Enhanced Early Detection of Cadmium Stress in Rice: Introducing a Novel Spectral Index Based on an Enhanced GAMI-Net Model

Jie Liu, Zhao Zhang, Shangran Zhou, Xingwang Liu (), Feng Li () and Lei Mao
Additional contact information
Jie Liu: Department of Environment, College of Environment and Resources, Xiangtan University, Xiangtan 411105, China
Zhao Zhang: RIOH High Science and Technology Group, Beijing 100088, China
Shangran Zhou: Department of Environment, College of Environment and Resources, Xiangtan University, Xiangtan 411105, China
Xingwang Liu: Department of Environment, College of Environment and Resources, Xiangtan University, Xiangtan 411105, China
Feng Li: Department of Environment, College of Environment and Resources, Xiangtan University, Xiangtan 411105, China
Lei Mao: China Urban Construction Design & Research Institute Co., Ltd., Beijing 100120, China

Sustainability, 2024, vol. 16, issue 19, 1-21

Abstract: Soil cadmium contamination poses a significant threat to global food security and human health, making the timely and accurate diagnosis of cadmium stress in rice crucial for effective pollution control and agricultural management. However, during the early growth stages of rice, particularly the tillering stage, the spectral response to cadmium stress is subtle, rendering traditional remote sensing methods inadequate. This study aims to develop an efficient early diagnosis index, the Cadmium Early Stress Index (CESI), for rapid and accurate detection of cadmium stress in rice at a regional scale. By integrating field surveys with Sentinel-2 satellite data, the study extracts multi-angle spectral features and employs an enhanced Generalized Additive Model Neural Network (E-GAMI-Net) for analysis. E-GAMI-Net analysis identified key indicators for early diagnosis, including log-transformed reflectance at 941 nm (R941_log), Optimized Soil-Adjusted Vegetation Index (OSAVI), and the interaction between Red Edge Amplitude and Chlorophyll content. Based on these findings, CESI was constructed, demonstrating superior diagnostic performance (R 2 = 0.77, RMSE = 0.09 mg/kg) compared to existing methods. CESI also exhibited high stability under noise interference, with only a 5.6% reduction in R 2 under 15% noise. In regional-scale remote sensing applications, CESI successfully generated cadmium stress distribution maps, identifying previously undetected moderate stress areas. CESI’s high accuracy (R 2 = 0.6073, RMSE = 0.3021) and stability make it a promising tool for large-scale cadmium stress monitoring and precision agriculture management.

Keywords: cadmium stress; early stress index; GAMI-Net; multi-angle spectral features; remote sensing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/19/8341/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/19/8341/ (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:jsusta:v:16:y:2024:i:19:p:8341-:d:1485589

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

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