Active fires are devastating natural disasters that cause socio-economical damage across the globe. The detection and mapping of these disasters require efficient tools, scientific methods, and reliable observations. Satellite images have been widely used for active fire detection (AFD) during the past years due to their nearly global coverage. However, accurate AFD and mapping in satellite imagery is still a challenging task in the remote sensing community, which mainly uses traditional methods. Deep learning (DL) methods have recently yielded outstanding results in remote sensing applications. Nevertheless, less attention has been given to them for AFD in satellite imagery. This study presented a deep convolutional neural network (CNN) “MultiScale-Net” for AFD in Landsat-8 datasets at the pixel level. The proposed network had two main characteristics: (1) several convolution kernels with multiple sizes, and (2) dilated convolution layers (DCLs) with various dilation rates. Moreover, this paper suggested an innovative Active Fire Index (AFI) for AFD. AFI was added to the network inputs consisting of the SWIR2, SWIR1, and Blue bands to improve the performance of the MultiScale-Net. In an ablation analysis, three different scenarios were designed for multi-size kernels, dilation rates, and input variables individually, resulting in 27 distinct models. The quantitative results indicated that the model with AFI-SWIR2-SWIR1-Blue as the input variables, using multiple kernels of sizes 3 × 3, 5 × 5, and 7 × 7 simultaneously, and a dilation rate of 2, achieved the highest F1-score and IoU of 91.62% and 84.54%, respectively. Stacking AFI with the three Landsat-8 bands led to fewer false negative (FN) pixels. Furthermore, our qualitative assessment revealed that these models could detect single fire pixels detached from the large fire zones by taking advantage of multi-size kernels. Overall, the MultiScale-Net met expectations in detecting fires of varying sizes and shapes over challenging test samples.

Soil salinity, a significant environmental indicator, is considered one of the leading causes of land degradation, especially in arid and semi-arid regions. In many cases, this major threat leads to loss of arable land, reduces crop productivity, groundwater resources loss, increases economic costs for soil management, and ultimately increases the probability of soil erosion. Monitoring soil salinity distribution and degree of salinity and mapping the electrical conductivity (EC) using remote sensing techniques are crucial for land use management. Salt-effected soil is a predominant phenomenon in the Eshtehard Salt Lake located in Alborz, Iran. In this study, the potential of Sentinel-2 imagery was investigated for mapping and monitoring soil salinity. According to the satellite's pass, different salt properties were measured for 197 soil samples in the field data study. Therefore several spectral features, such as satellite band reflectance, salinity indices, and vegetation indices, were extracted from Sentinel-2 imagery. To build an optimum machine learning regression model for soil salinity estimation, three different regression models, including Gradient Boost Machine (GBM), Extreme Gradient Boost (XGBoost), and Random Forest (RF), were used. The XGBoostmethod outperformed GBM and RF with the coefficient of determination (R²) more than 76%, Root Mean Square Error (RMSE) about 0.84 dS m⁻¹, and Normalized Root Mean Square Error (NRMSE) about 0.33 dS m⁻¹. The results demonstrated that the integration of remote sensing data, field data, and using an appropriate machine learning model could provide high-precision salinity maps to monitor soil salinity as an environmental problem.

<!-- *** Custom HTML *** --> Estimating volumetric soil moisture (M_v) and surface roughness (S) are the key parameters for numerous agricultural and hydrological applications. Although these two parameters can be effectively retrieved from synthetic aperture radar (SAR) data, the presence of vegetation can negatively affect the results. A method was proposed to accurately estimate M_v and S over vegetated agricultural areas. The method was based on applying the machine learning inversion approach along with SAR data to invert a combination of the parameterized water cloud model (PWCM) and the calibrated integral equation model (CIEM). The soil backscattered component in water cloud model (WCM) was generated by CIEM to be applied to the WCM parameterization and dataset simulation. Three machine learning algorithms, including the support vector regression (SVR), multi-output SVR (MSVR), and artificial neural network (ANN), were employed to model the relationship between the simulated dataset variables. The genetic algorithm was also applied to optimize the models’ parameters. The inversion technique results demonstrated that the MSVR and ANN had the highest accuracy in estimating M_v and S due to their better structures. The SMAPVEX-16 in situ dataset, along with three Sentinel-1 images, was applied to evaluate the accuracy of the WCM parameterization and the proposed method for M_v and S estimation. The accuracies of the PWCM in the VV and VH polarizations of Sentinel-1 C-band data were reasonable for VWC < 2.5 kg / m² [root-mean-square error (RMSE) = 1.44 and 1.77 dB, respectively]. Additionally, it was observed that the trained SVR, MSVR, and ANN had similar results for different VWC values. In summary, the proposed method had high potential in vegetated agricultural areas with VWC < 2.5 kg / m², for which the RMSEs were 4 to 7 vol. % and 0.35 to 0.46 cm depending on the VWC values in retrieving M_v and S, respectively.

Wetlands are valuable natural resources that provide many services to both the environment and humans. Over the past several decades, climatic change and human activities have had a considerable impact on the water level of wetlands. Zarivar Lake, located in the northwestern region of Iran, represents a significant ecological unit and aquatic ecosystem. In this study, from 2015 to 2022, the relationship between seasonal changes in Zarivar Lake’s waterbody (LWB) area and weather factors like precipitation, evapotranspiration, and the temperature of the lake’s surface water (LSWT) were examined. For this purpose, the Google Earth Engine (GEE) cloud platform, a powerful and fast tool for processing the time series of images, was used. The LWB was extracted by utilizing the average images of the dual-polarized SAR Sentinel-1 imagery for each season. Furthermore, meteorological parameters encompass the utilization of the Landsat-8 satellite’s thermal band to determine LSWT by using statistical mono-window (SMW), the CHIRPS rainfall model data for assessing precipitation levels, and the employment of MODIS evapotranspiration products in the form of 8-day data. The study revealed significant correlations between variations in Zarivar Lake’s waterbody area and meteorological factors. Correlation coefficients indicated a positive relationship between LWB area and precipitation during the winter (r = 0.67) and spring (r = 0.73), while weaker positive correlations were observed in the summer (r = 0.29) and fall (r = 0.30). Conversely, the LWB area showed a relative relationship with LSWT, with positive correlations in winter (r = 0.10) and spring (r = 0.26), and negative correlations in summer (r = −0.30) and fall (r = −0.07). Additionally, evapotranspiration parameters aligned with precipitation changes throughout the seasons, highlighting the significant influence of climate on Zarivar Lake.