Satellite Synthetic Aperture Radar Data Research Articles

Wide-Area Subsidence Monitoring and Analysis Using Time-Series InSAR Technology: A Case Study of the Turpan Basin

Ground subsidence often occurs over a large area. Although traditional monitoring methods have high accuracy, they cannot perform wide-area ground deformation monitoring. Synthetic Aperture Radar (SAR) interferometry (InSAR) technology utilizes phase information in SAR images to extract surface deformation information in a low-cost, large-scale, high-precision, and high-efficiency manner. With the increasing availability of SAR satellite data and the rapid development of InSAR technology, it provides the possibility for wide-area ground deformation monitoring using InSAR technology. Traditional time-series InSAR methods have cumbersome processing procedures, have large computational requirements, and rely heavily on manual intervention, resulting in relatively low efficiency. This study proposes a strategy for wide-area InSAR multi-scale deformation monitoring to address this issue. The strategy first rapidly acquires ground deformation information using Stacking technology, then identifies the main subsidence areas by setting deformation rate thresholds and visual interpretation, and finally employs advanced TS-InSAR technology to obtain detailed deformation time series for the main subsidence areas. The Turpan Basin in Xinjiang, China, was selected as the study area (7474.50 km2) to validate the proposed method. The results are as follows: (1) The basin is generally stable, but there is ground subsidence in the southern plain area, mainly affecting farmland. (2) From 2016 to 2019, the maximum subsidence rate in the farmland area was approximately 0.13 m/yr, with a maximum cumulative subsidence of about 0.25 m, affecting a total area of approximately 952.49 km2. The subsidence mainly occurred from late spring to mid-autumn, while lifting or subsidence mitigation occurred from late autumn to early spring. The study also analyzed the impacts of rainfall, geographical environment, and human activities on subsidence and found that multiple factors, including water resource reduction, overexploitation, geological characteristics, and the expansion of human activities, contributed to the subsidence problem in the Turpan Basin. This method contributes to wide-area InSAR deformation monitoring and the application of InSAR technology in engineering.

Using SAR-based products to calculate potato carbon uptake in a tropical Andean region

ABSTRACT Gross primary productivity (GPP) is an essential parameter to estimate the efficiency of carbon transfer in terrestrial ecosystems. The daily GPP has been monitored in the past using mainly optical satellite imagery. However, GPP has never been monitored using satellite Synthetic Aperture Radar (SAR) imagery. We evaluate the possibility of using Sentinel-1 SAR data to estimate GPP and compare with field GPP measurements using eddy covariance (EC) systems in three commercial potato fields, located in the Andean region of Colombia, using three different water irrigation levels. Raw and processed radar data from Sentinel-1 were compared with the daily and accumulated GPP from the EC. Results indicate that SAR data has a high correlation with the accumulated GPP measured by the EC in the field. The normalized radar backscatter and radar brilliance coefficients with VH polarization show a good correlation with the EC accumulated GPP in irrigated potato fields (R2: 0.77–0.81), while radar vegetation indices show a good correlation with the EC accumulated GPP in potato fields with no irrigation (R2≈0.82). In particular, the accumulated GPP during the whole potato crop cycle was estimated with good accuracy (~5% error with irrigation and ~10% error with no irrigation).

Lead fractions from SAR-derived sea ice divergence during MOSAiC

Abstract. Leads and fractures in sea ice play a crucial role in the heat and gas exchange between the ocean and atmosphere, impacting atmospheric, ecological, and oceanic processes. We estimated lead fractions from high-resolution divergence obtained from satellite synthetic aperture radar (SAR) data and evaluated them against existing lead products. We derived two new lead fraction products from divergence with a spatial resolution of 700 m calculated from daily Sentinel-1 images. For the first lead product, we advected and accumulated the lead fractions of individual time instances. With those accumulated divergence-derived lead fractions, we comprehensively described the presence of up to 10 d old leads and analyzed their deformation history. For the second lead product, we used only divergence pixels that were identified as part of linear kinematic features (LKFs). Both new lead products accurately captured the formation of new leads with widths of up to a few hundred meters. We presented a Lagrangian time series of the divergence-based lead fractions along the drift of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic Ocean during winter 2019–2020. Lead activity was high in fall and spring, consistent with wind forcing and ice pack consolidation. At larger scales of 50–150 km around the MOSAiC expedition, lead activity on all scales was similar, but differences emerged at smaller scales (10 km). We compared our lead products with six others from satellite and airborne sources, including classified SAR, thermal infrared, microwave radiometer, and altimeter data. We found that the mean lead fractions varied by 1 order of magnitude across different lead products due to different physical lead and sea ice properties observed by the sensors and methodological factors such as spatial resolution. Thus, the choice of lead product should align with the specific application.

REMOTE SENSING DATA APPLICATION TO MONITOR CARBON FARMING PRACTICES

Abstract. Carbon farming, a crucial strategy in mitigating climate change and promoting sustainable agriculture, requires precise monitoring to assess its effectiveness. This study explores the transformative potential of remote sensing data, with a focus on the fusion of Multispectral and Synthetic Aperture Radar satellite data, to enhance the precision and efficiency of carbon farming monitoring. Our research addresses the fundamental question: How can remote sensing data optimize the monitoring of carbon farming practices? This question drives our investigation into the practical applications of remote sensing technology in the context of carbon farming. In this article, the research is carried out in Lithuania, which is often covered with clouds or their shadows, so the application of various satellite images becomes even more meaningful. The study shows that the use of SAR image fusion for the identification of permanent meadows is appropriate and meaningful. The use of MSI image fusion for the identification of intermediate crops and stubble is also appropriate, but more research is needed that focuses on distinguishing these practices from other spectrally very similar practices.

Improving the Estimation of Rice Crop Damage from Flooding Events Using Open-Source Satellite Data and UAV Image Data

Having an additional tool for swiftly determining the extent of flood damage to crops with confidence is beneficial. This study focuses on estimating rice crop damage caused by flooding in Candaba, Pampanga, using open-source satellite data. By analyzing the correlation between Normalized Difference Vegetation Index (NDVI) measurements from unmanned aerial vehicles (UAVs) and Sentinel-2 (S2) satellite data, a cost-effective and time-efficient alternative for agricultural monitoring is explored. This study comprises two stages: establishing a correlation between clear sky observations and NDVI measurements, and employing a combination of S2 NDVI and Synthetic Aperture Radar (SAR) NDVI to estimate crop damage. The integration of SAR and optical satellite data overcomes cloud cover challenges during typhoon events. The accuracy of standing crop estimation reached up to 99.2%, while crop damage estimation reached up to 99.7%. UAVs equipped with multispectral cameras prove effective for small-scale monitoring, while satellite imagery offers a valuable alternative for larger areas. The strong correlation between UAV and satellite-derived NDVI measurements highlights the significance of open-source satellite data in accurately estimating rice crop damage, providing a swift and reliable tool for assessing flood damage in agricultural monitoring.

InSAR coseismic deformation field and seismogenic structure of the 2020 Mw6.0 Jiashi earthquake and the implication for the moderate-magnitude seismicity in the southwestern Tian Shan, western China

The Kepingtage fold-and-thrust belt in the southwestern Tian Shan in western China hosted the 2020 Mw 6.0 Jiashi earthquake with no apparent surface ruptures. The thrust nappe structure in this region is characterized by moderate-magnitude (Mw5.5-6.5) seismicity, but the seismogenic mechanisms and controlling factors remain under investigation. In this study, we utilized Sentinel-1A synthetic aperture radar satellite data to reconstruct the InSAR coseismic deformation field of the 2020 Jiashi earthquake. To address the limitation imposed by residual orbital phases during the interferometric measurement, we proposed a novel automatic method that combines ascending and descending track data with terrain features for orbit refinement. Eight comparative tests were conducted to prove the effectiveness of the proposed method. Subsequently, we inverted the jointly constrained deformation field after orbit correction to obtain the fault geometric parameters and slip distribution. Our results show that the 2020 Jiashi earthquake is characterized by right-lateral transpressive motion. The smooth interference fringes demonstrate spatially continuous surface uplift and subsidence without detectable coseismic surface ruptures, with a maximum uplift of ∼0.08 m and a maximum subsidence of ∼0.03 m, caused by the subsurface folding due to deep seismic rupture. This event is best fitted by a north-dipping fault plane with a depth of 4.2 km, a dip angle of 11.6°, and a strike of 276° beneath the Keping thrust fault. In terms of various geometric parameters of the fault, the inversion results of this study are generally similar to the focal mechanism solution provided by USGS (MWb), but are different from the focal mechanism solutions of other institutions and previous research results. Combined with the published geological investigations and seismic reflection surveys, we suggest that the seismogenic structure of the 2020 Jiashi earthquake is the lower ramp of the Keping thrust fault and the abrupt fault bend between the lower and upper ramp may limit the propagation of the coseismic rupture to the surface. The limited rupture of this event is dominated by the irregularities in fault geometry along strike and dip, as well as the lower rock strength of the cover above the detachment, which contribute to a deeper understanding of the seismic behavior in fold-and-thrust belts and the moderate-magnitude seismicity in the southwestern Tian Shan region.

Evaluation of U-Net transfer learning model for semantic segmentation of landslides in the Colombian tropical mountain region

Landslides in tropical regions, like the Colombian Andean region, pose unique challenges due to factors such as intense rainfall, steep slopes, and complex terrains. Mapping historical and current landslide activity through inventory maps is essential in tropical mountainous regions. While satellite data is commonly used for mapping, it can be time-consuming and manual-intensive, limiting inventory availability. Deep Learning (DL) models, especially Convolutional Neural Networks (CNNs), have shown promise in remote sensing applications with High Resolution (HR) imagery, including landslide detection. Despite advancements, their use in this field is still relatively limited. This study assesses the effectiveness of U-Net model, for automated landslide detection using spectral data from optical satellite imagery (RGB bands), two DEM-derived geo-indices (slope and curvature), and two Synthetic Aperture Radar (SAR) layers (VV amplitude pre- and post-landslide event in May 2015) across three image models (3, 5, and 7 bands). Initially, data is combined into multiband images, and the model is trained in the “La Argelia” river basin in Colombia’s Pacific region. Subsequently, the model is tested in the “La Liboriana” river basin in the western Andean region. The landslide detection results within the inference area are validated by comparing them with the landslide inventory and segmentation results. The U-Net model demonstrates good performance (F1-score around 0.70) for landslide detection, as confirmed in various geographical settings. By utilizing DL models and combining high-resolution satellite imagery, topographical, and SAR data, a comprehensive space-time mapping of landslides can be achieved. This approach has the potential to greatly improve the accuracy and effectiveness of landslide mapping, offering a more holistic view of the temporal dynamics related to these natural hazards.

Two Mw ≥ 6.5 Earthquakes in Central Pamir Constrained by Satellite SAR Observations

The Pamir, situated in central Asia, is a result of the ongoing northward advance of the Indian continent, leading to compression of the Asian landmass. While geodetic and seismic data typically indicate that the most significant deformation in Pamir is along its northern boundary, an Mw 7.2 earthquake on 7 December 2015 and an Mw 6.8 earthquake on 23 February 2023 have occurred in the remote interior of Pamir. These two Mw ≥ 6.5 earthquakes, with good observations of satellite synthetic aperture radar data, provide a rare opportunity to gain insights into rupture mechanics and deformation patterns in this challenging-to-reach region. Here, we utilize spaceborne synthetic aperture radar data to determine the seismogenic faults and finite slip models for these two earthquakes. Our results reveal that the 2015 earthquake ruptured a ~88 km long, left-lateral strike-slip fault that dips to northwest. The rupture of the 2015 earthquake extended to the ground surface over a length of ~50 km with a maximum slip of ~3.5 m. In contrast, the 2023 earthquake did not rupture the ground surface, with a maximum slip of ~2.2 m estimated at a depth of ~9 km. Notably, the seismogenic fault of the 2015 earthquake does not align with the primary strand of the Sarez–Karakul fault system (SKFS), and the 2023 earthquake occurred on a previously unmapped fault. The well-determined seismogenic faults for the 2015 and 2023 earthquakes, along with the SKFS and other distributed faults in the region, suggest the existence of a wide shear zone extending from south to north within the central Pamir.

Multi-source data analysis to assess the past and present kinematics of the Pisciotta Deep-Seated Gravitational Slope Deformation (southern Italy)

Although Deep-Seated Gravitational Slope Deformations are well-known in the literature, their evolution and kinematics are still poorly understood. Their behavior is often complex and characterized by small movements associated with steady-state creep, alternating with periods of stasis, or accelerating downslope movements that, in some cases, could result in sudden and catastrophic failure events. Therefore, a multidisciplinary approach is often required. In this work, we shed light on the complex geometry and kinematics of the Pisciotta DSGSD, a deep-seated roto-translational sliding involving structurally complex turbiditic rock mass and interacting with man-made infrastructures. To reveal the geometrical features and the spatial and temporal behavior of the analyzed phenomenon, a multidisciplinary investigation was performed. Typical DSGSD landforms were mapped employing in-situ surveys, aided by stereoscopic analysis of historical aerial images and high-resolution drone-based mapping. Structural data and ancillary ground-based surveys revealed the presence of a highly weathered and folded turbiditic sequence, with competent sandstone and calcarenite units alternated by tectonically disrupted, weak argillite and mudrock layers. Remote sensing measurements from optical imagery and Synthetic Aperture Radar satellite data assessed the DSGSD's past and current kinematics, allowing to distinguish a pre-failure period with accelerating displacement rates, a failure period with maximum displacement rates, and a current post-failure period with decelerating displacement rates. Analytical modeling established the deep reach (up to 85 m) of the studied DSGSD as it allowed the estimation of its bottom surface and volume, as verified by available boreholes and inclinometric measurements. Furthermore, numerical modeling outcomes highlighted how the progressive weakening and alteration of the DSGSD material, in conjunction with changes in groundwater dynamics, serve as the primary mechanisms driving the observed kinematics. The models also revealed the intricate interaction between the DSGSD and the neighboring infrastructures.

Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen

Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen

Temporal Changes in Mediterranean Pine Forest Biomass Using Synergy Models of ALOS PALSAR-Sentinel 1-Landsat 8 Sensors

Currently, climate change requires the quantification of carbon stored in forest biomass. Synthetic aperture radar (SAR) data offers a significant advantage over other remote detection measurement methods in providing structural and biomass-related information about ecosystems. This study aimed to develop non-parametric Random Forest regression models to assess the changes in the aboveground forest biomass (AGB), basal area (G), and tree density (N) of Mediterranean pine forests by integrating ALOS-PALSAR, Sentinel 1, and Landsat 8 data. Variables selected from the Random Forest models were related to NDVI and optical textural variables. For 2015, the biomass models with the highest performance integrated ALS-ALOS2-Sentinel 1-Landsat 8 data (R2 = 0.59) by following the model using ALS data (R2 = 0.56), and ALOS2-Sentinel 1-Landsat 8 (R2 = 0.50). The validation set showed that R2 values vary from 0.55 (ALOS2-Sentinel 1-Landsat 8) to 0.60 (ALS-ALOS2-Sentinel 1-Landsat 8 model) with RMSE below 20 Mg ha−1. It is noteworthy that the individual Sentinel 1 (R2 = 0.49). and Landsat 8 (R2 = 0.47) models yielded equivalent results. For 2020, the AGB model ALOS2-Sentinel 1-Landsat 8 had a performance of R2 = 0.55 (validation R2 = 0.70) and a RMSE of 9.93 Mg ha−1. For the 2015 forest structural variables, Random Forest models, including ALOS PAL-SAR 2-Sentinel 1 Landsat 8 explained between 30% and 55% of the total variance, and for the 2020 models, they explained between 25% and 55%. Maps of the forests’ structural variables were generated for 2015 and 2020 to assess the changes during this period using the ALOS PALSAR 2-Sentinel 1-Landsat 8 model. Aboveground biomass (AGB), diameter at breast height (dbh), and dominant height (Ho) maps were consistent throughout the entire study area. However, the Random Forest models underestimated higher biomass levels (>100 Mg ha−1) and overestimated moderate biomass levels (30–45 Mg ha−1). The AGB change map showed values ranging from gains of 43.3 Mg ha−1 to losses of −68.8 Mg ha−1 during the study period. The integration of open-access satellite optical and SAR data can significantly enhance AGB estimates to achieve consistent and long-term monitoring of forest carbon dynamics.

ГЕОДИНАМІКА

The aim of this research is the comparison and subsequent evaluation of the suitability of using SAR (Synthetic Aperture Radar) and multispectral (MSI) satellite data of the Copernicus program for mapping and accurate identification of surface water bodies. The paper considers sudden changes caused by significant climatological-meteorological influences in the country. The surface guidance extraction methodology includes the standard preprocessing of SAR images and concluding the determination of threshold values in binary mask generation. For MSI images, water masks are generated through automatic algorithmic processing on the Google Earth Engine cloud platform. During SAR image processing, it has been found that the VV polarization configuration type (vertical-vertical) is the most suitable. The Lee and Lee Sigma filters are recommended for eliminating radar noise. The chosen window size for filtering depends on the specific object and its spatial extent. The extraction of water surfaces from the MSI image is conducted using the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), a pair of Automated Water Extraction Index (AWEI) indices, and Water Ratio Index (WRI). Results are evaluated both graphically and numerically, using quantitative accuracy indicators to refine them. Automatic extraction of water surfaces from MSI images in the GEE platform environment is a fast, efficient, and relatively accurate tool for determining the true extent of groundwater. In conclusion, this research can provide more reliable estimates of hydrological changes and interannual variations in water bodies in the country. When combined with multitemporal monitoring, these results can be an effective tool for permanent monitoring of floods and droughts.The aim of this research is the comparison and subsequent evaluation of the suitability of using SAR (Synthetic Aperture Radar) and multispectral (MSI) satellite data of the Copernicus program for mapping and accurate identification of surface water bodies. The paper considers sudden changes caused by significant climatological-meteorological influences in the country. The surface guidance extraction methodology includes the standard preprocessing of SAR images and concluding the determination of threshold values in binary mask generation. For MSI images, water masks are generated through automatic algorithmic processing on the Google Earth Engine cloud platform. During SAR image processing, it has been found that the VV polarization configuration type (vertical-vertical) is the most suitable. The Lee and Lee Sigma filters are recommended for eliminating radar noise. The chosen window size for filtering depends on the specific object and its spatial extent. The extraction of water surfaces from the MSI image is conducted using the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), a pair of Automated Water Extraction Index (AWEI) indices, and Water Ratio Index (WRI). Results are evaluated both graphically and numerically, using quantitative accuracy indicators to refine them. Automatic extraction of water surfaces from MSI images in the GEE platform environment is a fast, efficient, and relatively accurate tool for determining the true extent of groundwater. In conclusion, this research can provide more reliable estimates of hydrological changes and interannual variations in water bodies in the country. When combined with multitemporal monitoring, these results can be an effective tool for permanent monitoring of floods and droughts.

Synergistic Integration of Time Series Optical and SAR Satellite Data for Mariculture Extraction

Mariculture is an important part of aquaculture, and it is important to address global food security and nutrition issues. However, seawater environmental conditions are complex and variable, which causes large uncertainties in the remote sensing spectral features. At the same time, mariculture types are distinct because of the different types of aquaculture (cage aquaculture and raft aquaculture). These factors bring great challenges for mariculture extraction and mapping using remote sensing. In order to solve these problems, an optical remote sensing aquaculture index named the marine aquaculture index (MAI) is proposed. Based on this spectral index, using time series Sentinel-1 and Sentinel-2 satellite data, a random forest classification scheme is proposed for mapping mariculture by combining spectral, textural, geometric, and synthetic aperture radar (SAR) backscattering. The results revealed that (1) MAI can emphasize the difference between mariculture and seawater; (2) the overall accuracy of mariculture in the Bohai Rim is 94.10%, and the kappa coefficient is 0.91; and (3) the area of cage aquaculture and raft aquaculture in the Bohai Rim is 16.89 km2 and 1206.71 km2, respectively. This study details an effective method for carrying out mariculture monitoring and ensuring the sustainable development of aquaculture.

Flood Inundation Mapping with Supervised Classifiers: 2021 Gediz Plain Flood

Generation of flood inundation maps is beneficial in flood risk assessment and evaluation. Flood inundation mapping can be achieved by many remote sensing techniques like change detection (CD) with thresholding and machine learning-based (ML) methods. Optical and synthetic aperture radar (SAR) imagery are widely used, provided by different satellite systems. This study used Sentinel-1 SAR and Sentinel-2 MSI satellite data in Google Earth Engine (GEE) with supervised ML algorithms. Gediz Plain, Turkey was selected as the study area, which is an agricultural area covered mostly by croplands. A flood event that occurred on February 2, 2021, was examined and flood inundation map for the study area was composed. Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighbor (KNN) ML algorithms were selected and models were trained with manually created labelled data in GEE. Also, CD was applied on after and before event SAR images in a traditional approach. RF classifier performs best in Sentinel-2 MSI imagery with 94% overall classification accuracy where KNN classifier gives 93.3% accuracy value for Sentinel-1 SAR dataset, indicating the robustness of SAR imagery for all-weather conditions.

Fusion of VNIR Optical and C-Band Polarimetric SAR Satellite Data for Accurate Detection of Temporal Changes in Vegetated Areas

In this paper, we propose a processing chain jointly employing Sentinel-1 and Sentinel-2 data, aiming to monitor changes in the status of the vegetation cover by integrating the four 10 m visible and near-infrared (VNIR) bands with the three red-edge (RE) bands of Sentinel-2. The latter approximately span the gap between red and NIR bands (700 nm–800 nm), with bandwidths of 15/20 nm and 20 m pixel spacing. The RE bands are sharpened to 10 m, following the hyper-sharpening protocol, which holds, unlike pansharpening, when the sharpening band is not unique. The resulting 10 m fusion product may be integrated with polarimetric features calculated from the Interferometric Wide (IW) Ground Range Detected (GRD) product of Sentinel-1, available at 10 m pixel spacing, before the fused data are analyzed for change detection. A key point of the proposed scheme is that the fusion of optical and synthetic aperture radar (SAR) data is accomplished at level of change, through modulation of the optical change feature, namely the difference in normalized area over (reflectance) curve (NAOC), calculated from the sharpened RE bands, by the polarimetric SAR change feature, achieved as the temporal ratio of polarimetric features, where the latter is the pixel ratio between the co-polar and the cross-polar channels. Hyper-sharpening of Sentinel-2 RE bands, calculation of NAOC and modulation-based integration of Sentinel-1 polarimetric change features are applied to multitemporal datasets acquired before and after a fire event, over Mount Serra, in Italy. The optical change feature captures variations in the content of chlorophyll. The polarimetric SAR temporal change feature describes depolarization effects and changes in volumetric scattering of canopies. Their fusion shows an increased ability to highlight changes in vegetation status. In a performance comparison achieved by means of receiver operating characteristic (ROC) curves, the proposed change feature-based fusion approach surpasses a traditional area-based approach and the normalized burned ratio (NBR) index, which is widespread in the detection of burnt vegetation.

Surface Water Mapping from SAR Images Using Optimal Threshold Selection Method and Reference Water Mask

Water resources are an important component of ecosystem services. During long periods of cloudiness and precipitation, when a ground-based sample is not available, the water bodies are detected from satellite SAR (synthetic-aperture radar) data using threshold methods (e.g., Otsu and Kittler–Illingworth). However, such methods do not enable to obtain the correct threshold value for the backscattering coefficient (σ0) of relatively small water areas in the image. The paper proposes and substantiates a method for the mapping of the surface of water bodies, which makes it possible to correctly identify water bodies, even in “water”/“land” class imbalance situations. The method operates on a principle of maximum compliance of the resulting SAR water mask with a given reference water mask. Therefore, the method enables the exploration of the possibilities of searching and choosing the optimal parameters (polarization and speckle filtering), which provide the maximum quality of SAR water mask. The method was applied for mapping natural and industrial water bodies in the Pohjois-Pohjanmaa region (North Ostrobothnia), Finland, using Sentinel-1A and -1B ground range detected (GRD) data (ascending and descending orbits) in 2018–2021. Reference water masks were generated based on optical spectral indices derived from Sentinel-2A and -2B data. The polarization and speckle filtering parameters were chosen since they provide the most accurate σ0 threshold (on average for all observations above 0.9 according to the Intersection over Union criterion) and are resistant to random fluctuations. If a reference water mask is available, the proposed method is more accurate than the Otsu method. Without a reference mask, the σ0 threshold is calculated as an average of thresholds obtained from previous observations. In this case, the proposed method is as good in accuracy as the Otsu method. It is shown that the proposed method enables the identification of surface water bodies under significant class imbalance conditions, such as when the water surface covers only a fraction of a percent of the area under study.

Tracking transient boreal wetland inundation with Sentinel-1 SAR: Peace-Athabasca Delta, Alberta and Yukon Flats, Alaska

ABSTRACT Accurate and frequent mapping of transient wetland inundation in the boreal region is critical for monitoring the ecological and societal functions of wetlands. Satellite Synthetic Aperture Radar (SAR) has long been used to map wetlands due to its sensitivity to surface inundation and ability to penetrate clouds, darkness, and certain vegetation canopies. Here, we track boreal wetland inundation by developing a two-step modified decision-tree algorithm implemented in Google Earth Engine using Sentinel-1 C-band SAR and Sentinel-2 Multispectral Instrument (MSI) time-series data as inputs. This approach incorporates temporal as well as spatial characteristics of SAR backscatter and is evaluated for the Peace-Athabasca Delta, Alberta (PAD), and Yukon Flats, Alaska (YF) from May 2017 to October 2019. Within these two boreal study areas, we map spatiotemporal patterns in wetland inundation classes of Open Water (OW), Floating Plants (FP), Emergent Plants (EP), and Flooded Vegetation (FV). Temporal variability, frequency, and maximum extents of transient wetland inundation are quantified. Retrieved inundation estimates are compared with in-situ field mapping obtained during the NASA Arctic-Boreal Vulnerability Experiment (ABoVE), and a multi-temporal Landsat-derived surface water map. Over the 2017–2019 study period, we find that fractional inundation area ranged from 18.0% to 19.0% in the PAD, and from 10.7% to 12.1% in the YF. Transient wetland inundation covered ~595 km2 of the PAD, comprising ~9.1% of its landscape, and ~102 km2 of the YF, comprising ~3.6%. The implications of these findings for wetland function monitoring, and estimating landscape-scale methane emissions are discussed, together with limitations and uncertainties of our approach. We conclude that time series of Sentinel-1 C-band SAR backscatter, screened with Sentinel-2 MSI optical imagery and validated by field measurements, offer a valuable tool for tracking transient boreal wetland inundation.

Exploring the potential of Sentinel-3 delay Doppler altimetry for enhanced detection of coastal currents along the Northwest Atlantic shelf

In this study, we evaluate Sentinel-3A satellite synthetic aperture radar (SAR) altimeter observations along the Northwest Atlantic coast, spanning the Nova Scotian Shelf, Gulf of Maine, and Mid-Atlantic Bight. Comparisons are made of altimeter sea surface height (SSH) measurements from three different altimeter data processing approaches: fully-focused synthetic aperture radar (FFSAR), un-focused SAR (UFSAR), and conventional low-resolution mode (LRM). Results show that fully-focused SAR data always outperform LRM data and are comparable or slightly better than the nominal un-focused SAR product. SSH measurement noise in both SAR-mode datasets is significantly reduced compared to LRM. FFSAR SSH 20-Hz noise levels, derived from 80-Hz FFSAR data, are lower than 20-Hz UFSAR SSH with 25% noise reduction offshore of 5 km, and 55–70% within 5 km of the coast. The offshore noise improvement is most likely due to the higher native along-track data posting rate (80 Hz for FFSAR, and 20 Hz for UFSAR), while the large coastal improvement indicates an apparent FFSAR data processing advantage approaching the coastlines. FFSAR-derived geostrophic ocean current estimates exhibit the lowest bias and noise when compared to in situ buoy-measured currents. Assessment at short spatial scales of 5–20 km reveals that Sentinel-3A SAR data provide sharper and more realistic measurement of small-scale sea surface slopes associated with expected nearshore coastal currents and small-scale gyre features that are much less well resolved in conventional altimetric LRM data.

Spatial Rice Yield Estimation Using Multiple Linear Regression Analysis, Semi-Physical Approach and Assimilating SAR Satellite Derived Products with DSSAT Crop Simulation Model

Accurate and consistent information on the area and production of field crops is vital for national and state planning and ensuring food security in India. Satellite-based remote sensing offers a suitable and cost-effective technique for regional- and national-scale crop monitoring. The use of remote sensing data for crop yield estimation has been demonstrated using a semi-physical approach with reasonable success. Assimilating remote sensing data with the DSSAT model and spectral indices-based regression analysis are promising methods for spatially estimating rice crop yields. Rice area and yield in the Cauvery delta zone of Tamil Nadu, India was estimated during samba (August–January) season in the years 2020–2021 using Sentinel 1A Synthetic Aperture Radar satellite data with three different spatial yield estimation methods, namely a spectral indices-based regression analysis, semi-physical approach, and integrating remote products with DSSAT crop growth model. A rice area map was generated for the study area using a rule-based classifier approach utilizing parameterization with a classification accuracy of 94.5% and a kappa score of 0.89. The total classified rice area in Cauvery Delta Region was 379,767 ha, and the Start of Season (SoS) maps for samba season revealed that the major planting period for rice was between 22 September and 9 November in 2020. The study also aimed to identify promising spatial yield estimation techniques for optimal rice yield prediction over large areas. Regression models resulted in rice yields of 3234 to 3905 kg ha−1 with a mean of 3654 kg ha−1. The net primary product was computed using the periodical PAR, fAPAR, Wstress, Tstress, and maximum radiation use efficiency in a semi-physical approach. The resultant rice yields ranged between 2652 and 3438 kg ha−1 with the mean of 3076 kg ha−1. During the integration of remote sensing products with crop growth models, LAI values were extracted from dB images and utilized to simulate rice yields in the range of 3684 to 4012 kg ha−1 with the mean of 3855 kg ha−1. When compared to the semi-physical approach, both integrating remote sensing products with the DSSAT crop growth model and spectral indices-based regression analysis had R2 greater than 0.80, NRMSE of less than 10%, and agreement of more than 90%, indicating that these two approaches could be used for spatial rice yield estimation.

Polarimetric SAR pixel offset tracking for large-gradient landslide displacement mapping

Interferometry and pixel offset tracking (POT) are two major technologies to measure ground deformation with repeat-pass synthetic aperture radar (SAR) observations. The SAR POT method can tolerate a much larger deformation gradient than interferometric SAR (InSAR), and thus it has been widely applied in monitoring earthquakes, volcanoes, landslides, glaciers, etc. However, there is an intrinsic trade-off between matching accuracy and detail preserving in terms of matching window size for the traditional SAR POT method using only single-polarization intensity. Taking advantage of polarimetric scattering properties, this study develops a novel POT method, named PCM-POT, towards mitigating the dependence of offset estimations on matching window size. It employs a generalized likelihood ratio test (GLRT) upon polarimetric covariance matrices (PCMs) to evaluate the similarity of two candidate pixels and estimate high-resolution surface displacements with high accuracy. The effectiveness and advantage of PCM-POT are demonstrated by an experiment of retrieving the displacements of the Slumgullion landslide from a pair of full-polarimetric UAVSAR images. Comparisons with the traditional POT indicate that using polarimetric scattering properties can reduce the uncertainty in offset estimations over heterogeneous sliding areas. Further experiments suggest that PCM-POT is also suitable for dual-polarimetric satellite SAR data.