Potential of Hyperspectral Data Combined With Optimal Band Combination Algorithm for Estimating Soil Organic Carbon Content in Lakeside Oasis
Bibliographic Data
| ID | 21649152 |
|---|---|
| Authors | Jixiang Yang (0000-0002-9631-2046, College of Geographic Sciences and Tourism Xinjiang Normal University Urumqi China), Xinguo Li (0000-0003-3277-9808, College of Geographic Sciences and Tourism Xinjiang Normal University Urumqi China, corresponding author), Xiaofei Ma (0000-0001-9456-0065, State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi China), Xiangyu Ge (0009-0008-2341-8051, Xinjiang Key Laboratory of Oasis Ecology Xinjiang University Urumqi China) |
| Year | 2024 |
| Publication date | 2024-10-09 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Land Degradation and Development (JOURNAL) |
| Journal identifiers | ISSN: 1085-3278 • E-ISSN: 1099-145X |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/ldr.5339 |
| OpenAlex | W4403255739 |
| Language | EN |
| Citations received | 2 |
| References cited | 42 |
Accurate estimation of soil organic carbon (SOC) content is essential for promoting regional sustainable agriculture and improving land quality. Visible and near‐infrared (Vis‐NIR) near‐Earth remote sensing spectroscopy has become an effective alternative to the traditional time‐consuming and costly methods due to its high‐resolution and nondestructive application, but it is vulnerable to the redundancy of spectral information and the overlap between bands. This study delves into the potential of optimal spectral parameters for estimating SOC in arid lakeside oases, using Bosten Lake in Xinjiang, China, as a focal point. Soil samples (0–10 cm, 10–20 cm, 20–30 cm, 30–40 cm) were collected, and their SOC content and hyperspectral reflectance were measured. The spectral data underwent preprocessing techniques, including continuum removal (CR), standard normal variate (SNV), and continuous wavelet transform (CWT). SOC content was predicted using back propagation neural network models constructed based on one‐dimensional (1D), two‐dimensional (2D), and three‐dimensional (3D) correlation coefficients. Results showcased the effectiveness of the CWT method in accentuating potential spectral information and enhancing variable correlation. Among the indices, 3D exhibited the highest performance ( R 2 = 0.82, RPD = 2.02 for TDI‐1 at 0–10 cm; R 2 = 0.85, RPD = 2.28 for TDI‐2 at 10–20 cm; R 2 = 0.83, RPD = 2.24 for TDI‐1 at 20–30 cm; R 2 = 0.86, RPD = 2.53 for TDI‐4 at 30–40 cm), followed by 2D and then 1D. These insights offer guidance for future strategies in hyperspectral data preprocessing and spectral index determination, facilitating SOC spatial distribution mapping and advancing sustainable agricultural planning. They also have implications for determining the spatial distribution of SOC content based on spatial interpolation, which would contribute to regional agricultural planning and sustainable development
Algorithm · Hyperspectral imaging · Imaging spectrometer · Near-infrared spectroscopy · Physics · Preprocessor · Remote sensing · Soil carbon · Soil water · Spectrometer · Computer Science · Environmental Science · Geochemistry and Geologic Mapping · Mathematics · Remote Sensing in Agriculture · Soil Geostatistics and Mapping · Artificial Intelligence · Geology · Soil Science
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |