Optimization of Multi-Source Remote Sensing Soil Salinity Estimation Based on Different Salinization Degrees

Remote Sensing, Год журнала: 2025, Номер 17(7), С. 1315 - 1315

Опубликована: Апрель 7, 2025

The timely and accurate monitoring of regional soil salinity is crucial for the sustainable development land stability ecological environment in arid semi-arid regions. However, due to spatiotemporal heterogeneity properties environmental conditions, improving accuracy salinization remains challenging. This study aimed explore whether partitioned modeling based on degrees during both bare vegetation cover periods can enhance prediction. Specifically, this integrated situ hyperspectral data satellite multispectral using spectral response functions. Subsequently, machine learning methods such as random forest (RF), extreme gradient boosting (XGBoost), support vector (SVM), multiple linear regression (MLR) were employed, combination with sensitive indices, develop a multi-source remote sensing estimation model optimized different (mild or lower vs. moderate higher salinization). performance approach was then compared an overall that does not distinguish between determine optimal strategy. results highlight effectiveness considering enhancing sensitivity indices accuracy. Classifying helps identify variable combinations are more construction content (SSC) models, positively impacting estimation. strategy outperformed stability, R2 values reaching 0.84 0.80 corresponding RMSE 0.1646% 0.1710% periods, respectively. proposes degrees, providing scientific evidence technical precise assessment effective management salinization.

Язык: Английский

---

Artificial Intelligence and IoT for Water Saving in Agriculture: A Systematic Review

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 101008 - 101008

Опубликована: Май 1, 2025

Язык: Английский

---

A novel framework for multi-layer soil moisture estimation with high spatio-temporal resolution based on data fusion and automated machine learning

Agricultural Water Management, Год журнала: 2024, Номер 306, С. 109173 - 109173

Опубликована: Ноя. 22, 2024

Язык: Английский

---

Mapping Soil Cadmium Content Using Multi-Spectral Satellite Images and Multiple-Residual-Stacking Model: Incorporating Information from Homologous Pollution and Spectrally Active Materials

Journal of Hazardous Materials, Год журнала: 2024, Номер 485, С. 136755 - 136755

Опубликована: Дек. 6, 2024

Язык: Английский

---