Synthetic Aperture Radar Backscatter Research Articles

Spatiotemporal variations of wetland backscatter: The role of water depth and vegetation characteristics in Sentinel-1 dual-polarization SAR observations

Synthetic Aperture Radar (SAR) backscatter observations are sensitive to hydrologic conditions and vegetation characteristics of land cover. This study conducted a high spatial-resolution investigation (30-m) on the response of dual-polarization C-band (5.6 cm wavelength) SAR backscatter coefficients (σ°) to temporal changes of surface water depth (dw) and spatial variations of vegetation characteristics in the south Florida Everglades wetlands. We investigated (1) linear relationships between σ° and dw values, and (2) the effects of vegetation density and morphology on σ°-dw relationships. We developed a new method to classify pixels with significant linear relationships of multi-temporal σ° and dw (R2 > 0.5 and p-value <0.04), which were termed “Reliable Scatterer” (RS). RS included positive, negative, and a combination of both positive and negative relationships (corresponding to RS+, RS−, RS±, respectively). Our analysis revealed spatially varying vegetation densities and morphologies had a significant impact on RS types, where we found RS+ type pixels for woody vegetation, RS± for a mix of medium- and high-density herbaceous vegetation using C-band VV (C-VV) data, and RS− for sparse herbaceous vegetation using C-VH data. Overall, our study indicates that C-band dual-polarization backscatter is sensitive to water-depth variations for some vegetation types, and this sensitivity has the potential to serve as a reliable indicator for monitoring water depth in wetland environments.

Coherent and Incoherent Change Detection for Soil Moisture Retrieval From Sentinel-1 Data

This study proposes a hybrid incoherent–coherent change detection (CD) approach to retrieve surface soil moisture (SSM) from Sentinel-1 data. It combines time-series observations of synthetic aperture radar (SAR) backscatter and interferometric closure phase to deliver a method that does not require external calibration. A proof-of-concept assessment based on synthetic and experimental data is presented. Sentinel-1 and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> data over a study site in Southern Italy during the Winter–Spring season 2017 that covered both bare and vegetated soil conditions have been acquired and analyzed. For bare soils, results indicate good performance, that is, Pearson correlation ≈0.8 and root mean square error (RMSE) ≈0.05 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . Conversely, over vegetated surfaces, poor results are found.

Urban Impervious Surface Extraction Using Seasonal Time Series SAR Images

Urban impervious surface (UIS) is an essential indicator to measure urban ecology, and understanding its temporal and spatial distribution is of great significance for alleviating urban diseases such as heat island. Although remote sensing techniques have been widely used to extract UIS, the capability of synthetic aperture radar (SAR) technology in UIS extraction has not been thoroughly investigated. Accordingly, this study systematically analyzed the role of the backscattering and interferometric coherence features of dual polarized (VH/VV) SAR in UIS extraction, taking Tianjin city, China, as a study case. Specifically, we used seasonal time series Sentinel-1B backscattering and coherence images to extract UIS via two object-based classifiers. We further investigated the seasonal, polarization, and precipitation effects on UIS extraction using SAR images. Our findings suggested that, for coherence images, the extraction accuracy of UIS in summer is the highest, while for backscattering, that in spring and winter are the highest. Our results also suggested that VH polarization is more suitable for UIS extraction compared with VV polarization. These findings of this study confirmed the utility and effectiveness of time series SAR backscattering and coherence images in UIS extraction, providing an essential reference for UIS extraction in other places with a similar climate. We encourage more efforts to be made towards UIS extraction using SAR images and towards designing a filling scheme for the UIS extraction in a continuous manner, especially in cloudy and rainy areas.

The potential of synthetic aperture radar interferometry for assessing meltwater lake dynamics on Antarctic ice shelves

Abstract. Surface meltwater drains on several Antarctic ice shelves, resulting in surface and sub-surface lakes that are potentially critical for the ice shelf collapse. Despite these phenomena, our understanding and assessment of the drainage and refreezing of these lakes is limited, mainly due to lack of field observations and to the limitations of optical satellite imagery during polar night and in cloudy conditions. This paper explores the potential of backscatter intensity and of interferometric coherence and phase from synthetic aperture radar (SAR) imagery as an alternative to assess the dynamics of meltwater lakes. In four case study regions over Amery and Roi Baudouin ice shelves, East Antarctica, we examine spatial and temporal variations in SAR backscatter intensity and interferometric (InSAR) coherence and phase over several lakes derived from Sentinel-1A/B C-band SAR imagery. Throughout the year, the lakes are observed in a completely frozen state, in a partially frozen state with a floating ice lid and as open-water lakes. Our analysis reveals that the meltwater lake delineation is challenging during the melting period when the contrast between melting snow and lakes is indistinguishable. Despite this finding, we show using a combination of backscatter and InSAR observations that lake dynamics can be effectively captured during other non-summertime months. Moreover, our findings highlight the utility of InSAR-based observations for discriminating between refrozen ice and sub-surface meltwater and indicate the potential for phase-based detection and monitoring of rapid meltwater drainage events. The potential of this technique to monitor these meltwater change events is, however, strongly determined by the satellite revisit interval and potential changes in scattering properties due to snowfall or melt events.

Countrywide mapping of shrub forest using multi-sensor data and bias correction techniques

The continual increase of shrub forest in the Swiss Alps over the past few decades impacts biodiversity, forest succession and the protective function of forests. Therefore, up-to-date and area-wide information on its distribution is of great interest. To detect the shrub forest areas for the whole of Switzerland (41,285 km2), we developed an approach that uses Random Forest (RF), bias correction techniques and data from multiple remote sensing sources. Manual aerial orthoimage interpretation of shrub forest areas was conducted in a non-probabilistic way to derive initial training data. The multi-sensor and open access predictor data included digital terrain and vegetation height models obtained from Airborne Laser Scanning (ALS) and stereo-imagery, as well as Synthetic Aperture Radar (SAR) backscatter from Sentinel-1 and multispectral imagery from Sentinel-2. To mitigate the expected bias due to the training data sampling strategy, two techniques using RF probability estimates were tested to improve mapping accuracy. 1) an iterative and semi-automated active learning technique was used to generate further training data and 2) threshold-moving related object growing was applied. Both techniques facilitated the production of a shrub forest map for the whole of Switzerland at a spatial resolution of 10 m. An accuracy assessment was performed using independent data covering 7640 regularly distributed National Forest Inventory (NFI) plots. We observed the influence of the bias correction techniques and found higher accuracies after each performed iteration. The Mean Absolute Error (MAE) for the predicted shrub forest proportion was reduced from 6.04% to 2.68% while achieving a Mean Bias Error (MBE) of close to 0. The present study underscores the potential of combining multi-sensor data with bias correction techniques to provide cost-effective and accurate countrywide detection of shrub forest. Moreover, the map complements currently available NFI plot sample point data.

Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data

Global crop mapping and monitoring requires high-resolution spatio-temporal information. In this regard, dual polarimetric Synthetic Aperture Radar (SAR) sensors provide high temporal and high spatial resolutions with large swath width. Generally, crop phenological development studies utilized SAR backscatter intensity-based descriptors. However, these descriptors are derived either from the covariance matrix elements or from the eigendecomposition. Therefore, this approach fails to utilize the complete polarization information of the scattered wave. In this study, we propose a target characterization parameter, θxP that utilizes the 2D Barakat degree of polarization and the elements of the covariance matrix. We also propose an unsupervised clustering scheme using θxP and the scattering entropy, HxP. We utilize time-series Sentinel-1 data of canola and wheat fields over a Canadian test site to show the sensitivity of θxP to the development of crop morphology at different phenological stages. During the initial growth stages, θxP values are low due to the low vegetation density. In contrast, at advanced phenological stages, we observe decreased values of θxP due to the appearance of complex canopy structure. Similarly, the effectiveness of the unsupervised HxP/θxP clustering plane is also evident from the temporal clustering plots. This innovative clustering framework is beneficial for the operational use of Sentinel-1 SAR data for agricultural applications.

Soil moisture retrieval over agricultural fields through integration of synthetic aperture radar and optical images

ABSTRACT The soil roughness and two-way vegetation transmissivity parameters affect the radar backscattered signal; therefore, the results of the soil moisture retrieval from Synthetic Aperture Radar (SAR) images. In this paper, the Improved Water Cloud Model was extended to specify the effects of the above parameters. The surface roughness parameter values were calculated for each ground measurement site using an artificial neural network. This parameter was then used in the proposed models to rectify the effects of surface roughness on soil moisture estimation. Also, the Normalized Difference Water Index, derived from Landsat 5 imagery, was used to account for the impact of the vegetation canopy on the SAR backscatter. The accuracy of the proposed model was assessed using AIRSAR C-, L-, and P-band data, ALOS PALSAR L-band and two reference data were collected during NASA’s SMEX03 and SMAPVEX08 campaigns. The experimental results indicated that the proposed models had improved the RMSE and R values with respect to those in the IWCM in all bands and polarizations, with the highest values concerning the L band and VV polarization. In this band, the value of RMSE decreased by 0.002 (6.25%), and R increased by 0.054 (9.45%).

Deriving exclusion maps from C-band SAR time-series in support of floodwater mapping

Synthetic Aperture Radar (SAR) intensity is used as an input to many flood-mapping algorithms. The appearance of floodwater tends to cause a substantial decrease of backscatter intensity over scarcely vegetated terrain. However, limitations exist in areas where the SAR backscatter is not sufficiently sensitive to surface changes, e.g. shadow areas due to topography or obstacles on the ground, densely forested areas, sand, etc. Thus, we argue that it is of paramount importance to complement any SAR-based flood extent map with an exclusion map (EX-map) indicating all areas where the presence of water cannot be derived from SAR intensity observations. In this study, we introduce a methodology for generating an EX-map based on the analysis of time-series of SAR backscatter data. In particular, the identification of the EX-map is based on the combined use of three temporal indicators based on backscatter statistics, i.e. temporal median, minimum and standard deviation. As a test case, EX-maps were derived from Sentinel-1 data acquired during the 2014–2019 time period from six representative study sites. Reference maps were generated using a global land cover map, Digital Elevation Model (DEM)-derived shadow/layover masks, global urban footprint (GUF) data and a Sand Exclusion Layer (SEL). The cross-comparison revealed that the EX-map was consistent with reference maps obtained from other data sources.

The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations

Abstract. The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB &gt;250 Mg ha−1, where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates. The dataset is available at https://doi.org/10.1594/PANGAEA.894711 (Santoro, 2018).

Improving Urban Flood Mapping by Merging Synthetic Aperture Radar-Derived Flood Footprints with Flood Hazard Maps

Remotely sensed flood extents obtained in near real-time can be used for emergency flood incident management and as observations for assimilation into flood forecasting models. High-resolution synthetic aperture radar (SAR) sensors have the potential to detect flood extents in urban areas through clouds during both day- and night-time. This paper considers a method for detecting flooding in urban areas by merging near real-time SAR flood extents with model-derived flood hazard maps. This allows a two-way symbiosis, whereby currently available SAR urban flood extent improves future model flood predictions, while flood hazard maps obtained after the SAR overpasses improve the SAR estimate of urban flood extents. The method estimates urban flooding using SAR backscatter only in rural areas adjacent to urban ones. It was compared to an existing method using SAR returns in both rural and urban areas. The method using SAR solely in rural areas gave an average flood detection accuracy of 94% and a false positive rate of 9% in the urban areas and was more accurate than the existing method.

Effect of Vegetation Structure on Above Ground Biomass in Tropical Deciduous Forests of Central India

The study aimed to determine the above-ground biomass (AGB) of tropical deciduous forests in Central India using field-based techniques and spaceborne quad-pol ALOS PALSAR-2 L-band and dual-pol Sentinel 1A C-band synthetic aperture radar (SAR). Field sampling based on 73 forested grids exhibited a diverse forest composition in the Satpura Tiger Reserve (STR) with a total of 54 tree species of which 31.48% species were IUCN Red-Listed. The forests were highly diverse (H’=3.22) with a low concentration of dominance (CD =0.06) and a quite high species evenness (E = 0.81). The sensitivity and correlation between field-based AGB and SAR backscatter in STR were highest in cross-polarization due to its higher sensitivity to the vegetation structure in both L- and C-band SAR, where L-VH based AGB estimates were found more accurate (RMSE:5.17%). The study provided vital information for setting up strategies for biodiversity conservation, enhancing forest productivity, and restoring wildlife habitats.

Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine

Sentinel-1 satellites provide temporally dense and high spatial resolution synthetic aperture radar (SAR) imagery. The open data policy and global coverage of Sentinel-1 make it a valuable data source for a wide range of SAR-based applications. In this regard, the Google Earth Engine is a key platform for large area analysis with preprocessed Sentinel-1 backscatter images available within a few days after acquisition. To preserve the information content and user freedom, some preprocessing steps (e.g., speckle filtering) are not applied on the ingested Sentinel-1 imagery as they can vary by application. In this technical note, we present a framework for preparing Sentinel-1 SAR backscatter Analysis-Ready-Data in the Google Earth Engine that combines existing and new Google Earth Engine implementations for additional border noise correction, speckle filtering and radiometric terrain normalization. The proposed framework can be used to generate Sentinel-1 Analysis-Ready-Data suitable for a wide range of land and inland water applications. The Analysis Ready Data preparation framework is implemented in the Google Earth Engine JavaScript and Python APIs.

Oil Spill Thickness Determination from L-band Synthetic Aperture Radar

ABSTRACTEfficient and effective oil spill response requires accurate information regarding slick location, extent, and thickness to direct remediation activities. Of these three, the most challenging to determine is thickness. Ideally, the needed information would be provided by remote sensing instruments, particularly those operating from space. In this study we consider the capability of L-band synthetic aperture radar (SAR) for identifying oil layer thickness for slicks in open water given the range of oil properties and environmental conditions typical in this setting. The goal is to determine layer thickness with precision equivalent to that of the Bonn Agreement Oil Appearance Code. Here we report preliminary results of an ongoing study to determine whether either relative or absolute slick thickness can be determined from L-band SAR. The study has an experimental component, which uses low noise airborne SAR data acquired over slicks to evaluate the capability to determine relative thickness, i.e., to differentiate sheen from zones of varying thicker oil within a slick. The component of the study to evaluate whether absolute thickness can be determined from SAR uses backscatter simulations within a physics-based model of electromagnetic scattering from slicked and unslicked ocean surfaces accounting for oil properties, meteorological conditions, and sea state. As part of the theoretical component of the study, we evaluate the backscatter sensitivity to oil characteristics to determine which properties most influenced the SAR measurements. These results are used to determine whether the oil-to-water ratio or the oil thickness can be quantified with reasonable accuracy based upon SAR backscatter intensities alone or whether it requires calibration to go from relative to absolute thickness. The ratio of the backscatter contrast between clean and slicked ocean surfaces is shown to be sensitive to variations within slicks that well correlate with the oil layer thickness. Determination of absolute thickness is much more challenging given the variation of oil properties as the oil weathers on the sea surface.

Urban classification using preserved information of high dimensional textural features of Sentinel-1 images in Tabriz, Iran

Monitoring urban land through satellite images has rapidly developed with the advent of modern technologies, and the increasing number of satellites plays a contributing role. While optical images have a high capability in urban monitoring, they still have some limitations, including their dependence on climatic conditions and spectral information, which lead to difficulty in making a distinction between bare land, buildings and other features. The impossibility of optical imagery at night is another issue that can make the land cover classification difficult. Synthetic aperture radar (SAR) allows imaging in all climatic conditions and at nighttime, with an ability to detect phenomena based on their geometry, roughness, and location, making the land cover classification much easier. In the present study, radar Sentinel-1 images with polarization VV and VH were used for the land classification in Tabriz. Sentinel-2 images for the same time were applied as a reference for the calibration and accuracy assessment. Maximum likelihood (ML) and support vector machine (SVM) algorithms were also employed for supervised classification. In both algorithms, the classification was performed in windows with different sizes once by the SAR backscattering coefficient (σ0) and then by combining the backscattering coefficients with the statistical data obtained from the texture. The results showed that the use of radar images only with backscattering intensity resulted in poor performance while using the gray-level co-occurrence matrix (GLCM) of texture features increased the accuracy. The transmitted frequencies of radar images have different redistributions to different phenomena. The numerical results obtained from the radar image classification show that using only the radar image redistribution led to low accuracies at both VV and VH polarization, but the use of the textural analysis significantly increased the accuracy of the classifications. The statistical results obtained from the ML and SVM classifications for radar images at VV and VH polarization indicated that the latter performed better than the former. When texture analysis was not used in the classes, the classification accuracy was low with kappa values of 0.37 and 0.42 for VV and VH polarization, respectively. The use of texture analysis and obtaining the optimum window size is increase the classification accuracy with a better performance for VH polarization. The SVM classification method with a kappa coefficient of 0.72% showed better performance than the ML one with a kappa coefficient of 0.61%. Conclusively, in the absence of Sentinel-2 datasets, Sentinel-1 images are good alternatives if the preserved texture information is available for the land cover classification. Results of this research are of great importance for developing the remote sensing methods and their techniques can be considered as progressive research in the domain of remote sensing sciences.

Sentinel-1 interferometric coherence and backscattering analysis for crop monitoring

In this study, we investigate the synergic use of synthetic aperture radar (SAR) backscattering (i.e., sigma nought σ0) and InSAR coherence (γ) maps as a tool for crop growth monitoring. Experiments were carried out using Sentinel-1 TOPS SAR data and field observations in one of the State General Directorate of Agriculture Enterprise farms in Konya (Central Turkey). The phenological stages of maize, sunflower, and wheat have been analyzed and compared to coherence and backscatter time series of Sentinel-1 data on multiple tracks and polarizations. The results evidence a strong correlation between different phenological stages of the crops and the InSAR coherence. Specifically, the observed coherence values are the highest for the maize (γasc, desc = 0.47) and sunflower (γasc = 0.49, γdesc = 0.48) after plowing the fields and seeding the crops. The coherence decreases with the plants' growth and reaches the lowest values for maize, sunflower, and wheat (γ = 0.08, γ = 0.09 and γ = 0.07, respectively) when the ground is completely covered by plants. Then, a coherence increase is observed after the harvesting time (γ = 0.51, γ = 0.50 and γ = 0.42 for maize, sunflower, and wheat, respectively). In terms of multi-temporal SAR backscattering, we find significant changes of the σ0 values during the crops' growing stages due to the changes in their leaf geometry and physical structure. The highest σ0 values for the maize, sunflower, and wheat are obtained as −9.18 dB, −5.24 dB and −10.05 dB, respectively, for the ascending orbit, in mid growing stages. Results show the improved capacity of SAR-driven measurements for agriculture monitoring and precise farming activities when InSAR coherence and backscattering are synergistically used. Specifically, the coherence allows estimating the main growth stages of the different crop types. Moreover, SAR backscattering provides reliable information on the whole growth stages during the agricultural season, and it might be profitably exploited for crop assessment.

Lake water level variability determination from SAR backscatter of discrete objects, GNSS levelling and satellite altimetry

In the absence of enough gauging stations for monitoring lake water level, three (3) methods are presented in this research: (1) Synthetic Aperture Radar (SAR) backscatter from discrete object; (2) Global Navigation Satellite System (GNSS) levelling; and (3) satellite altimetry. The results showed that the Laguna Lake water level is not homogeneous but instead varies at different locations. The GNSS leveling showed that water level varies from −0.64 to 1.26 m from water gauge data. Using the fish pen guard house as discrete object for double bounce scattering a correlation of 0.86 was computed between the gauge data and sigma 0 the equation derived through linear regression was used to predict water level from SAR images at different periods. The satellite altimeter data correlated with water level from graduated staff at 0.97 with a difference of about 0.29 m. It also showed an increasing water level trend from north to south.

Floodwater detection in urban areas using Sentinel-1 and WorldDEM data

Remote sensing using synthetic aperture radar (SAR) is an important tool for emergency flood incident management. At present, operational services are mainly aimed at flood mapping in rural areas, as mapping in urban areas is hampered by the complicated backscattering mechanisms occurring there. A method for detecting flooding at high resolution in urban areas that may contain dense housing is presented. This largely uses remotely sensed data sets that are readily available on a global basis, including open-access Sentinel-1 SAR data, the WorldDEM digital surface model (DSM), and open-access World Settlement Footprint data to identify urban areas. The method is a change detection technique that locally estimates flood levels in urban areas. It searches for increased SAR backscatter in the post-flood image due to double scattering between water (rather than unflooded ground) and adjacent buildings, and reduced SAR backscatter in areas away from high slopes. Areas of urban flooding are detected by comparing an interpolated flood level surface to the DSM. The method was tested on two flood events that occurred in the UK during the storms of Winter 2019–2020. High urban flood detection accuracies were achieved for the event in moderate density housing. The accuracy was reduced for the event in dense housing, when street widths became comparable to the DSM resolution, though it would still be useful for incident management. The method has potential for operational use for detecting urban flooding in near real-time on a global basis.

Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification

Robust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the potential of textural information to improve automated image classification. In this work, we investigate the inclusion of Sentinel-1 (S1) texture features into a Bayesian classifier that accounts for linear per-class variation of its features with IA. We use the S1 extra-wide swath (EW) product in ground-range detected format at medium resolution (GRDM), and we compute seven grey level co-occurrence matrix (GLCM) texture features from the HH and the HV backscatter intensity in the linear and logarithmic domain. While GLCM texture features obtained in the linear domain vary significantly with IA, the features computed from the logarithmic intensity do not depend on IA or reveal only a weak, approximately linear dependency. They can therefore be directly included in the IA-sensitive classifier that assumes a linear variation. The different number of looks in the first sub-swath (EW1) of the product causes a distinct offset in texture at the sub-swath boundary between EW1 and the second sub-swath (EW2). This offset must be considered when using texture in classification; we demonstrate a manual correction for the example of GLCM contrast. Based on the Jeffries–Matusita distance between class histograms, we perform a separability analysis for 57 different GLCM parameter settings. We select a suitable combination of features for the ice classes in our data set and classify several test images using a combination of intensity and texture features. We compare the results to a classifier using only intensity. Particular improvements are achieved for the generalized separation of ice and water, as well as the classification of young ice and multi-year ice.

Multi-Scale LG-Mod Analysis for a More Reliable SAR Sea Surface Wind Directions Retrieval

An improved version of the Local-Gradient-Modified (LG-Mod) algorithm for Sea Surface Wind (SSW) directions retrieval by means of Synthetic Aperture Radar (SAR) images is presented. A “local” multi-scale analysis of wind-aligned SAR patterns is introduced to improve the LG-Mod sensitivity to SAR backscattering modulations, occurring locally with various spatial wavelengths. The Marginal Error parameter is redefined, and the adoption of the Directional Accuracy Maximization Criterion (DAMC) allows for the novel Multi-Scale (MS) LG-Mod to automatically select the local processing scale that may be regarded as optimal for pattern enhancement, once a discrete set of scales has been already fixed. Hence, this optimal scale successfully gives evidence to guarantee the best achievable local direction estimation. The assessment of the MS LG-Mod is carried on both simulated SAR images and a Sentinel-1 (S-1) dataset, consisting of 350 Interferometric Wide Swath Ground Range Multi-Look Detected High-Resolution images, which cover the region of the Gulf of Maine. In the latter case, the removal of artifacts and non-wind features from SAR amplitudes is mandatory before directional estimations. In situ wind observations gathered by the National Oceanic and Atmospheric Administration National Data Buoy Center (NOAA NDBC) are exploited for validation. The findings obtained from S-1 data confirm the ones from simulated patterns. The MS LG-Mod analysis performs better than each single-scale one in terms of both percentages of reliable directions and directional Root Mean Square Error (RMSE) values achieved.

Grassland Mowing Detection Using Sentinel-1 Time Series: Potential and Limitations

Grasslands encompass vast and diverse ecosystems that provide food, wildlife habitat and carbon storage. Their large range in land use intensity significantly impacts their ecological value and the balance between these goods and services. Mowing dates and frequencies are major aspects of grassland use intensity, which have an impact on their ecological value as habitats. Previous studies highlighted the feasibility of detecting mowing events based on remote sensing time series, a few of which using synthetic aperture radar (SAR) imagery. Although providing encouraging results, research on grassland mowing detection often lacks sufficient precise reference data for corroboration. The goal of the present study is to quantitatively and statistically assess the potential of Sentinel-1 C-band SAR for detecting mowing events in various agricultural grasslands, using a large and diverse reference data set collected in situ. Several mowing detection methods, based on SAR backscattering and interferometric coherence time series, were thoroughly evaluated. Results show that 54% of mowing events could be detected in hay meadows, based on coherence jumps. Grazing events were identified as a major confounding factor, as most false detections were made in pastures. Parcels with one mowing event in the summer were identified with the highest accuracy (71%). Overall, this study demonstrates that mowing events can be detected through Sentinel-1 coherence. However, the performances could probably be further enhanced by discriminating pastures beforehand and combining Sentinel-1 and Sentinel-2 data for mowing detection.