Search Results (660)

Realistic representation of microwave backscattering from vegetated surfaces is important for developing accurate soil moisture retrieval algorithms that use synthetic aperture radar (SAR) imagery. Many studies have reported considerable discrepancies between the simulated and observed backscatter. However, there has been limited effort to identify the sources of errors and contributions quantitatively using process-based backscatter simulation in comparison with extensive ground observations. This study examined the influence of input uncertainties on simulated backscatter from a first-order radiative transfer model, named the Wheat Canopy Scattering Model (WCSM), using ground-based and airborne data collected during the SMAPVEX12 campaign. Input uncertainties to WCSM were simulated using error statistics for two crop growth stages. The Sobol’ method was adopted to analyze the uncertainty in WCSM-simulated backscatters originating from different inputs before and after the wheat ear emergence. The results show that despite the presence of wheat ears, uncertainty in root mean square (RMS) height of 0.2 cm significantly influences simulated co-polarized backscatter uncertainty. After ear emergence, uncertainty in ears dominates simulated cross-polarized backscatter uncertainty. In contrast, uncertainty in RMS height before ear emergence dominates the accuracy of simulated cross-polarized backscatter. These findings suggest that considering wheat ears in the model structure and precise representation of surface roughness is essential to accurately simulate backscatter from a wheat field. Since the discrepancy between the simulated and observed backscatter coefficients is due to both model and observation uncertainty, the uncertainty of the UAVSAR data was estimated by analyzing the scatter between multiple backscatter coefficients obtained from the same targets near-simultaneously, assuming the scatter represents the observation uncertainty. Observation uncertainty of UAVSAR backscatter for HH, VV, and HV polarizations are 0.8 dB, 0.87 dB, and 0.86 dB, respectively. Discrepancies between WCSM-simulated backscatter and UAVSAR observations are discussed in terms of simulation and observation uncertainty. Full article

Food sustainability has become a major global concern in recent years. Multiple complimentary strategies to deal with this issue have been developed; one of these approaches is regenerative farming. The identification and analysis of crop type phenology are required to achieve sustainable regenerative faming. Earth Observation (EO) data have been widely applied to crop type identification using supervised Machine Learning (ML) and Deep Learning (DL) classifications, but these methods commonly rely on large amounts of ground truth data, which usually prevent historical analysis and may be impractical in very remote, very extensive or politically unstable regions. Thus, the development of a robust but intelligent unsupervised classification model is attractive for the long-term and sustainable prediction of agricultural yields. Here, we propose FastDTW-HC, a combination of Fast Dynamic Time Warping (DTW) and Hierarchical Clustering (HC), as a significantly improved method that requires no ground truth input for the classification of winter food crop varieties of barley, wheat and rapeseed, in Norfolk, UK. A series of variables is first derived from the EO products, and these include spectral indices from Sentinel-2 multispectral data and backscattered amplitude values at dual polarisations from Sentinel-1 Synthetic Aperture Radar (SAR) data. Then, the phenological patterns of winter barley, winter wheat and winter rapeseed are analysed using the FastDTW-HC applied to the time-series created for each variable, between Nov 2019 and June 2020. Future research will extend this winter food crop mapping analysis using FastDTW-HC modelling to a regional scale. Full article

Due to the limited availability of high-quality optical images during the rice growth period in the Lingnan region of China, effectively monitoring the rice planting situation has been a challenge. In this study, we utilized multi-temporal Sentinel-1 data to develop a method for rapidly extracting the range of rice fields using a threshold segmentation approach and employed a U-Net deep learning model to delineate the distribution of rice fields. Spatio-temporal changes in rice distribution in Leizhou City, Guangdong Province, China, from 2017 to 2021 were analyzed. The results revealed that by analyzing SAR-intensive time series data, we were able to determine the backscattering coefficient of typical crops in Leizhou and use the threshold segmentation method to identify rice labels in SAR-intensive time series images. Furthermore, we extracted the distribution range of early and late rice in Leizhou City from 2017 to 2021 using a U-Net model with a minimum relative error accuracy of 3.56%. Our analysis indicated an increasing trend in both overall rice planting area and early rice planting area, accounting for 44.74% of early rice and over 50% of late rice planting area in 2021. Double-cropping rice cultivation was predominantly concentrated in the Nandu River basin, while single-cropping areas were primarily distributed along rivers and irrigation facilities. Examination of the traditional double-cropping areas in Fucheng Town from 2017 to 2021 demonstrated that over 86.94% had at least one instance of double cropping while more than 74% had at least four instances, which suggested that there is high continuity and stability within the pattern of rice cultivation practices observed throughout Leizhou City. Full article

Using microwave remote sensing to invert forest parameters requires clear canopy scattering characteristics, which can be intuitively investigated through scattering measurements. However, there are very few ground-based measurements on forest branches, needles, and canopies. In this study, a quantitative analysis of the canopy branches, needles, and ground contribution of Masson pine scenes in C-, X-, and Ku-bands was conducted based on a microwave anechoic chamber measurement platform. Four canopy scenes with different densities by defoliation in the vertical direction were constructed, and the backscattering data for each scene were collected in the C-, X-, and Ku-bands across eight incidence angles and eight azimuth angles, respectively. The results show that in the vertical observation direction, the backscattering energy of the C- and X-bands was predominantly contributed by the ground, whereas the Ku-band signal exhibited higher sensitivity to the canopy structure. The backscattering energy of the scene was influenced by the incident angle, particularly in the cross-polarization, where backscattering energy increased with larger incident angles. The scene’s backscattering energy was influenced by the scattering and extinction of canopy branches and needles, as well as by ground scattering, resulting in a complex relationship with canopy density. In addition, applying orientation correction to the polarization scattering matrix can mitigate the impact of the incident angle and reduce the decomposition energy errors in the Freeman–Durden model. In order to ensure the reliability of forest parameter inversion based on SAR data, a greater emphasis should be placed on physical models that account for signal scattering and the extinction process, rather than relying on empirical models. Full article

Tree species are important factors affecting the carbon sequestration capacity of forests and maintaining the stability of ecosystems, but trees are widely distributed spatially and located in complex environments, and there is a lack of large-scale regional tree species classification models for remote sensing imagery. Therefore, many studies aim to solve this problem by combining multivariate remote sensing data and proposing a machine learning model for forest tree species classification. However, satellite-based laser systems find it difficult to meet the needs of regional forest species classification characters, due to their unique footprint sampling method, and SAR data limit the accuracy of species classification, due to the problem of information blending in backscatter coefficients. In this work, we combined Sentinel-1 and Sentinel-2 data to construct a machine learning tree classification model based on optical features, vegetation spectral features, and PolSAR polarization observation features, and propose a forest tree classification feature selection method featuring the Hilbert–Huang transform for the problem of mixed information on the surface of SAR data. The PSO-RF method was used to classify forest species, including four temperate broadleaf forests, namely, aspen (Populus L.), maple (Acer), peach tree (Prunus persica), and apricot tree (Prunus armeniaca L.), and two coniferous forests, namely, Chinese pine (Pinus tabuliformis Carrière) and Mongolian pine (Pinus sylvestris var. mongolica Litv.). In this study, some experiments were conducted using two Sentinel-1 images, four Sentinel-2 images, and 550 measured forest survey sample data points pertaining to the forested area of Fuxin District, Liaoning Province, China. The results show that the fusion model constructed in this study has high accuracy, with a Kappa coefficient of 0.94 and an overall classification accuracy of 95.1%. In addition, this study shows that PolSAR data can play an important role in forest tree species classification. In addition, by applying the Hilbert–Huang transform to PolSAR data, other feature information that interferes with the perceived vertical structure of forests can be suppressed to a certain extent, and its role in the classification of forest species, combined with PolSAR, should not be ignored. Full article

Soil moisture is a key variable that affects ecosystem carbon and water cycles and that can directly affect climate change. Remote sensing is the best way to obtain global soil moisture data. Currently, soil moisture remote sensing products have coarse spatial resolution, which limits their application in agriculture, the ecological environment, and urban planning. Soil moisture downscaling methods rely mainly on optical data. Affected by weather, the spatial discontinuity of optical data has a greater impact on the downscaling results. The synthetic aperture radar (SAR) backscatter coefficient is strongly correlated with soil moisture. This study was based on the Google Earth Engine (GEE) platform, which integrated Moderate-Resolution Imaging Spectroradiometer (MODIS) optical and SAR backscattering coefficients and used machine learning methods to downscale the soil moisture product, reducing the original soil moisture with a resolution of 10 km to 1 km and 100 m. The downscaling results were verified using in situ observation data from the Shandian River and Wudaoliang. The results show that in the two study areas, the downscaling results after adding SAR backscattering coefficients are better than before. In the Shandian River, the R increases from 0.28 to 0.42. In Wudaoliang, the R value increases from 0.54 to 0.70. The RMSE value is 0.03 (cm³/cm³). The downscaled soil moisture products play an important role in water resource management, natural disaster monitoring, ecological and environmental protection, and other fields. In the monitoring and management of natural disasters, such as droughts and floods, it can provide key information support for decision-makers and help formulate more effective emergency response plans. During droughts, affected areas can be identified in a timely manner, and the allocation and scheduling of water resources can be optimized, thereby reducing agricultural losses. Full article

Accurate and reliable knowledge about grassland distribution is essential for farmers, stakeholders, and government to effectively manage grassland resources from agro-economical and ecological perspectives. This study developed a novel pixel-based grassland classification approach using three supervised machine learning (ML) algorithms, which were assessed in the province of Manitoba, Canada. The grassland classification process involved three stages: (1) to distinguish between vegetation and non-vegetation covers, (2) to differentiate grassland from non-grassland landscapes, and (3) to identify three specific grassland classes (tame, native, and mixed grasses). Initially, this study investigated different satellite data, such as Sentinel-1 (S1), Sentinel-2 (S2), and Landsat 8 and 9, individually and combined, using the random forest (RF) method, with the best performance at the first two steps achieved using a combination of S1 and S2. The combination was then utilized to conduct the first two steps of classification using support vector machine (SVM) and gradient tree boosting (GTB). In step 3, after filtering out non-grassland pixels, the performance of RF, SVM, and GTB classifiers was evaluated with combined S1 and S2 data to distinguish different grassland types. Eighty-nine multitemporal raster-based variables, including spectral bands, SAR backscatters, and digital elevation models (DEM), were input for ML models. RF had the highest classification accuracy at 69.96% overall accuracy (OA) and a Kappa value of 0.55. After feature selection, the variables were reduced to 61, increasing OA to 72.62% with a Kappa value of 0.58. GTB ranked second, with its OA and Kappa values improving from 67.69% and 0.50 to 72.18% and 0.58 after feature selection. The impact of raster data quality on grassland classification accuracy was assessed through multisensor image fusion. Grassland classification using the Hue, Saturation, and Value (HSV) fused images showed higher OA (59.18%) and Kappa values (0.36) than the Brovey Transform (BT) and non-fused images. Finally, a web map was created to show grassland results within the Soil Landscapes of Canada (SLC) polygons, relating soil landscapes to grassland distribution and providing valuable information for decision-makers and researchers. Future work may include extending the current methodology by considering other influential variables, like meteorological parameters or soil properties, to create a comprehensive grassland inventory across the whole Prairie ecozone of Canada. Full article

High-spatiotemporal-resolution and accurate soil moisture (SM) data are crucial for investigating climate, hydrology, and agriculture. Existing SM products do not yet meet the demands for high spatiotemporal resolution. The objective is to develop and evaluate a retrieval framework to derive SM estimates with high spatial (100 m) and temporal (<3 days) resolution that can be used on a national scale in China. Therefore, this study integrates multi-source data, including optical remote sensing (RS) data from Sentinel-2 and Landsat-7/8/9, synthetic aperture radar (SAR) data from Sentinel-1, and auxiliary data. Four machine learning and deep learning algorithms are applied, including Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and Ensemble Learning (EL). The integrated framework (IF) considers three feature scenarios (SC1: optical RS + auxiliary data, SC2: SAR + auxiliary data, SC3: optical RS + SAR + auxiliary data), encompassing a total of 33 features. The results are as follows: (1) The correlation coefficients (r) between auxiliary data (such as sand fraction, r = −0.48; silt fraction, r = 0.47; and evapotranspiration, r = −0.42), SAR features (such as the backscatter coefficients for VV-pol (${\sigma }_{vv}^0$), r = 0.47), and optical RS features (such as Shortwave Infrared Band 2 (SWIR2) reflectance data from Sentinel-2 and Landsat-7/8/9, r = −0.39) with observed SM are significant. This indicates that multi-source data can provide complementary information for SM monitoring. (2) Compared to XGBoost and LSTM, RFR and EL demonstrate superior overall performance and are the preferred models for SM prediction. Their R² for the training and test sets exceed 0.969 and 0.743, respectively, and their ubRMSE are below 0.022 and 0.063 m³/m³, respectively. (3) The SM prediction accuracy is highest for the scenario of optical + SAR + auxiliary data, followed by SAR + auxiliary data, and finally optical + auxiliary data. (4) With an increasing Normalized Difference Vegetation Index (NDVI) and SM values, the trained models exhibit a general decrease in prediction performance and accuracy. (5) In 2021 and 2022, without considering cloud cover, the IF theoretically achieved an SM revisit time of 1–3 days across 95.01% and 96.53% of China’s area, respectively. However, SC1 was able to achieve a revisit time of 1–3 days over 60.73% of China’s area in 2021 and 69.36% in 2022, while the area covered by SC2 and SC3 at this revisit time accounted for less than 1% of China’s total area. This study validates the effectiveness of combining multi-source RS data with auxiliary data in large-scale SM monitoring and provides new methods for improving SM retrieval accuracy and spatiotemporal coverage. Full article

This study examines the combined use of ground penetrating radar (GPR) and Sentinel-1 synthetic aperture radar (SAR) data for estimating soil moisture in a 25-decare field in Sivas, Türkiye. Soil moisture, vital for sustainable agriculture and ecosystem management, was assessed using in situ measurements, SAR backscatter analysis, and GPR-derived dielectric constants. A novel empirical model adapted from the classical soil moisture index (SSM) was developed for Sentinel-1, while GPR data were processed using the reflected wave method for estimating moisture at 0–10 cm depth. GPR demonstrated a stronger correlation within situ measurements (R² = 74%) than Sentinel-1 (R² = 32%), reflecting its ability to detect localized moisture variations. Sentinel-1 provided broader trends, revealing its utility for large-scale analysis. Combining these techniques overcame individual limitations, offering detailed spatial insights and actionable data for precision agriculture and water management. This integrated approach highlights the complementary strengths of GPR and SAR, enabling accurate soil moisture mapping in heterogeneous conditions. The findings emphasize the value of multi-technique methods for addressing challenges in sustainable resource management, improving irrigation strategies, and mitigating climate impacts. Full article

The increasing demand for large-scale, high-frequency environmental monitoring has driven the adoption of satellite-based technologies for effective forest management, especially in the context of climate change. This study explores the potential of SAR for estimating the mass of harvesting residues, a significant component of forest ecosystems that impacts nutrient cycling, fire risk, and bioenergy production. The research hypothesizes that while the spatial distribution of residues remains stable, changes in moisture content—reflected in variations in the dielectric properties of the woody material—can be detected by SAR techniques. Two models, the generalized linear model (GLM) and random forest (RF) model, were used to predict the mass of residues using interferometric variables (phase, amplitude, and coherence) as well as the backscatter signal from several acquisition pairs. The models provided encouraging results (R² of 0.48 for GLM and 0.13 for RF), with an acceptable bias and RMSE. It was concluded that it is possible to derive useful indications about the mass of harvesting residues from SAR data and the findings could lead to the improved monitoring and management of forest residues, contributing to sustainable forestry practices and the enhanced utilization of bioenergy resources. Full article

As a polarimetric synthetic aperture radar (SAR) mode capable of simultaneously acquiring abundant surface information and conducting large-width observations, compact polarimetric synthetic aperture radar (CP SAR) holds great promise for mangrove dynamics monitoring. Nevertheless, there have been no studies on mangrove identification using CP SAR. This study aims to explore the potential of C-band CP SAR for mangrove monitoring applications, with the objective of identifying the most effective CP SAR descriptors for mangrove discrimination. A systematic comparison of 52 well-known CP features is provided, utilizing CP SAR data derived from the reconstruction of C-band Gaofen-3 quad-polarimetric data. Among all the features, Shannon entropy (SE), a random polarimetric constituent (V_B), Shannon entropy (SE_I), and the Bragg backscattering constituent (V_G) exhibited the best performance. By combining these four features, we designed three supervised classifiers—support vector machine (SVM), maximum likelihood (ML), and artificial neural network (ANN)—for comparative analysis experiments. The results demonstrated that the optimal polarimetric feature combination not only reduced the redundancy of polarimetric feature data but also enhanced overall accuracy. The highest accuracy of mangrove extraction reached 98.04%. Among the three classifiers, SVM outperformed the other classifiers in mangrove extraction, while ML achieved the highest overall classification accuracy. Full article

The LuTan-1 (LT-1) satellite, launched in 2022, is China’s first L-band full-polarimetric Synthetic Aperture Radar (SAR) constellation, boasting interferometry capabilities. However, given its limited use in subsidence monitoring to date, a comprehensive evaluation of LT-1’s interferometric quality and capabilities is necessary. In this study, we utilized the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique to analyze mining-induced subsidence results near Shenmu City (China) with LT-1 data, revealing nine subsidence areas with a maximum subsidence of −19.6 mm within 32 days. Furthermore, a comparative analysis between LT-1 and Sentinel-1 data was conducted focusing on the aspects of subsidence results, interferometric phase, scattering intensity, and interferometric coherence. Notably, LT-1 detected some subsidence areas larger than those identified by Sentinel-1, attributed to LT-1’s high resolution, which significantly enhances the detectability of deformation gradients. Additionally, the coherence of LT-1 data exceeded that of Sentinel-1 due to LT-1’s L-band long wavelength compared to Sentinel-1’s C-band. This higher coherence facilitated more accurate capturing of differential interferometric phases, particularly in areas with large-gradient subsidence. Moreover, the quality of LT-1’s monitoring results surpassed that of Sentinel-1 in root mean square error (RMSE), standard deviation (SD), and signal-to-noise ratio (SNR). In conclusion, these findings provide valuable insights for future subsidence-monitoring tasks utilizing LT-1 data. Ultimately, the systematic differences between LT-1 and Sentinel-1 satellites confirm that LT-1 is well-suited for detailed and accurate subsidence monitoring in complex environments. Full article

Predicting crop yield throughout its development cycle is crucial for planning storage, processing, and distribution. Optical remote sensing has been used for yield prediction but has limitations, such as cloud interference and only capturing canopy-level data. Synthetic Aperture Radar (SAR) complements optical data by capturing information even in cloudy conditions and providing additional plant insights. This study aimed to explore the correlation of SAR variables with soybean yield at different crop stages, testing if SAR data enhances predictions compared to optical data alone. Data from three growing seasons were collected from an area of 106 hectares, using eight SAR variables (Alpha, Entropy, DPSVI, RFDI, Pol, RVI, VH, and VV) and four speckle noise filters. The Random Forest algorithm was applied, combining SAR variables with the EVI optical index. Although none of the SAR variables showed strong correlations with yield (r < |0.35|), predictions improved when SAR data were included. The best performance was achieved using DPSVI with the Boxcar filter, combined with EVI during the maturation stage (with EVI:RMSE = 0.43, 0.49, and 0.60, respectively, for each season; while EVI + DPSVI:RMSE = 0.39, 0.49, and 0.42). Despite improving predictions, the computational demands of SAR processing must be considered, especially when optical data are limited due to cloud cover. Full article

Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood. Full article

Forest degradation is a major issue in ecosystem monitoring, and to take reformative measures, it is important to detect, map, and quantify the losses of forests. Synthetic Aperture Radar (SAR) time-series data have the potential to detect forest loss. However, its sensitivity is influenced by the ecoregion, forest type, and site conditions. In this work, we assessed the accuracy of open-source C-band time-series data from Sentinel-1 SAR for detecting deforestation across forests in Africa, South Asia, and Southeast Asia. The statistical Cumulative Sums of Change (CuSUM) algorithm was applied to determine the point of change in the time-series data. The algorithm’s robustness was assessed for different forest site conditions, SAR polarizations, resolutions, and under varying moisture conditions. We observed that the change detection algorithm was affected by the site- and forest-management activities, and also by the precipitation. The forest type and eco-region affected the detection performance, which varied for the co- and cross-pol backscattering components. The cross-pol channel showed better deforested region delineation with less spurious detection. The results for Kalimantan showed a better accuracy at a 100 m spatial resolution, with a 25.1% increase in the average Kappa coefficient for the VH polarization channel in comparison with a 25 m spatial resolution. To avoid false detection due to the high impact of soil moisture in the case of Haldwani, a seasonal analysis was carried out based on dry and wet seasons. For the seasonal analysis, the cross-pol channel showed good accuracy, with an average Kappa coefficient of 0.85 at the 25 m spatial resolution. This work was carried out in support of the upcoming NISAR mission. The datasets were repackaged to the NISAR-like HDF5 format and processing was carried out with methods similar to NISAR ATBDs. Full article