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Küçük Menderes River Basin (KMRB) is located in the western part of Turkey in the city of İzmir. The basin hosts about half a million people and 90% of the population makes their living from farming and animal husbandry. Most of the water needs for agricultural irrigation, animal husbandry, and drinking are met from groundwater resources in the basin. Since the water pumped from the aquifers cannot be naturally recharged by surface waters and precipitation, the groundwater level is rapidly receding and a decrease of up to 150 meters has been observed in the historical groundwater level time series since the 1990s. For this reason, land subsidence occurred in the basin and as a result of this, crustal surface cracks have formed and infrastructure and superstructure facilities have been endangered. To prevent loss of life and property that may occur in the future, it has become necessary to estimate the magnitude and the expansion zone of the land subsidence by carrying out geodetic monitoring studies in the region. In this study, the analyzes performed by Interferometric Synthetic Aperture Radar-Small Baseline Subset (InSAR-SBAS) and Global Navigation Satellite System (GNSS) methods to monitor land subsidence in the KMRB are presented. For this purpose, monthly 74 SAR images from 2017 to the end of 2022 were evaluated according to the SBAS procedure in the GMTSAR academic analysis software. In order to, both validate the InSAR-SBAS results and convert the deformation rate in the Line of Sight (LOS) to the ellipsoid normal direction, 3 periods of GNSS campaign were performed between 2021 and 2023 in 5 different GNSS stations and evaluated with the GAMIT/GLOBK software. According to the analyses, the land subsidence rate of up to −165 mm/year has been estimated in the KMRB, especially in the district centers of Bayındır and Ödemiş. In addition, unlike many studies, different rates and propagation of subsidence were determined in different parts of the basin depending on the aquifer type and precipitation. Monthly time series of the deformations were created and the behavior of the deformations and its relationship with the groundwater withdrawal and the lithological structure in the region was scrutinized. Thus, the spatio-temporal behaviour of the land subsidence and the associated geological factors were revealed. [ABSTRACT FROM AUTHOR]

• Explore the connection between D-InSAR technology and mining subsidence models. • Analyzed the accuracy of D-InSAR technology in inverting model parameters. • Revise the parameter in the PIM prediction model using the Boltzmann function. • Propose a model by combining the revised model with a time function. The prediction of surface movement and deformation in the mining area is of great significance to the production of the mining. Since the differential interferometry synthetic aperture radar measurement (D-InSAR) technology cannot predict the deformation caused by mining face, and the existing probability integral method (PIM) prediction model can only predict final mining deformation under critical extraction. Therefore, on the basis of PIM prediction model, the simplified Boltzmann function and Gompertz time function were introduced to form the BPIM dynamic prediction model for the analysis of temporal and spatial characteristics of the mining area. The parameters of BPIM model are inversed by D-InSAR monitoring data combined with optimization algorithm, and the mining of the working face is followed by the dynamic forward prediction of the changes in temporal and spatial characteristics. In addition, we analyzed the sensitivity of the main parameters in the model to the predicted results and the variation relationship of the model subsidence coefficient with the mining of the working face. When the model was applied to the working face of Guobei coal mine, the RMSE of the BPIM dynamic prediction model is 25.1 mm. Constructed with the previous dynamic prediction model established by conventional methods, its deformation accuracy under the prediction of subcritical extraction on the surface of the mining area is improved by 58.7%. The results demonstrated that the BPIM dynamic model effectively avoids the problem of large prediction results under subcritical extraction, and can provide some suggestions for the planning and production of the mining area. [ABSTRACT FROM AUTHOR]

Spatially explicit measurement of Above Ground Biomass (AGB) is crucial for the quantification of forest carbon stock and fluxes. To achieve this, an integration of Optical and Synthetic Aperture Radar (SAR) satellite datasets could provide an accurate estimation of forest biomass. This will also help in removing the uncertainties associated with the single sensor-based estimation approaches. Therefore, the present study attempts to integrate Sentinel-2 optical data with Sentinel-1 SAR dataset to estimate AGB in the Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. In this study, two non-parametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions—linear, sigmoidal, radial and polynomial and Random Forest (RF) were employed for the prediction of AGB using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA). Ground based AGB was estimated through allometric equation at 35 sampling sites with the help of tree height and Diameter at Breast's Height (DBH). Standalone collinearity analysis among different parameters resulted in poor correlation of AGB with VH (r = 0.05) and IA (r = 0.015), whereas a significantly good correlation with NDVI (r = 0.80) and VV (r = 0.74) were observed. Inclusion of NDVI with VV and VH together also resulted in a better correlation (r = 0.85) than other combinations. The SVM with linear kernel utilizing parametric the combinations of VV + VH + NDVI and VV + VH + NDVI + IA were found to be best performing on the basis of evaluation metrics. The outcome of this study highlighted the significance of machine learning techniques and synergistic use of different remote sensing data for an improved AGB quantification in tropical forests. [ABSTRACT FROM AUTHOR]

The main objective of our work was to investigate the impact of rain on wave observations from C-band (~5.3 GHz) synthetic aperture radar (SAR) in tropical cyclones. In this study, 10 Sentinel-1 SAR images were available from the Satellite Hurricane Observation Campaign, which were taken under cyclonic conditions during the 2016 hurricane season. The third-generation wave model, known as Simulating WAves Nearshore (SWAN) (version 41.31), was used to simulate the wave fields corresponding to these Sentinel-1 SAR images. In addition, rainfall data from the Tropical Rainfall Measuring Mission satellite passing over the spatial coverage of the Sentinel-1 SAR images were collected. The simulated results were validated against significant wave heights (SWHs) from the Jason-2 altimeter and European Centre for Medium-Range Weather Forecasts data, revealing a root mean square error (RMSE) of ~0.5 m with a 0.25 scatter index. Winds retrieved from the VH-polarized Sentinel-1 SAR images using the Sentinel-1 Extra Wide-swath Mode Wind Speed Retrieval Model after Noise Removal were taken as prior information for wave retrieval. It was discovered that rain did indeed affect the SAR wave retrieval, as evidenced by the 3.21-m RMSE of SWHs between the SAR images and the SWAN model, which was obtained for the ~1000 match-ups with raindrops. The raindrops dampened the wave retrieval when the rain rate was < ~5 mm/hr; however, they enhanced wave retrieval for higher rain rates. It was also found that the portion of the rain-induced ring wave with a wave number > 0.05 rad/m (~125 m wavelength) was clearly observed in the SAR-derived wave spectra. [ABSTRACT FROM AUTHOR]

• Two new indices, IBUI opt and IBUI sar proposed for mapping impervious features. • IBUI opt uses Green, Red, NIR and SWIR spectral band information. • IBUI sar uses SAR L-band HH and HV polarization information. • IBUI sar outperformed in semi-urban and rural areas. • IBUI opt performed better in high dense urban scenario. In order to cope up with the emerging demand of interpreting and detecting urban features from remotely sensed imagery, this study proposes an innovative unique yet practically easy approach to extract impervious surfaces and features using certain built-up indices (BUIs) that are developed from space-borne optical and SAR remotely sensed sensors. Resourcesat-2 LISS III and HH/HV dual polarized L-band ALOS PALSAR data are respectively the space-borne optical and synthetic aperture radar (SAR) data used for designing the BUIs used for impervious feature detection. BUI generated from optical sensor uses green (G), red (R), near infrared (NIR) and short wave infrared (SWIR) channels simultaneously. BUI designed from SAR sensor uses horizontal-horizontal (HH) co-polarized and horizontal-vertical (HV) cross-polarized L-band backscatter coefficient/sigma nought (σ°) data. The feature extraction performed by two new indices, one derived from existing indices using spectral-based information from optical sensors– an Impervious Built-up Index (IBUI opt); and the other from SAR HH/HV σ° backscatter information– an Impervious Built-up Index (IBUI sar); are cross-checked and validated with Ground Truth (GT) data that revealed the performance of the indices designed and proposed in the study for three sample sites bearing completely different biophysical environments; for easy and rapid extraction of impervious land features in satellite imageries. Overall accuracies of impervious feature extraction using IBUI opt are 92.3%, 95% and 75.6% in comparison to 90%, 98% and 87.6% using IBUI sar for the three study areas respectively. The approach adopted in this study is henceforth acceptable due to its high accuracy, simplicity and reliability; and hence easy to replicate. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we used the available algorithm for soil moisture estimation over LOPEX05 (the Loess Plateau land surface process Experiment (2005)) area. The available algorithm used ENVISAT/ASAR AP mode VV polarization observational data at a low incidence angle and ground measured soil moistures. The ground measurements were performed in the summer of the 2005 during the LOPEX05 field campaign. The validated results indicate that an average difference between the soil moistures estimated from the microwave remote sensing and ground measurements is less than 0.02cm3/cm3, with a RMS error of 2.0%, and a maximum less than 0.04cm3/cm3. The algorithm was applied to the surface soil moisture mapping later. The results show that this algorithm is suitable for monitoring soil moisture information of the agricultural fields over the Chinese Loess Plateau, when ground land cover situation and the resolution of imagery data are taken into account. However, we also find that there are large differences over the steep slope region, the edge of mesa. The results are not acceptable for surface soil moisture estimation in these regions. Thus, the surface soil moisture retrieval in the steep slope region of the Loess Plateau need to be further investigated in the future. [Copyright &y& Elsevier]

Abstract: Bistatic SAR (BSAR) systems have recently been the subject of several studies, but little attention has been given to the potential of the location of targets. In the paper, the performance of the estimation of 3-D position of a target (TPE) in parasitic SAR is obtained analytically and illustrated by computer simulation using ambiguity function analysis, and the maximum likelihood estimate (MLE) approach. It was shown that by using a multi look parasitic BSAR all three coordinates of an isolated point target (IPT) could be evaluated. Analytical closed form equations that characterize the measurement accuracy were derived. In the final these equations will be used to demonstrate various cases of more practical, including optimal trajectories choice under any geometry configuration case, moreover, the conclusion is verified via Matlab. [Copyright &y& Elsevier]