气候变化领域集成服务门户(CCPortal): Establishing a Hyperspectral Model for the Chlorophyll and Crude Protein Content in Alpine Meadows Using a Backward Feature Elimination Method

CCPortal

DOI	10.3390/agriculture14050757
	Establishing a Hyperspectral Model for the Chlorophyll and Crude Protein Content in Alpine Meadows Using a Backward Feature Elimination Method
	Ji, Tong; Liu, Xiaoni
发表日期	2024
EISSN	2077-0472
起始页码	14
结束页码	5
卷号	14 期号:5
英文摘要	(1) Background: The effective selection of hyperspectral feature bands is pivotal in monitoring the nutritional status of intricate alpine grasslands on the Qinghai-Tibet Plateau. The traditional methods often employ hierarchical screening of multiple feature indicators, but their universal applicability suffers due to the use of a consistent methodology across diverse environmental contexts. To remedy this, a backward feature elimination (BFE) selection method has been proposed to assess indicator importance and stability. (2) Methods: As research indicators, the crude protein (CP) and chlorophyll (Chl) contents in degraded grasslands on the Qinghai-Tibet Plateau were selected. The BFE method was integrated with partial least squares regression (PLS), random forest (RF) regression, and tree-based regression (TBR) to develop CP and Chl inversion models. The study delved into the significance and consistency of the forage quality indicator bands. Subsequently, a path analysis framework (PLS-PM) was constructed to analyze the influence of grassland community indicators on SpecChl and SpecCP. (3) Results: The implementation of the BFE method notably enhanced the prediction accuracy, with Delta R2RF-Chl = 56% and Delta R2RF-CP = 57%. Notably, spectral bands at 535 nm and 2091 nm emerged as pivotal for CP prediction, while vegetation indices like the PRI and mNDVI were critical for Chl estimation. The goodness of fit for the PLS-PM stood at 0.70, indicating the positive impact of environmental factors such as grassland cover on SpecChl and SpecCP prediction (rChl = 0.73, rCP = 0.39). SpecChl reflected information pertaining to photosynthetic nitrogen associated with photosynthesis (r = 0.80). (4) Disscusion: Among the applied model methods, the BFE+RF method is excellent in periodically discarding variables with the smallest absolute coefficient values. This variable screening method not only significantly reduces data dimensionality, but also gives the best balance between model accuracy and variables, making it possible to significantly improve model prediction accuracy. In the PLS-PM analysis, it was shown that different coverage and different community structures and functions affect the estimation of SpecCP and SpecChl. In addition, SpecChl has a positive effect on the estimation of SpecCP (r = 0.80), indicating that chlorophyll does reflect photosynthetic nitrogen information related to photosynthesis, but it is still difficult to obtain non-photosynthetic and compound nitrogen information. (5) Conclusions: The application of the BFE + RF method to monitoring the nutritional status of complex alpine grasslands demonstrates feasibility. The BFE filtration process, focusing on importance and stability, bolsters the system's generalizability, resilience, and versatility. A key research avenue for enhancing the precision of CP monitoring lies in extracting non-photosynthetic nitrogen information.
英文关键词	hyperspectral reflectance; feature elimination method; nutrient; alpine grassland; model
语种	英语
WOS研究方向	Agriculture
WOS类目	Agronomy
WOS记录号	WOS:001232969300001
来源期刊	AGRICULTURE-BASEL
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/293005
作者单位	Gansu Agricultural University; Gansu Agricultural University
推荐引用方式 GB/T 7714	Ji, Tong,Liu, Xiaoni. Establishing a Hyperspectral Model for the Chlorophyll and Crude Protein Content in Alpine Meadows Using a Backward Feature Elimination Method[J],2024,14(5).
APA	Ji, Tong,&Liu, Xiaoni.(2024).Establishing a Hyperspectral Model for the Chlorophyll and Crude Protein Content in Alpine Meadows Using a Backward Feature Elimination Method.AGRICULTURE-BASEL,14(5).
MLA	Ji, Tong,et al."Establishing a Hyperspectral Model for the Chlorophyll and Crude Protein Content in Alpine Meadows Using a Backward Feature Elimination Method".AGRICULTURE-BASEL 14.5(2024).