Deep Learning
Reyhaneh Bagheri; Fatemeh Tabib Mahmoudi; AmirHossein Gholamian
Abstract
Background and Objectives: Accurate soil moisture estimation is essential for various hydrological processes such as irrigation planning, and environmental monitoring; however, prediction accuracy is often limited by sparse in-situ measurements and uncertainties in remote sensing products. This study ...
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Background and Objectives: Accurate soil moisture estimation is essential for various hydrological processes such as irrigation planning, and environmental monitoring; however, prediction accuracy is often limited by sparse in-situ measurements and uncertainties in remote sensing products. This study aims to develop an enhanced soil moisture prediction framework by integrating a Gated Recurrent Unit (GRU) deep learning model with multi source data augmentation techniques in order to evaluate its performance across diverse climatic conditions. Methods: This study proposed an enhanced GRU deep learning framework supported by multi source data augmentation to predict soil moisture across ten U.S. Climate Reference Network (USCRN) stations representing diverse climatic and ecological conditions. The proposed model integrates Conv1D layers, bidirectional GRUs, multi-head attention, and dense layers to capture short and long-term temporal dependencies while fusing multi source inputs including ISMN in-situ measurements, SMAP products, and GLDAS. Data augmentation strategies composed of noise injection, temporal warping, scaling, and window slicing were applied to expand the training dataset and reduce overfitting. Model performance was compared against a standard GRU using some of the evaluation metrics. Results: Results demonstrate that the augmented GRU model consistently outperformed the standard GRU across all stations, with notable improvements in R² (up to 0.912), RMSE, and MAE. Performance gains were particularly evident in humid continental and Mediterranean climates, while regions with complex forested or semi-arid environments also benefited from data augmentation. These improvements confirm that data augmentation enhances the model’s generalization under climatic variability and mitigates limitations associated with SMAP resolution and GLDAS uncertainty.Conclusion: The integration of multi-source datasets with an augmented GRU architecture provides a reliable framework for soil moisture estimation across diverse environments. The proposed approach offers strong potential for applications in environmental monitoring, precision agriculture, and water resource management.
Artificial Intelligence
H. Karim Tabbahfar; F. Tabib Mahmoudi
Abstract
Background and Objectives: Considering the drought and global warming, it is very important to monitor changes in water bodies for surface water management and preserve water resources in the natural ecosystem. For this purpose, using the appropriate spectral indices has high capabilities to distinguish ...
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Background and Objectives: Considering the drought and global warming, it is very important to monitor changes in water bodies for surface water management and preserve water resources in the natural ecosystem. For this purpose, using the appropriate spectral indices has high capabilities to distinguish surface water bodies from other land covers. This research has a special consideration to the effect of different types of land covers around water bodies. For this reason, two different water bodies, lake and wetland, have been used to evaluate the implementation results.Methods: The main objective of this research is to evaluate the capabilities of the genetic algorithm in optimum selection of the spectral indices extracted from Sentinel-2 satellite image due to distinguish surface water bodies in two case studies: 1) the pure water behind the Karkheh dam and 2) the Shadegan wetland having water mixed with vegetation. In this regard, the set of optimal indices is obtained with the genetic algorithm followed by the support vector machine (SVM) classifier. Results: The evaluation of the classification results based on the optimum selected spectral indices showed that the overall accuracy and Kappa coefficient of the recognized surface water bodies are 98.18 and 0.9827 in the Karkheh dam and 98.04 and 0.93 in Shadegan wetland, respectively. Evaluation of each of the spectral indices measured in both study areas was carried out using quantitative decision tree (DT) classifier. The best obtained DT classification results show the improvements in overall accuracy by 1.42% in the Karkheh Dam area and 1.56% in the Shadegan Wetland area based on the optimum selected indices by genetic algorithm followed by SVM classifier. Moreover, the obtained classification results are superior compared with Random Forest classifier using the optimized set of spectral features.Conclusion: Applying the genetic algorithm on the spectral indices was able to obtain two optimal sets of effective indices that have the highest amount of accuracy in classifying water bodies from other land cover objects in the study areas. Considering the collective performance, genetic algorithm selects an optimal set of indices that can detect water bodies more accurately than any single index.
Data Preprocessing
F. Tabib Mahmoudi; A. Karami
Abstract
Background and Objectives: Pan-sharpening algorithms integrate the spectral capabilities of the multispectral imagery with the spatial details of the panchromatic one to obtain a product with confident spectral and spatial resolutions. Due to the large diversities in the utilized pan-sharpening algorithms, ...
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Background and Objectives: Pan-sharpening algorithms integrate the spectral capabilities of the multispectral imagery with the spatial details of the panchromatic one to obtain a product with confident spectral and spatial resolutions. Due to the large diversities in the utilized pan-sharpening algorithms, occurring spatial and spectral deviations in their results should be recognized by performing the quantitative assessment analysis.Methods: In this research, the pan-sharpened images from PCA, IHS, and Gram-Schmidt transformation based algorithms are evaluated for the multi-spectral and panchromatic images fusion of Landsat-8 OLI sensor (medium scale resolution satellite) and WorldView-2 (high-resolution satellite). Quantitative analysis is performed on the pan-sharpened products based on the Per-Pixel Deviation (PPD) measure for spectral deviation analysis and high-pass filter and edge extraction measures for analyzing the spatial correlations. Moreover, entropy and standard deviation quantitative evaluation measures are also utilized based on the pan-sharpened image content.Results: Quantitative analysis represents that increasing the spatial resolution of the utilized remote sensing data has direct impacts on the spectral, spatial, and content-based characteristics of the generated Pan-sharpened products. Gram-Schmidt transformation based pan-sharpening method has the least spectral deviations in both WorldView-2 and Landsat-8 satellite images. But, the amount of spectral, spatial and content-based quantitative measures of PCA and IHS are changing with various spatial resolutions.Conclusion: it can be said that Gram-Schmidt pan-sharpening method has the best performance in both medium-scale and high-resolution data sets based on the spectral, spatial, and content quantitative evaluation results. The IHS pan-sharpening method has better performance than the PCA method in Landsat-8 OLI data. But, by increasing the spatial resolution of the data, PCA generates pan-sharpened products with better spectral, spatial, and content based quantitative evaluation results.
