Improving the spatial and temporal estimation of ecosystem respiration using multi-source data and machine learning methods in a rainfed winter wheat cropland

Ruhua Lu; Pei Zhang; Zhaopeng Fu; Jie Jiang; Jiancheng Wu; Qiang Cao; Yongchao Tian; Yan Zhu; Weixing Cao; Xiaojun Liu

doi:10.1016/j.scitotenv.2023.161967

Improving the spatial and temporal estimation of ecosystem respiration using multi-source data and machine learning methods in a rainfed winter wheat cropland

Sci Total Environ. 2023 May 1:871:161967. doi: 10.1016/j.scitotenv.2023.161967. Epub 2023 Feb 2.

Authors

Ruhua Lu¹, Pei Zhang¹, Zhaopeng Fu¹, Jie Jiang¹, Jiancheng Wu¹, Qiang Cao¹, Yongchao Tian¹, Yan Zhu¹, Weixing Cao¹, Xiaojun Liu²

Affiliations

¹ National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; MOE Engineering Research Center of Smart Agricultural, Nanjing Agricultural University, Nanjing 210095, China; MARA Key Laboratory for Crop System Analysis and Decision Making, Nanjing Agricultural University, Nanjing 210095, China; Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing 210095, China.
² National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; MOE Engineering Research Center of Smart Agricultural, Nanjing Agricultural University, Nanjing 210095, China; MARA Key Laboratory for Crop System Analysis and Decision Making, Nanjing Agricultural University, Nanjing 210095, China; Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing 210095, China. Electronic address: liuxj@njau.edu.cn.

PMID: 36737023
DOI: 10.1016/j.scitotenv.2023.161967

Abstract

The investigation of ecosystem respiration (RE) and its vital influential factors along with the timely and accurate detection of spatiotemporal variations in RE are essential for guiding agricultural production planning. RE observation in the plot region is primarily based on the laborious chamber method. However, upscaling the spatial-temporal estimates of RE at the canopy scale is still challenging. The present study conducted a field experiment to determine RE using the chamber method. A multi-rotor unmanned aerial vehicle (UAV) equipped with a multispectral camera was employed to acquire the canopy spectral data of wheat during each RE test experiment. Moreover, the agronomic indicators of aboveground plant biomass, leaf area index, leaf dry mass as well as agrometeorological and soil data were measured simultaneously. The study analyzed the potential of multi-information for estimating RE at the field scale and proposed two strategies for RE estimation. In addition, a semiempirical, yet Lloyd and Taylor-based, remote sensing model (LT1-NIR_V) was developed for estimating RE observed across different growth stages with a small margin of error (coefficient of determination [R²] = 0.60-0.64, root-mean-square error [RMSE] = 285.98-316.19 mg m^-2 h^-1). Further, five machine learning (ML) algorithms were utilized to independently estimate RE using two different datasets. The rigorous analyses, which included statistical comparison and cross-validation for estimating RE, confirmed that the XGBoost model, with the highest R² and lowest RMSE (R² = 0.88 and RMSE = 172.70 mg m^-2 h^-1), performed the best among the evaluated ML models. The LT1-NIR_V model was less effective in estimating RE compared with the other ML models. Based on this comprehensive comparison analysis, the ML model can successfully estimate variations in wheat field RE using high-resolution UAV multispectral images and environmental factors from the wheat cropland system, thereby providing a valuable reference for monitoring and upscaling RE observations.

Keywords: Ecosystem respiration; Machine learning; Multi-source data; RS-RE model; UAV multispectral remote sensing.

MeSH terms

Crops, Agricultural
Ecosystem*
Remote Sensing Technology* / methods
Respiration
Triticum