Sentinel-1 C-band Research Articles

High-precision sugarcane yield prediction by integrating 10-m Sentinel-1 VOD and Sentinel-2 GRVI indexes

Developing accurate models of sugarcane yield mapping at fine-scale is of paramount importance and will benefit many aspects of managing growth and harvest of sugarcane crops. Here, we combined high-spatial-resolution multi-sensor (optical and microwave) remote sensing data to estimate sugarcane yield using two approaches. First, we retrieved the 10 m resolution vegetation optical depth (VOD) using C-band Sentinel-1 synthetic aperture radar data via the water cloud model, then derived a combined VOD and Sentinel-2 derived green–red vegetation index (GRVI, a traditional vegetation index) time series. Second, we adopted the eXtreme Gradient Boost (XGBoost) machine learning algorithm to estimate total sugarcane production based on the aggregated mean monthly VOD and GRVI time series at the county scale. County-scale sugarcane yield data from 81 counties across Guangxi, China (2018–2020) were used for model training and testing. We then kept the trained best-performance model unchanged to re-predict pixel-scale (10-m resolution) yield using VOD and GRVI images. A SHapley Additive exPlanations (SHAP) approach was employed to test XGBoost knowledge regarding the mechanisms affecting yield. The main outcomes from this study were as follows. (1) The XGBoost model and VOD and GRVI time series unequivocally provided reliable and precise estimates of sugarcane yield, with a low relative error both at the county scale and at the pixel scale. (2) Predictions of sugarcane yield in the growing season using combined VOD and GRVI data yields high accuracy (R2 = 0.815) with specific spatial patterns of sugarcane yields for Guangxi province, and were feasible up to two months prior to harvest. (3) The SHAP model revealed that Sentinel-1 derived VOD data are important for predicting yield. This research portrays the benefits of combining VOD and GRVI data in automatic machine learning models for estimating sugarcane yield at high spatial and temporal resolutions.

Soil moisture retrieval from Sentinel-1 using a first-order radiative transfer model—A case-study over the Po-Valley

Soil moisture is an important variable controlling many land surface processes and is used to quantify precipitation, drought, flooding, irrigation and other factors that influence decision making and risk-assessment. This paper presents the retrieval of high resolution (∼1 km) soil moisture data from Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter measurements using a new bistatic radiative transfer modeling framework (RT1) previously only tested for scatterometer data. The model is applied over a diverse set of landcover types across the entire Po-Valley in Italy over a 4-year time-period from 2016 to 2019. The performance of the soil moisture retrievals is analyzed with respect to the ERA5-Land reanalysis dataset. The model parameterisation and retrieval method are chosen such as to constitute a trade-off between a physically plausible and a computationally feasible modeling approach. The results demonstrate the potential of RT1 for the retrieval of high-resolution soil moisture data from SAR time series.

Spatial Evaluation of a Natural Flood Management Project Using SAR Change Detection

This study proposes an evaluation method using C-band Sentinel-1 synthetic aperture radar (SAR) data to provide evidence of flood characteristic changes after the restoration of a floodplain. A portable, flexible evaluation framework has replicated previous change detection research approaches to analyse a Natural Flood Management (NFM) project on the Sussex Ouse river in southern England, conducted by the Sussex Flow Initiative (SFI), to ascertain how control measures have helped mitigate flood risk. GIS operations were conducted on the mapped results of the change detection procedure to identify how flood area, form and compactness have been affected after the NFM installation restored a floodplain to slow river flow and how these changes relate to the overall aims of the project. Innovative means were employed to verify the change detection methodology by sampling flood records from internet-published drone footage. The overall accuracy achieved using the Change Detection and Thresholding (CDAT) technique was 75%. The use of SAR data provides evidence of how NFM features function during significant flood events, providing a mapped delineation of the actual flood extent. A comprehensive scorecard has been developed to evaluate the positive and negative outcomes of the spatial changes that have manifested in post-restoration floods, in comparison to inundation before the installation. Results from this study have been included in the annual report of the SFI project to demonstrate how key features have attenuated flood waters in accordance with design intentions.

Deciphering Small-Scale Seasonal Surface Dynamics of Rock Glaciers in the Central European Alps Using DInSAR Time Series

The Essential Climate Variable (ECV) Permafrost is currently undergoing strong changes due to rising ground and air temperatures. Surface movement, forming characteristic landforms such as rock glaciers, is one key indicator for mountain permafrost. Monitoring this movement can indicate ongoing changes in permafrost; therefore, rock glacier velocity (RGV) has recently been added as an ECV product. Despite the increased understanding of rock glacier dynamics in recent years, most observations are either limited in terms of the spatial coverage or temporal resolution. According to recent studies, Sentinel-1 (C-band) Differential SAR Interferometry (DInSAR) has potential for monitoring RGVs at high spatial and temporal resolutions. However, the suitability of DInSAR for the detection of heterogeneous small-scale spatial patterns of rock glacier velocities was never at the center of these studies. We address this shortcoming by generating and analyzing Sentinel-1 DInSAR time series over five years to detect small-scale displacement patterns of five high alpine permafrost environments located in the Central European Alps on a weekly basis at a range of a few millimeters. Our approach is based on a semi-automated procedure using open-source programs (SNAP, pyrate) and provides East-West displacement and elevation change with a ground sampling distance of 5 m. Comparison with annual movement derived from orthophotos and unpiloted aerial vehicle (UAV) data shows that DInSAR covers about one third of the total movement, which represents the proportion of the year suited for DInSAR, and shows good spatial agreement (Pearson R: 0.42–0.74, RMSE: 4.7–11.6 cm/a) except for areas with phase unwrapping errors. Moreover, the DInSAR time series unveils spatio-temporal variations and distinct seasonal movement dynamics related to different drivers and processes as well as internal structures. Combining our approach with in situ observations could help to achieve a more holistic understanding of rock glacier dynamics and to assess the future evolution of permafrost under changing climatic conditions.

Dam Deformation Monitoring using Cloud-Based P-SBAS Algorithm: The Kramis Dam Case (Algeria)

This paper presents the application of the Parallel Small Baseline Subset (P-SBAS) algorithm, provided by the Geohazards Exploitation Platform for the precise monitoring of an earth dam's ground deformation using C-band Sentinel-1 data. The test site object of this study was the Kramis dam, located in the Mostaganem State, Western Algeria. Among the multiple advanced DInSAR techniques, SBAS is very adequate for long-term displacement monitoring in areas with changing terrain and vegetation. Ten corner reflectors were installed as a backscattered radar signal amplification tool to reduce the effect of temporal decorrelation and delineate the dam area. Four Sentinel-1 A and B satellite tracks were available (T30, T37, T103, and T110) to measure displacements, in the Line of Site (LoS) direction, for two years since the installation of the CRs in July 2019. The results showed a subsiding area on the left bank of the dam dike, with a velocity of 4mm/yr, and an uplifting rate of 3-4mm/yr in the upper part of the dike. The entire 3-dimensional vector of displacement of the dam vicinity was estimated using the least-squares method, proving a better understanding of the dam's temporal deformation, particularly for dams with a high exposure factor and associated risk.

On the potential of Sentinel-1 for sub-field scale soil moisture monitoring

Soil moisture (SM) datasets at high spatial resolutions are beneficial for a wide range of applications, such as monitoring and prediction of hydrological extremes, numerical weather prediction, and precision agriculture. For large scale applications in particular, remotely sensed SM has advantages over in situ data because it provides gridded estimates and because it is less labour-intensive. However, until present, active microwave SM data have not been presented at their native spatial resolution, since the quality of these data is limited by speckle. We explored the potential and limits of high spatial resolution of active microwave SM observations. We used a Sentinel-1 C-band SAR SM dataset at six spatial resolutions ranging from 20 × 20 to 120 × 120 m2. This was compared to a closely spaced (20 m) in situ dataset collected on a non-irrigated agricultural field (±2.5 ha) in the Southeast of Luxembourg. A comparison of the field and satellite datasets demonstrated how Sentinel-1 data with a high spatial resolution can be used to quantify temporal within-field SM variability. SM was accurately estimated at spatial resolutions of 60 × 60 m2 and coarser, where the temporal correlation was found to be 0.67 and sub-field variations in SM were still detected. Spatial correlation was limited by the absence of SM variability within the field. These results indicate that high spatial resolution SM estimates from Sentinel-1 data can be valuable for monitoring temporal SM variations within agricultural fields.

Multiband 2D InSAR deformation models with error estimates from natural neighbour interpolation: Case study in the Latrobe Valley, Australia

A long term objective for Australia is to develop a continent-wide 2D deformation model based on satellite interferometric synthetic aperture radar (InSAR). Nation-wide InSAR processing has been demonstrated in European countries and Japan. However, there are some key differences to the Australian situation for a 2D model (i.e., vertical and east–west surface movement rates) where SAR data from ascending and descending satellite orbits are needed. The primary difference is that while ESA’s Sentinel-1 ascending and descending satellite observations underpin 2D models in other parts of the world, the Australian continent is only covered by descending Sentinel-1 SAR data in most areas. Combined with Australia’s large size of 7.63 million km2, this means a different approach is needed to realise 2D deformation models. In this article, we propose the novel approach of (1) combining supplementary multiband SAR (ascending X-band and L-band) with the descending C-band Sentinel-1 data, and (2) using natural neighbour (NN) interpolation as a computationally efficient interpolation method for combining the multiband InSAR data on common grids prior to 2D model computation. This is an alternative to the computationally intensive Ordinary Kriging (OK) approach. A disadvantage of NN compared to OK is that it does not compute an error estimate, which we address by deriving an error estimate for the NN interpolation so that errors are provided for each 2D rate to aid with their correct interpretation. To demonstrate our new approach, we compute experimental 2D deformation models that propagate the full error budget from InSAR, a GNSS connection to ITRF2014 and NN interpolation into an error grid in the Latrobe Valley in southeast Australia. The significance of our approach is that it provides a suitable combination of methods that may be used to develop 2D continental deformation models for Australia’s unique circumstances. Our results demonstrate the accuracy of NN and OK interpolation are at the same level (within error), and the error estimates from both interpolation techniques are also comparable: X band (TerraSAR-X) ∼ 1 mm/yr; C band (Sentinel-1) ∼ 2.5 mm/yr, and L band (ALOS PALSAR1) 4 mm/yr. For the first time, we reveal 2D deformation in the Latrobe Valley region showing horizontal east–west deformation in the area surrounding an above-ground mine and reaching 20 mm/yr, with vertical subsidence rates reaching −40 mm/yr.

High Spatial and Temporal Soil Moisture Retrieval in Agricultural Areas Using Multi-Orbit and Vegetation Adapted Sentinel-1 SAR Time Series

The retrieval of soil moisture information with spatially and temporally high resolution from Synthetic Aperture Radar (SAR) observations is still a challenge. By using multi-orbit Sentinel-1 C-band time series, we present a novel approach for estimating volumetric soil moisture content for agricultural areas with a temporal resolution of one to two days, based on a short-term change detection method. By applying an incidence angle normalization and a Fourier Series transformation, the effect of varying incidence angles on the backscattering signal could be reduced. As the C-band co-polarized backscattering signal is prone to vegetational changes, it is used in this study for the vegetational correction of its related backscatter ratios. The retrieving algorithm was implemented in a cloud-processing environment, enabling a potential global and scalable application. Validated against eight in-situ cosmic ray neutron probe stations across the Rur catchment (Germany) as well as six capacitance stations at the Apulian Tavoliere (Italy) site for the years 2018 to 2020, the method achieves a correlation coefficient of R of 0.63 with an unbiased Root Mean Square Error of 0.063 m3/m3.

A Globally Applicable Method for NDVI Estimation from Sentinel-1 SAR Backscatter Using a Deep Neural Network and the SEN12TP Dataset

Vegetation monitoring is important for many applications, e.g., agriculture, food security, or forestry. Optical data from space-borne sensors and spectral indices derived from their data like the normalised difference vegetation index (NDVI) are frequently used in this context because of their simple derivation and interpretation. However, optical sensors have one major drawback: cloud coverage hinders data acquisition, which is especially troublesome for moderate and tropical regions. One solution to this problem is the use of cloud-penetrating synthetic aperture radar (SAR) sensors. Yet, with very different image characteristics of optical and SAR data, an optical sensor cannot be easily replaced by SAR sensors. This paper presents a globally applicable model for the estimation of NDVI values from Sentinel-1 C-band SAR backscatter data. First, the newly created dataset SEN12TP consisting of Sentinel-1 and -2 images is introduced. Its main features are the sophisticated global sampling strategy and that the images of the two sensors are time-paired. Using this dataset, a deep learning model is trained to regress SAR backscatter data to NDVI values. The benefit of auxiliary input information, e.g., digital elevation models, or land-cover maps is evaluated experimentally. After selection of the best model configuration, another experimental evaluation on a carefully selected hold-out test set confirms that high performance, low error, and good level of spatial detail are achieved. Finally, the potential of our approach to create dense NDVI time series of frequently clouded areas is shown. One limit of our approach is the neglect of the temporal characteristics of the SAR and NDVI data, since only data from a single date are used for prediction.

SAR and InSAR data linked to soil moisture changes on a temperate raised peatland subjected to a wildfire

Interferometry of Synthetic Aperture Radar (InSAR) can potentially contribute to the cost-effective regional or global monitoring of the degradation and restoration of peatlands. However, there are uncertainties about the links between InSAR results and peatland ecohydrological parameters, especially soil moisture. Here, we analyse the relationships between the temporal evolutions of InSAR coherence, ground displacements, and in-situ soil moisture measurements for a temperate raised bog at Ballynafagh, Co. Kildare, Ireland, in the period 2017-mid-2021. We also investigate the effects of a wildfire in June–July 2019 on those relationships. InSAR-derived ground displacements from Sentinel-1 C-band radar data indicate long-term subsidence of the intact and Active Raised Bog. Superimposed on the long-term displacement trends are annual oscillations that are linked to variations in rainfall and temperature and that are in phase with changes in soil moisture. We show that InSAR coherence is directly related to the change in soil moisture, with large changes causing coherence decrease or loss. The wildfire removed a 10–20 cm thick mossy vegetation layer across 60–70% of the intact bog area. The SAR backscatter intensity in VV polarisation increased after the wildfire, but the InSAR coherence, the InSAR-derived surface displacements and the soil moisture were not noticeably affected. We therefore infer that C-band radar waves attenuate in the active vegetation layer, but penetrate through it into the upper few cm of the underlying peat. The SAR backscatter occurs primarily at this level in the peat, where its coherence is controlled by the soil moisture. These findings underpin application and interpretation of radar for monitoring of peatlands, even if affected by wildfires, which are forecast to increase in both frequency and intensity due to global warming.

Monitoring erosion in tropical savannas from C-band radar coherence

Increased erosion related to climate and/or land cover change has adverse impacts on terrestrial and aquatic ecosystems. Mapping of erosion hotspots improves our ability to identify and potentially remediate the most active erosion sources. The topical savannas of the Great Barrier Reef catchments, in northeast Australia, are generating excessive sediment yields, primarily from gully erosion. To reduce the adverse impacts on marine ecosystems, there is an urgent need to identify priority erosion hotspots and implement mitigation measures. While repeat airborne LiDAR surveys allow subtle topographic change detection, they are cost-intensive for catchment-scale applications and their applicability is constrained by data avalability and detection treshold of ground level change. In contrast, satellite-based radar imagery can allow large scale tracking of geomorphic change at high temporal resolution. Here we apply a new method based on Sentinel-1 C-band radar images and Coherence Change Detection (CCD), where large stacks of interferometric coherence images are subdivided with rain gauge time-series for separation of erosion-rich and erosion-free coherence information. After correcting the former with the latter, the resulting corrected coherence maps are compared with differential elevation models derived from multitemporal LiDAR, regional scale gully delineation maps, maps of gullying potential and in-situ field verification. Our results demonstrate the promising potential of this technique in detecting gully erosion hotspots. The coherence loss indicating erosion/deposition is well detected in wide gully morphologies, however, the line-of-sight angle does not allow penetration into narrow linear gullies. Further, CCD detects sheetwash or rill erosion occurring in areas identified as at risk of gully expansion, which is commonly below detection threshold for multitemporal LiDAR datasets. When used with LiDAR-derived geomorphic change mapping and gullying potential maps, CCD allows identification of gully erosion dynamics and forecasting gully evolution and creation, which is critical for supporting mitigation measures.

Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for burnt and unburnt scars

This research compares the use of the SAR (Sentinel-1) and Optical (Sentinel-2) sensors in identifying and mapping burnt and unburnt scars are rising during a bushfire in southeastern Australia and Margalla Hills, Islamabad, Pakistan, in 2019 and 2020. In order to evaluate the backscatter strength along with the Polarimetric decomposition portion, the C-band dual-polarized Sentinel-1 data was investigated to determine the magnitude of the burnt areas of forest cover in the study area. We could derive texture measurements from locally-based statistics using the Grey Level Co-occurrence Matrix (GLCM) and the backscatter coefficient. This was because of how well it picked up on differences in texture between burned and unburned scars. In contrast, Sentinel-2 optical remote sensing was employed to evaluate the extent of the burnt intensity levels for both regions utilizing the differential Normalized Burnt Ratio (dNBR). A Support Vector Machine (SVM) and Markov Random Field (MRF) classifier were utilized to investigate the study’s context. The ideal smoothing parameter is the result of incorporating the image’s spectral characteristics and spatial meaning. Sentinel-2 images were used as a foundation for both the test and training datasets, which were built from images of both unburned and burned areas broken down pixel by pixel. In both types, including spectral sensitivity and sensitivity of Polarimetric for the two groups identified after classification, the experimental findings showed a clear association between them. The algorithm’s efficiency was evaluated using the kappa coefficient and F-score calculation. Except for Sentinel-1 data in Pakistan, all fire areas have more than 0.80 accuracies. The highest precision of both Sentinel-1 and Sentinel-2 was also provided by the performance of users’ and producers’ accuracy. The entropy alpha decomposition helped define the target given by the H-a plane based on its physical properties. After the burn, the entropy and alpha values diminished and formed a pattern. However, the findings in this field validate the effectiveness of SAR sensors data and optical satellite in forest applications. The related sensitivity is highly dependent on the composition of the landscape, the geographical nature of the study area, and the severity of the burn.

Mapping lava flow from the Kilauea eruption of 2018 in the east rift zone using space-based synthetic aperture radar

ABSTRACT Lava flows from volcanoes can significantly destroy infrastructure, although they rarely threaten human life. Mapping lava flows is essential to managing the volcanic hazard during eruptions and understanding significant changes in geomorphology after eruptions. In 2018, the eruption of Kilauea, Hawaii’s youngest and most active volcano, destroyed more than 700 homes, farmlands, and roads. Active lava flow tracking is crucial for providing timely warnings to evacuate residents and protect infrastructure. We constructed a map of the lava flow in the East Rift Zone (ERZ) during the Kilauea volcanic eruption in 2018 using remotely sensed satellite synthetic aperture radar observations. We processed 23 C-band Sentinel-1 A/B interferometric wide mode acquisitions captured from May 1 to 17 September 2018 and applied a coherent change detection (CCD) technique to detect minute changes in the target structure. A time series of coherence maps provided detailed spatial information on the movement of the lava flow fields in the ERZ. The overall lava flow map had an area of approximately 33.3 km2 and was verified with a lava thickness map provided by the United States Geological Survey that was generated by two digital elevation models using airborne light detection and ranging observations. Excellent agreement (>90%) among the lava flow observations suggests that the CCD technique is effective in monitoring lava flows during a volcanic eruption.

A satellite-based monitoring system for quantifying surface water and mesic vegetation dynamics in a semi-arid region

Semi-arid and arid systems cover one third of the earth’s land surface, and are becoming increasingly drier, but existing datasets do not capture all of the types of water resources that sustain these systems. In semi-arid environments, small surface water bodies and areas of mesic vegetation (wetlands, wet meadows, riparian zones) function as critical water resources. However, the most commonly-used maps of water resources are derived from the Landsat time series or single date aerial photographs, and are too coarse either spatially or temporally to effectively monitor water resource dynamics. In this study, we produced a Sentinel Fusion (SF) water resources product for a semi-arid mountainous region of the western United States, which includes monthly maps of both a) surface water and b) mesic vegetation at 10 m spatial resolution using freely available Earth observation data on an open access platform. We applied random forest classifiers to optical data from the Sentinel-2 time series, synthetic aperture radar (SAR) data from the Sentinel-1 time series, and topographic variables. We compared our SF product with three commonly used and publicly available datasets in the western U.S. We found that our surface water class contained fewer omission errors than a leading global surface water product (94 % producer’s accuracy (PA) vs 84 %) and comparable user’s accuracy (UA) (91 % vs 97 %) with commission errors occurring largely in mixed water pixels. Our mesic vegetation class had up to 43 % higher PAs compared to the National Wetlands Inventory (NWI) estimates and up to 78 % higher UAs over the Sage Grouse Initiative mesic resources maps during the most critical part of the water year. We found that while inclusion of SAR data from the C-band Sentinel-1 sensor consistently improved estimates of water resources in each of the last four months of the 2021 water year when compared to optical-only + topographic variables, only in September did those improvements lie outside of the 95 % confidence interval. With nine times finer spatial resolution and more frequent image collection, our SF maps characterize intra-annual dynamics of smaller water bodies (<30 m wide) and mesic vegetation integral to ecosystem functioning in semi-arid systems compared to leading Landsat-derived products. Further, our workflow is easily reproducible using freely available data on an open access platform, and can be adopted to help guide land use decisions related to water resources by farmers, ranchers, and conservationists in semi-arid environments.

Sentinel-1 Response to Canopy Moisture in Mediterranean Forests before and after Fire Events

This study investigates the sensibility of Sentinel-1 C-band backscatter to the moisture content of tree canopies over an area of about 500 km2 in north-western Portugal, with specific analysis over burnt areas. Sentinel-1 C-VV and C-VH backscatter values from 276 images acquired between January 2018 and December 2020 were assigned to five classes depending on the Drought Code (DC) scenario over several unburned and burned sites with total (&gt;90%) forest canopy cover. Confounding variables such as tree cover and incidence angle were accounted for by masking using specific thresholds. The following results are discussed: (a) C-VV and C-VH backscatter values are inversely correlated (R2 = 0.324 to 0.438 −p &lt; 0.001) with local incidence angle over canopies; (b) correlation is significantly stronger over very wet scenarios (DC class = 0 to 1); (c) C-VV and C-VH backscatter values can discriminate wet to dry forest environments, but they are less sensitive to the transition between dry (DC classes = 1 to 10, 10 to 100) and extremely dry environments (DC classes = 100 to 1000); (d) C-VH is more sensible than C-VV to capture burnt canopy; and (e) the C-VH polarization captures post-fire recovery after an average minimum period of 360 days after the fire event, although with less distinction for extremely wet soils. We conclude that C-band VH backscatter intensity decreases from wet to dry canopy conditions, that this behavior of the backscatter signal with respect to canopy dryness is lost after a fire event, and that after one year it is recovered.

Time series soil moisture retrieval from SAR data: Multi-temporal constraints and a global validation

Time series algorithms for soil moisture retrieval from synthetic aperture radar (SAR) data have steadily increased in popularity over the past decade due to the feasibility of decoupling the effect of other surface variables, and the increasing availability of dense time series SAR data. While soil moisture inversion from time series data can utilize more independent observations, the value of further constraints on the inversion process are widely acknowledged. However, how to constrain a time series retrieval for global soil moisture mapping is still unresolved. In this study, three kinds of time series constraints were further developed and evaluated, including the use of 1) temporal behavior of soil moisture and soil moisture bounds; 2) temporal behavior of vegetation or time-invariant vegetation; and 3) time series ensemble skill. The effect of these constraints was investigated using 4 years (2016–2019) C-band Sentinel-1 data collected over 547 worldwide stations from 17 networks available on the international soil moisture network (ISMN) and intensive ground samples collected during the Fifth Soil Moisture Active and Passive Experiment (SMAPEx-5). While the effect of these temporal retrieval skills varies in time and space, the global validation yielded four general suggestions: 1) the assumption of time-invariant vegetation contributed negatively even for a short retrieval period of ≤12 days; 2) reliable soil moisture bounds of each retrieval period can substantially improve the retrieval statistics at the cost of an underestimated soil moisture range; 3) the temporal constraints of soil moisture and vegetation need to be used together with the soil moisture bounds for reliable estimation; 4) the use of an ensemble retrieval could partly remove the retrieval uncertainties at the expense of underestimating soil moisture variation. The use of these constraints resulted in a competitive correlation coefficient (R: 0.64), root mean square error (RMSE: 0.072 m3/m3) and unbiased RMSE (ubRMSE: 0.052 m3/m3) at a spatial grid of 100 m, with similar performance achieved across a retrieval window up to 132 days.

Analyzing spatio-temporal mechanisms of land subsidence in the Parowan Valley, Utah, USA

Parowan Valley, Utah (USA), is an agricultural region experiencing rapid subsidence due to extensive groundwater extraction from aquifers with a significant portion of fine-grained sediments. To analyze the subsidence spatio-temporally, time-series Interferometric Synthetic Aperture Radar (InSAR) of 155 Sentinel-1 C-band scenes were processed. These data showed approximately 30 cm of ground subsidence in Parowan Valley from 2014 to 2020. Because of the high temporal sampling rate of the Sentinel-1 satellite (12-day cycle), it is possible to determine the seasonal changes of ground deformation and relate this to groundwater extraction. To better understand the relationship between ground deformation and groundwater extraction in the Parowan Valley, temporal changes in hydraulic head data from US Geological Survey observation wells were monitored. Additionally, well logs were analyzed and used to construct a map that showed the percentage of fine-grained material in the subsurface. The investigation of hydraulic head and geology, together with InSAR-derived ground displacement data, indicates that the most subsidence occurs where there is a co-occurrence of high groundwater demand and a high percentage of fine-grained sediments, but recharge likely plays a role in mitigating subsidence in some areas. The subsidence developed in Parowan Valley shows a long-term trend as well as seasonal variation and appears to be influenced by both agricultural activity and annual precipitation.

Detecting Deforestation Using Logistic Analysis and Sentinel-1 Multitemporal Backscatter Data

This paper presents a new approach for detecting deforestation using Sentinel-1 C-band backscattering data. It is based on the temporal analysis of the backscatter intensity and its correlation with the scattering behavior of deforested plots. The backscatter intensity temporal variability is modeled with a logistic function, whose lower and upper boundaries are, respectively, set based on the representative backscatter values for forest and deforested plots. The approach also enables the identification of the date of each deforestation event, corresponding to the inflection point of the logistic curve that best fits the backscatter intensity time series. The methodology was applied to two forest biomes, a tropical forest at Iguazu National Park in Argentina and a temperate forest in the Brăila region in Romania. The optimal flattening parameter was 0.12 for both sites, with an F1-score of 0.93 and 0.71 for the tropical and temperate forests, respectively. The temporal accuracy shows a bias on the estimated date, with a slight delay of 2 months. The results reveal that the Sentinel C-band data can be successfully used for deforestation detection over tropical forests; however, the accuracy for temperate forests might be 20 pp lower, depending on the environmental conditions, such as rainfall, snow and management after logging.

On the Use of Satellite-Based Interferometry for Structural Monitoring of Bridge Portfolios

Transport network management authorities need to address targeted monitoring and structural assessment on appropriately selected critical bridges to improve the serviceability and safety of the network optimising the involved financial budgets and resources. The multi-temporal satellite-based differential interferometry, commonly referred to as MTInSAR, is a remote-sensing technique aimed at detecting displacements of coherent scatterers on the terrestrial surface with centimetre- to millimetre-level accuracy. This paper applies an MTInSAR-based methodology for the portfolio-scale evaluation of deformation phenomena on bridges, suitable for addressing targeted sensor-based monitoring or on-site inspection plans. The methodology is applied to a case-study highway network in Roma (Italy) by using Sentinel-1 (C-band) and COSMO-SkyMed (X-band) satellite datasets. The interferometry products are processed to identify bridge-specific deformation scenarios. The MTInSAR products and the deformation scenarios related to some specific test bridges subjected to subsidence phenomena are analysed to highlight the advantages and disadvantages of the investigated approach.

Exploring Convolutional Neural Networks for Predicting Sentinel-C Backscatter Between Image Acquisitions

Sentinel-1 C-band radar backscatter satellite images provide a repeating sequence of fine-resolution (10-m) observations that can be used for a number of applications, but the 12-day interval between satellite observations is too infrequent for many applications, such as measuring moisture dynamics. For a variety of applications, moisture information is demanded at high temporal frequency and fine spatial resolution over large areas. Machine learning approaches have been used to predict higher spatial resolutions than the original satellite images, but little effort has been made to increase the temporal resolution of acquired backscatter images. This study extends machine learning approaches to infer fine-resolution backscatter between observations relying on auxiliary data observations, including elevation and daily gridded weather. Several variations of Multi-modal Fully Convolutional Neural Network architectures, problem setup, and training methods are explored for a predominantly rural area in southwest Oklahoma near the transition between humid subtropical and semiarid climates. The training area lies in the overlap zone for adjacent Sentinel-1 satellite tracks, allowing for training with several different temporal offsets. We find that the UNET architecture produced the most accurate and robust estimated backscatter patterns, with superior prediction compared to a prior observation baseline in nearly all cases investigated when geography was included in the training data. This superior performance also generalized to nearby areas when training data for a given geography was not available, where 86% of predictions performed superior compared to a prior observation baseline.