Estimation of Soil Salinity by Combining Spectral and Texture Information from UAV Multispectral Images in the Tarim River Basin, China DOI Creative Commons

Jiaxiang Zhai,

Nan Wang, Bifeng Hu

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(19), С. 3671 - 3671

Опубликована: Окт. 1, 2024

Texture features have been consistently overlooked in digital soil mapping, especially salinization mapping. This study aims to clarify how leverage texture information for monitoring through remote sensing techniques. We propose a novel method estimating salinity content (SSC) that combines spectral and from unmanned aerial vehicle (UAV) images. Reflectance, index, one-dimensional (OD) were extracted UAV Building on the features, we constructed two-dimensional (TD) three-dimensional (THD) indices. The technique of Recursive Feature Elimination (RFE) was used feature selection. Models estimation built using three distinct methodologies: Random Forest (RF), Partial Least Squares Regression (PLSR), Convolutional Neural Network (CNN). Spatial distribution maps then generated each model. effectiveness proposed is confirmed utilization 240 surface samples gathered an arid region northwest China, specifically Xinjiang, characterized by sparse vegetation. Among all indices, TDTeI1 has highest correlation with SSC (|r| = 0.86). After adding multidimensional information, R2 RF model increased 0.76 0.90, improvement 18%. models, outperforms PLSR CNN. model, which (SOTT), achieves RMSE 5.13 g kg−1, RPD 3.12. contributes 44.8% prediction, contributions TD THD indices 19.3% 20.2%, respectively. confirms great potential introducing semi-arid regions.

Язык: Английский

Multi‐Scale Soil Salinization Dynamics From Global to Pore Scale: A Review DOI Creative Commons
Nima Shokri, Amirhossein Hassani, Muhammad Sahimi

и другие.

Reviews of Geophysics, Год журнала: 2024, Номер 62(4)

Опубликована: Сен. 27, 2024

Abstract Soil salinization refers to the accumulation of water‐soluble salts in upper part soil profile. Excessive levels salinity affects crop production, health, and ecosystem functioning. This phenomenon threatens agriculture, food security, stability, fertility leading land degradation loss essential services that are fundamental sustaining life. In this review, we synthesize recent advances at various spatial temporal scales, ranging from global core, pore, molecular offering new insights presenting our perspective on potential future research directions address key challenges open questions related salinization. Globally, identify significant understanding salinity, which (a) considerable uncertainty estimating total area salt‐affected soils, (b) geographical bias ground‐based measurements (c) lack information data detailing secondary processes, both dry‐ wetlands, particularly concerning responses climate change. At core scale, impact salt precipitation with evolving porous structure evaporative fluxes media is not fully understood. knowledge crucial for accurately predicting water due evaporation. Additionally, effects transport properties media, such as mixed wettability conditions, saline evaporation resulting patterns remain unclear. Furthermore, effective continuum equations must be developed represent experimental pore‐scale numerical simulations.

Язык: Английский

Процитировано

25
Spatial variability of soil salinity in coastal saline-alkali farmlands: A novel approach integrating a stacked model with the reconstructed in-situ hyperspectral feature DOI

Dexi Zhan,

Yunting Liu, Weihao Yang

и другие.

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 235, С. 110376 - 110376

Опубликована: Апрель 19, 2025

Язык: Английский

Процитировано

1
Estimation of Soil Organic Matter Based on Spectral Indices Combined with Water Removal Algorithm DOI Creative Commons
Jiawei Xu,

Yuteng Liu,

Changxiang Yan

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(12), С. 2065 - 2065

Опубликована: Июнь 7, 2024

Soil moisture strongly interferes with the spectra of soil organic matter (SOM) in near-infrared region, which reduces correlation between and decreases accuracy prediction SOM. In this study, we explored feasibility two types spectral indices, two- three-band mixed (SI) indices (SI3), water removal algorithms, direct standardization (DS) external parameter orthogonalization (EPO), to estimate SOM wet soils using a total 192 samples at six content gradients. The estimation accuracies combined algorithms were better than those full data algorithms: SI-EPO (R2 = 0.735, RMSEp 3.4102 g/kg) higher EPO 0.63, 4.1021 g/kg), SI-DS 0.70, 3.7085 DS 0.61, 4.2806 g/kg); SI3-EPO 0.752, 3.1344 was SI-EPO; both effectively mitigated influence moisture, demonstrating superior performance small-sample scenarios. This study introduces novel approach counteract impact on estimation.

Язык: Английский

Процитировано

5
Digital mapping of soil salinity with time-windows features optimization and ensemble learning model DOI Creative Commons
Shuaishuai Shi, Nan Wang, Songchao Chen

и другие.

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102982 - 102982

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

4
Leveraging moisture elimination and hybrid deep learning models for soil organic carbon mapping with multi-modal remote sensing data DOI
Yilin Bao, Xiangtian Meng,

Weimin Ruan

и другие.

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 139, С. 104513 - 104513

Опубликована: Апрель 15, 2025

Язык: Английский

Процитировано

0
Improving in-situ spectral estimation of wetland soil organic carbon by integrating multiple optimization strategies DOI
Hongyi Li, Jiang-Tao Yang, Bifeng Hu

и другие.

CATENA, Год журнала: 2025, Номер 255, С. 109078 - 109078

Опубликована: Апрель 23, 2025

Язык: Английский

Процитировано

0
Estimation of Soil Salinity by Combining Spectral and Texture Information from UAV Multispectral Images in the Tarim River Basin, China DOI Creative Commons

Jiaxiang Zhai,

Nan Wang, Bifeng Hu

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(19), С. 3671 - 3671

Опубликована: Окт. 1, 2024

Texture features have been consistently overlooked in digital soil mapping, especially salinization mapping. This study aims to clarify how leverage texture information for monitoring through remote sensing techniques. We propose a novel method estimating salinity content (SSC) that combines spectral and from unmanned aerial vehicle (UAV) images. Reflectance, index, one-dimensional (OD) were extracted UAV Building on the features, we constructed two-dimensional (TD) three-dimensional (THD) indices. The technique of Recursive Feature Elimination (RFE) was used feature selection. Models estimation built using three distinct methodologies: Random Forest (RF), Partial Least Squares Regression (PLSR), Convolutional Neural Network (CNN). Spatial distribution maps then generated each model. effectiveness proposed is confirmed utilization 240 surface samples gathered an arid region northwest China, specifically Xinjiang, characterized by sparse vegetation. Among all indices, TDTeI1 has highest correlation with SSC (|r| = 0.86). After adding multidimensional information, R2 RF model increased 0.76 0.90, improvement 18%. models, outperforms PLSR CNN. model, which (SOTT), achieves RMSE 5.13 g kg−1, RPD 3.12. contributes 44.8% prediction, contributions TD THD indices 19.3% 20.2%, respectively. confirms great potential introducing semi-arid regions.

Язык: Английский

Процитировано

2