Comparative Analysis of Feature Importance Algorithms for Grassland Aboveground Biomass and Nutrient Prediction Using Hyperspectral Data

Yue Zhao, Dawei Xu, Shuzhen Li, Kai Tang, Hongliang Yu, Ruirui Yan, Zhenwang Li (), Xu Wang () and Xiaoping Xin
Additional contact information
Yue Zhao: State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, Key Laboratory of Grassland Resource Monitoring Evaluation and Innovative Utilization, Ministry of Agriculture and Rural Affairs, Hulunber Grassland Ecosystem National Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Dawei Xu: State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, Key Laboratory of Grassland Resource Monitoring Evaluation and Innovative Utilization, Ministry of Agriculture and Rural Affairs, Hulunber Grassland Ecosystem National Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Shuzhen Li: State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, Key Laboratory of Grassland Resource Monitoring Evaluation and Innovative Utilization, Ministry of Agriculture and Rural Affairs, Hulunber Grassland Ecosystem National Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Kai Tang: State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Hongliang Yu: State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, Key Laboratory of Grassland Resource Monitoring Evaluation and Innovative Utilization, Ministry of Agriculture and Rural Affairs, Hulunber Grassland Ecosystem National Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Ruirui Yan: State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, Key Laboratory of Grassland Resource Monitoring Evaluation and Innovative Utilization, Ministry of Agriculture and Rural Affairs, Hulunber Grassland Ecosystem National Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Zhenwang Li: Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou University, Yangzhou 225009, China
Xu Wang: State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, Key Laboratory of Grassland Resource Monitoring Evaluation and Innovative Utilization, Ministry of Agriculture and Rural Affairs, Hulunber Grassland Ecosystem National Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Xiaoping Xin: State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, Key Laboratory of Grassland Resource Monitoring Evaluation and Innovative Utilization, Ministry of Agriculture and Rural Affairs, Hulunber Grassland Ecosystem National Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Agriculture, 2024, vol. 14, issue 3, 1-18

Abstract: Estimating forage yield and nutrient composition using hyperspectral remote sensing is a major challenge. However, there is still a lack of comprehensive research on the optimal wavelength for the analysis of various nutrients in pasture. In this research, conducted in Hailar District, Hulunber City, Inner Mongolia Autonomous Region, China, 126 sets of hyperspectral data were collected, covering a spectral range of 350 to 1800 nanometers. The primary objective was to identify key spectral bands for estimating forage dry matter yield (DMY), nitrogen content (NC), neutral detergent fiber (NDF), and acid detergent fiber (ADF) using principal component analysis (PCA), random forests (RF), and SHapley Additive exPlanations (SHAP) analysis methods, and then the RF and Extra-Trees algorithm (ERT) model was used to predict aboveground biomass (AGB) and nutrient parameters using the optimized spectral bands and vegetation indices. Our approach effectively minimizes redundancy in hyperspectral data by selectively employing crucial spectral bands, thus improving the accuracy of forage nutrient estimation. PCA identified the most variable bands at 400 nm, 520–550 nm, 670–720 nm, and 930–950 nm, reflecting their general spectral significance rather than a link to specific forage nutrients. Further analysis using RF feature importance pinpointed influential bands, predominantly within 930–940 nm and 700–730 nm. SHAP analysis confirmed critical bands for DMY (965 nm, 712 nm, and 1652 nm), NC (1390 nm and 713 nm), ADF (1390 nm and 715–725 nm), and NDF (400 nm, 983 nm, 1350 nm, and 1800 nm). The fitting accuracy for ADF estimated using RF was lower ( R 2 = 0.58), while the fitting accuracy for other indicators was higher ( R 2 ≥ 0.59). The performance and prediction accuracy of ERT ( R 2 = 0.63) were noticeably superior to those of RF. In conclusion, our method effectively identifies influential bands, optimizing forage yield and quality estimation.

Keywords: remote sensing data analysis; forage quality assessment; spectral feature selection; agricultural resource management; environmental sensing technologies (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/3/389/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/3/389/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:3:p:389-:d:1347912

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().