-date OLSR models performed better (R2 = 0.20–0.60, relative RMSE = 15–30%). PLSR models performed well and were comparable using different spectral regions (full-spectrum, VNIR-only and SWIR-only), with validation R2 = 0.68–0.82 and RRMSE = 12–25%. Testing across potato types, models produced reliable predictions (R2 = 0.45–0.75, RRMSE = 13–30%), but with some bias. Cross-season models had validation R2 = 0.46–0.75 and RRMSE = 17–100%, with a more significant bias than the cross-potato type models. To achieve models that are generalizable and robust, we recommend: (1) obtaining ground measurements that capture the full range of plant growth conditions and developmental stages, and (2) ensuring that image processing approaches minimize spectral discrepancies among dates. © 2021 Elsevier Inc.
英文关键词	Foliar nitrogen; Imaging spectroscopy; Petiole nitrate; Potato; Tuber yield
语种	英语
scopus关键词	Crops; Cultivation; Forecasting; Image processing; Infrared devices; Infrared radiation; Least squares approximations; Nutrients; Spectroscopy; Vegetation; Developmental stage; Ground measurements; Hyper-spectral imageries; Hyperspectral vegetation indices; Imaging spectroscopy; Ordinary least squares regressions; Partial least squares regressions (PLSR); Short wave infrared; Predictive analytics; Solanum tuberosum; Varanidae
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/178963
作者单位	Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, United States; Department of Horticulture, University of Wisconsin-Madison, Madison, WI, United States; Vegetable Crop Research Unit, USDA-ARS, Madison, WI, United States
推荐引用方式 GB/T 7714	Liu N.,Townsend P.A.,Naber M.R.,et al. Hyperspectral imagery to monitor crop nutrient status within and across growing seasons[J],2021,255.
APA	Liu N.,Townsend P.A.,Naber M.R.,Bethke P.C.,Hills W.B.,&Wang Y..(2021).Hyperspectral imagery to monitor crop nutrient status within and across growing seasons.Remote Sensing of Environment,255.
MLA	Liu N.,et al."Hyperspectral imagery to monitor crop nutrient status within and across growing seasons".Remote Sensing of Environment 255(2021).