L-band Synthetic Aperture Radar Research Articles

The ABoVE L-band and P-band airborne synthetic aperture radar surveys

Abstract. Permafrost-affected ecosystems of the Arctic–boreal zone in northwestern North America are undergoing profound transformation due to rapid climate change. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) is investigating characteristics that make these ecosystems vulnerable or resilient to this change. ABoVE employs airborne synthetic aperture radar (SAR) as a powerful tool to characterize tundra, taiga, peatlands, and fens. Here, we present an annotated guide to the L-band and P-band airborne SAR data acquired during the 2017, 2018, 2019, and 2022 ABoVE airborne campaigns. We summarize the ∼80 SAR flight lines and how they fit into the ABoVE experimental design (Miller et al., 2023; https://doi.org/10.3334/ORNLDAAC/2150). The Supplement provides hyperlinks to extensive maps, tables, and every flight plan as well as individual flight lines. We illustrate the interdisciplinary nature of airborne SAR data with examples of preliminary results from ABoVE studies including boreal forest canopy structure from TomoSAR data over Delta Junction, AK, and the Boreal Ecosystem Research and Monitoring Sites (BERMS) area in northern Saskatchewan and active layer thickness and soil moisture data product validation. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band airborne SAR data (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl).

Analysis of the relationship between L-band SAR backscatter and understory weed density in eucalyptus plantation forests

ABSTRACT This study aims to reveal the relationships between understory coverage (principally, weed growth) beneath the canopies of Sumatran eucalyptus plantations and L-band backscatter data gathered by the PALSAR-2 Synthetic Aperture Radar (SAR) onboard the ALOS-2 satellite. Statistical analysis in conjunction with in situ measurements of forest structural parameters reveals that the SAR polarization ratio is significantly negatively correlated with understory coverage in forests taller than or equal to 10 m or above 2 years of age. Our field investigations confirm that a decrease in canopy coverage takes place some 2 years after transplanting, which in turn permits increased penetration (decreased attenuation) of microwave signals thereby enabling the SAR to detect changes in understory coverage. In addition, both growth and unmanaged remnants of the understory vegetation in older forests reduce evaporation from the ground, which in turn stabilizes soil-moisture levels throughout the year and allows co-polarized backscatter data (which might otherwise be dominated by soil-moisture changes) to contain significant information on the underlying vegetation. No correlation between the understory and SAR backscatter was recorded in younger forests undergoing programs of intensive weed management. This is presumably because strong canopy and trunk backscatter obscured the response from understory vegetation and because artificial changes were induced in vegetation water content as a result of the application of herbicides which in turn reduced the accuracy of some of our field measurements of understory coverage. This research nevertheless demonstrates an important potential application of active satellite microwave remote sensing. We show that satellite-based L-band SAR can be used as a tool to investigate coverage percentages of understory layers. This synoptic technique reduces the need for repeated field surveys across large areas of forest. It can enhance ecosystem assessment and improve understory coverage estimates in sparse forests and in various types of plantations.

The BELSAR dataset: Mono- and bistatic full-pol L-band SAR for agriculture and hydrology

The BELSAR dataset consists of high-resolution multitemporal airborne mono- and bistatic fully-polarimetric synthetic aperture radar (SAR) data in L-band, alongside concurrent measurements of vegetation and soil biogeophysical variables measured in maize and winter wheat fields during the summer of 2018 in Belgium. Its collection was funded by the European Space Agency (ESA) to address the lack of publicly-accessible experimental datasets combining multistatic SAR and in situ measurements. As such, it offers an opportunity to advance the development of SAR remote sensing science and applications for agricultural monitoring and hydrology. This paper aims to facilitate its adoption and exploration by offering comprehensive documentation and integrating its multiple data sources into a unified, analysis-ready dataset.

LT-1 SAR Satellite Constellation for Permafrost Deformation Monitoring Along the Tibetan Plateau Engineering Corridor

Abstract. The Tibetan Plateau stands as one of China's largest middle and low latitude permafrost regions. However, the effects of global warming and human activities have led to permafrost thawing, inducing surface instability and posing significant threats to infrastructure and indigenous communities. The deployment of Lu Tan-1 (LT-1), China's premier L-band synthetic aperture radar (SAR) satellite constellation, offers a novel opportunity to assess these changes. This paper evaluates the deformation of critical engineering corridors, such as the Qinghai-Tibet Railway (QTR) and the Qinghai-Tibet Highway (QTH), utilizing time-series InSAR techniques with LT-1 SAR constellation data. We introduce both Stacking InSAR and a multi-baseline persistent scatterer multitemporal (MT-InSAR) method to enhance permafrost and engineering corridor deformation detection capabilities. Results obtained through the MT-InSAR approach reveal line-of-sight (LOS) deformation velocities of permafrost in the Beiluhe region ranging from -90 mm/y to approximately 70 mm/y, with an average velocity amplitude of 0.06 m/y. Differential displacement between alpine meadows and alpine deserts across the Beiluhe region is successfully discerned using LT-1 SAR data. Deformation velocities of QTR, QTH were found to be lower than that of permafrost, with average velocities of 0.027 m/y. These findings underscore the LT-1 SAR constellation's potential to serve as a valuable SAR data source for monitoring engineering corridor deformation within the Tibetan Plateau permafrost region.

Three-dimensional forest structure features extraction based on TomoSAR

Abstract. Forest structure is an important indicator for evaluating forest ecosystem and an indispensable input parameter for estimating forest carbon sink. Synthetic Aperture Radar (SAR) Tomography (TomoSAR), as a novel microwave remote sensing technique with 3D imaging capability, has been demonstrated to be an valuable tool for accurate inversion of forest structure. At present, the forest vertical structure parameters inversion is almost all concentrated on the forest height estimation. However, the spatial structure of forests is complex, and it is not enough to only use the forest height to express the forest structure. This fails to identify tree canopy, branch layer and underlying vegetation layer, etc., resulting in the partial and inaccurate forest vertical structure information. To solve this problem, this paper constructs 3D structural indices from tomograms, namely Horizontal Structure Index (HS) and Vertical Structure Index (VS). The HS primarily describes canopy density, compensating for the lack of information on the horizontal dimension of forest structure provided by traditional TomoSAR techniques; while VS describes the complexity of canopy distribution in the altitude direction, further enriching information on forest structure vertically. In order to verify their feasibility and validity, HS and VS are extracted using six fully-polarized L-band SAR images covering the Krycklan watershed in Sweden. Moreover, the accuracy is verified by using external high-precision LiDAR data. The results indicate a strong correlation between the structural indices extracted from TomoSAR and those extracted from LiDAR.

Potential Landslide Identification in Baihetan Reservoir Area Based on C-/L-Band Synthetic Aperture Radar Data and Applicability Analysis

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results.

Comparison of Synthetic Aperture Radar Sentinel-1 and ALOS-2 observations for lake monitoring

This work investigates the efficacy of L-band and C-band Synthetic Aperture Radar (SAR) sensors onboard ALOS-2 and Sentinel-1 satellites, as compared to optical sensors onboard Sentinel-2 satellite, for mapping open water of the Tri An reservoir, one of the largest artificial reservoirs in South Vietnam, during the 2016-2023 period. The Google Earth Engine (GEE) was the primary computing platform to pre-process all satellite observations. The Otsu threshold algorithm was employed to generate water/non-water maps derived from the VH- and HH-polarized backscatter coefficient data acquired by Sentinel-1 and ALOS-2 satellites and from the Modified Normalized Difference Water Index (MNDWI) data acquired by Sentinel-2 satellite, respectively. The findings reveal the stability of Tri An reservoir’s surface water extent from 2017 to 2022, followed by a significant decline of nearly 70% during the dry season of 2023 to approximately 100 km2. This substantial decrease can be explained by the impact of a robust El Niño phase occurring in the region simultaneously. Overall, there is a high consistency between results derived from SAR and optical sensors, but the correlation between Sentinel-1 and Sentinel-2 (R = 0.9774) was higher than that between ALOS-2 and Sentinel-2 (R = 0.9145). During the drought period, both C-band and L-band SAR sensors overestimate the reservoir’s surface water extent due to the similarity in their backscatter coefficient between water and dry flat soil surfaces. This misclassification is more pronounced in ALOS-2 data than Sentinel-1 data, suggesting that the C-band sensor is more suitable than the L-band sensor for mapping the lake’s open water areas.

Heavy Rain Episodes Identified by L-band InSAR and Limitations of Split-Spectrum Method in Indonesia

Located in a tropical area with abundant precipitation, Indonesia is highly prone to heavy rain hazards, in particular landslides and floods. Thus, rainfall observation is vital. Nonetheless, the topography, the fund availability, as well as the archipelagic state of Indonesia may raise difficulties for in-situ observation, such as rain gauge and weather radar. Currently, the advance of radiometer satellites, such as the Global Precipitation Mission delivers rain estimation and has proven to show good association with in-situ observation on a monthly basis, not daily over the Indonesia area. Therefore, it is vital to have additional measurement methods. For the first time, we apply L-band Interferometric Synthetic Aperture Radar (InSAR) to observe heavy rain in Indonesia. From our three study cases, we successfully identified localized anomalies due to the dense water vapor during heavy rain in the InSAR images. The localized anomalies vary from 10.9 cm in West Java, 7.8 cm in East Kalimantan, and 7.7 cm in West Kalimantan. Furthermore, we utilize the split-spectrum method for our InSAR result; the high-water vapor occurrence in the troposphere associated with heavy rain should be identified in the non-dispersive term. Nevertheless, due to long temporal separation and thinner bandwidth, the split-spectrum method results display unsatisfactory results. We conclude that, while InSAR has the ability to identify heavy rain, having SSM to distinguish between non-dispersive and dispersive phases is not currently practical in Indonesia.

Advanced floodplain mapping: HEC-RAS and ArcGIS pro application on Swat River

AbstractFloods stand out as one of the most devastating environmental threats worldwide resulting in the tragic loss of human lives and significant damage to our essential infrastructure. This study focuses on creating floodplain maps for the two important reaches of Swat River in the Khyber Pakhtunkhwa Province of Pakistan, namely, Kalam–Khwazakhela and Khwazakhela–Chakdara reaches. The Advanced Land Observing Satellite Phased Array Type L-band Synthetic Aperture Radar (ALOS PALSAR) 12.5 m Digital Elevation Model (DEM) data has been used for this purpose. Furthermore, the sensitivity of the flood model was assessed for the Flood 2022 event, considering simulated flood depth, extent, and velocity in relation to various terrains derived from the 12.5-m ALOS PALSAR data. To estimate extreme flows for different return periods (2, 5, 10, 25, 50, and 100 years), the Log Normal (LN), Log Pearson III (LP3), and Generalized Extreme Value (GEV) distributions were employed for the frequency analysis. The GEV distribution turned out to be the best fit for modeling the Swat River for both the Chakdara and Khwazakhela gauge stations. To predict flood levels for the peak floods identified through frequency analysis and for the specific return periods the Hydrologic Engineering Center's River Analysis System (HEC-RAS) 2D simulations were performed. Subsequently the outcomes of this model were used to create floodplain maps using Geographic Information System Professional (ArcGIS Pro) software customized for various return periods. The analysis revealed a linear increase in flood inundation area with longer return periods. The floodplain maps developed hold significant importance for the governing authorities of the Swat region. These maps serve as essential tools for implementing proactive measures against potential infrastructure damage, thereby protecting against economic losses and enhancing public safety. This research effectively combines hydrological modeling and geospatial technology offering practical solutions for managing flood risks. It serves as a valuable guide for making well-informed decisions and promoting sustainable development in flood-prone areas.

Synthetic aperture radar (SAR) detects large gas seeps in Alaska lakes

Reservoirs of 14C-depleted methane (CH4), a potent greenhouse gas, residing beneath permafrost are vulnerable to escape where permafrost thaw creates open-talik conduits. However, little is known about the magnitude and variability of this methane source or its response to climate change. Remote-sensing detection of large gas seeps would be useful for establishing a baseline understanding of sub-permafrost methane seepage, as well as for monitoring these seeps over time. Here we explored synthetic aperture radar’s (SAR) response to large sub-permafrost gas seeps in an interior Alaskan lake. In SAR scenes from 1992 to 2011, we observed high perennial SAR L-band backscatter (σ 0) from a ∼90 m-wide feature in the winter ice of interior Alaska’s North Blair Lake (NBL). Spring and fall optical imagery showed holes in the ice at the same location as the SAR anomaly. Through field work we (1) confirmed gas bubbling at this location from a large pockmark in the lakebed, (2) measured flux at the location of densest bubbles (1713 ± 290 mg CH4 m−2 d−1), and (3) determined the bubbles’ methane mixing ratio (6.6%), radiocarbon age (18 470 ± 50 years BP), and δ13CCH4 values (−44.5 ± 0.1‰), which together may represent a mixture of sources and processes. We performed a first order comparison of SAR σ 0 from the NBL seep and other known sub-permafrost methane seeps with diverse ice/water interface shapes in order to evaluate the variability of SAR signals from a variety of seep types. Results from single-polarized intensity and polarimetric L-band SAR decompositions as well as dual-polarized C-band SAR are presented with the aim to find the optimal SAR imaging parameters to detect large methane seeps in frozen lakes. Our study indicates the potential for SAR remote sensing to be used to detect and monitor large, sub-permafrost gas seeps in Arctic and sub-Arctic lakes.

Equatorial Ionospheric Scintillation Measurement in Advanced Land Observing Satellite (ALOS) Phased Array-Type L-Band Synthetic Aperture Radar (PALSAR) Observations

Amplitude stripes imposed by ionospheric scintillation have been frequently observed in many of the equatorial nighttime acquisitions of the Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR). This type of ionospheric artifact impedes PALSAR interferometric and polarimetric applications, and its formation cause, morphology, and negative influence have been deeply investigated. However, this artifact can provide an alternative opportunity in a positive way for probing and measuring ionosphere scintillation. In this paper, a methodology for measuring ionospheric scintillation parameters from PALSAR images with amplitude stripes is proposed. Firstly, sublook processing is beneficial for recovering the scattered stripes from a single-look complex image; the amplitude stripe pattern is extracted via band-rejection filtering in the frequency domain of the sublook image. Secondly, the amplitude spectrum density function (SDF) is estimated from the amplitude stripe pattern. Thirdly, a fitting scheme for measuring the scintillation strength and spectrum index is conducted between the estimated and theoretical long-wavelength SDFs. In addition, another key parameter, the scintillation index, can be directly measured from the amplitude stripe pattern or indirectly derived from the scintillation strength and spectrum index. The proposed methodology is fully demonstrated on two groups of PALSAR acquisitions in the presence of amplitude stripes. Self-validation is conducted by comparing the measured and derived scintillation index and by comparing the measurements of range lines and azimuth lines. Cross-validation is performed by comparing the PALSAR measurements with in situ Global Position System (GPS) measurements. The processing results demonstrate a powerful capability to robustly measure ionospheric scintillation parameters from space with high spatial resolution.

Snow water equivalent retrieval over Idaho – Part 2: Using L-band UAVSAR repeat-pass interferometry

Abstract. This study evaluates using interferometry on low-frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 d temporal baseline, captured by an L-band aerial platform, NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved measurements shows a strong Pearson correlation (R=0.80) and low RMSE (0.1 m, n=64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R=0.52, n=57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R=0.60, RMSD = 0.023 m, n=3.2×106) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R=0.72, RMSD = 0.013 m, n=6.5×104). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.

Assessing Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 Data for Large-Scale Wildfire-Burned Area Mapping: Insights from the 2017–2019 Canada Wildfires

Wildfires play a crucial role in the transformation of forest ecosystems and exert a significant influence on the global climate over geological timescales. Recent shifts in climate patterns and intensified human–forest interactions have led to an increase in the incidence of wildfires. These fires are characterized by their extensive coverage, higher frequency, and prolonged duration, rendering them increasingly destructive. To mitigate the impact of wildfires on climate change, ecosystems, and biodiversity, it is imperative to conduct systematic monitoring of wildfire progression and evaluate their environmental repercussions on a global scale. Satellite remote sensing is a powerful tool, offering precise and timely data on terrestrial changes, and has been extensively utilized for wildfire identification, tracking, and impact assessment at both local and regional levels. The Canada Centre for Mapping and Earth Observation, in collaboration with the Canadian Forest Service, has developed a comprehensive National Burned Area Composite (NBAC). This composite serves as a benchmark for curating a bi-temporal multi-source satellite image dataset for change detection, compiled from the archives of Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2. To our knowledge, this dataset is the inaugural large-scale, multi-source, and multi-frequency satellite image dataset with 20 m spatial resolution for wildfire mapping, monitoring, and evaluation. It harbors significant potential for enhancing wildfire management strategies, building upon the profound advancements in deep learning that have contributed to the field of remote sensing. Based on our curated dataset, which encompasses major wildfire events in Canada, we conducted a systematic evaluation of the capability of multi-source satellite earth observation data in identifying wildfire-burned areas using statistical analysis and deep learning. Our analysis compares the difference between burned and unburned areas using post-event observation solely or bi-temporal (pre- and post-event) observations across diverse land cover types. We demonstrate that optical satellite data yield higher separability than C-Band and L-Band Synthetic Aperture Radar (SAR), which exhibit considerable overlap in burned and unburned sample distribution, as evidenced by SAR-based boxplots. With U-Net, we further explore how different input channels influence the detection accuracy. Our findings reveal that deep neural networks enhance SAR’s performance in mapping burned areas. Notably, C-Band SAR shows a higher dependency on pre-event data than L-Band SAR for effective detection. A comparative analysis of U-Net and its variants indicates that U-Net works best with single-sensor data, while the late fusion architecture marginally surpasses others in the fusion of optical and SAR data. Accuracy across sensors is highest in closed forests, with sequentially lower performance in open forests, shrubs, and grasslands. Future work will extend the data from both spatial and temporal dimensions to encompass varied vegetation types and climate zones, furthering our understanding of multi-source and multi-frequency satellite remote sensing capabilities in wildfire detection and monitoring.

Annual maps of forest cover in the Brazilian Amazon from analyses of PALSAR and MODIS images

Abstract. Many forest cover maps have been generated by using optical and/or microwave images, but these forest cover maps have large area and spatial discrepancies. To date, few studies have assessed forest cover maps in terms of two biophysical parameters used in forest definition: canopy height and canopy coverage. We generated annual forest cover maps from 2007 to 2010 and evergreen forest cover maps from 2000 to 2021 in the Brazilian Amazon using the images from the Phased Array type L-band Synthetic Aperture Radar and the time series images from the Moderate Resolution Imaging Spectroradiometer, using the forest definition of the Food and Agriculture Organization of the United Nations (>5 m tree height and >10 % canopy coverage) as the reference. We used the canopy height and canopy coverage datasets from the Geoscience Laser Altimeter System during 2003–2007 to assess annual forest cover maps from 2007 to 2010 and annual evergreen forest cover maps from 2003 to 2007, and the results show high accuracy of these forest cover and evergreen forest cover maps. These annual forest cover maps and annual evergreen forest cover maps provide data support for the analyses of the causes, processes, and consequences of forest cover changes in the Brazilian Amazon (https://doi.org/10.6084/m9.figshare.21445626; Qin and Xiao, 2022a; https://doi.org/10.6084/m9.figshare.21445590; Qin and Xiao, 2022b).

Overview of Circular Structures of Various Origins and Sizes in Egypt as a Contribution to Natural Hazard Data Mining Based on Remote Sensing Data and Geoinformation Systems (GIS) Analysis

This study is focused on the detection and typification of circular features with different sizes, origins, and states of erosion as well as on their surrounding tectonic setting, as well as on their impact on the environment and on the occurrence of natural hazards based on different satellite data of Egypt. Sentinel 2, Landsat and ASTER images and Sentinel 1- and ALOS L-band Phased Array Synthetic Aperture Radar (PALSAR)-radar data make it possible to identify larger ring structures as well as smaller circular features like maars or sinkholes in karst areas. Evaluations of the various satellite data contribute to the systematic and standardized inventory ring structures, most of which are related to magmatic intrusions. Such an inventory is a prerequisite for hazard preparedness and should be part of natural hazard data mining integrated into a Geo Information System (GIS). Mapping traces of volcanic activities (craters, maars, cones) is essential as a contribution to land use planning. By gathering the data and integrating the knowledge of the different ring structures in standardized GIS data base one of the many steps towards hazard preparedness and adapted land use planning can be achieved. Circular features are often buried by aeolian and fluvial sediments and become only visible on radar images or on Landsat or ASTER RGB images combining thermal bands. Larger ring structures have not only an influence on groundwater flow but also on geodynamic activity (earthquakes and related secondary effects).

MODELING LAND DEFORMATIONS IN MOUNTAINS BY COMBINING TIME-SERIES L-BAND SAR IMAGES AND SPATIOTEMPORAL STATISTICAL MODELS

Abstract. Predicting land deformations using remote-sensing technology is extremely challenging because the simulation models that are involved have many parameters, including geological features, that must be calibrated against areas of interest, and no universal model is available. Instead, this paper proposes a method for modeling land deformations with time-series L-band synthetic aperture radar (SAR) images and spatiotemporal statistical models for prediction. First, time-series SAR analysis generates temporal land deformation. Then, water-related products are obtained from the Today’ Earth system of the Japan Aerospace Exploration Agency. The three-day consecutive maximum values are calculated and then interpolated spatially. Finally, a descriptive or dynamic spatiotemporal statistical model is applied. The low-rank Gaussian process model, a descriptive model, is found to be highly accurate for explaining land deformations, even for actual slid areas, as long as the areas of analysis are limited, e.g., 150 m × 50 m. It is concluded that the proposed method is effective for predicting land deformations spatially and temporally and so can be used to identify possible landslide locations.

Forest Aboveground Biomass Estimation in Subtropical Mountain Areas Based on Improved Water Cloud Model and PolSAR Decomposition Using L-Band PolSAR Data

Forest aboveground biomass (AGB) retrieval using synthetic aperture radar (SAR) backscatter has received extensive attention. The water cloud model (WCM), because of its simplicity and physical significance, has been one of the most commonly used models for estimating forest AGB using SAR backscatter. Nevertheless, forest AGB estimation using the WCM is usually based on simplified assumptions and empirical fitting, leading to results that tend to overestimate or underestimate. Moreover, the physical connection between the model and the polarimetric synthetic aperture radar (PolSAR) is not established, which leads to the limitation of the inversion scale. In this paper, based on the fully polarimetric SAR data from the Advanced Land Observing Satellite-2 (ALOS-2) Phased Array-type L-band Synthetic Aperture Radar (PALSAR-2), the relative contributions of the three major scattering mechanisms were first analyzed in a hilly area of southern China. On this basis, the traditional WCM was extended by considering the secondary scattering mechanism. Then, to establish the direct relationship between the vegetation scattering mechanism and forest AGB, a new relationship equation between the PolSAR decomposition model and the improved water cloud model (I-WCM) was constructed without the help of external data. Finally, a nonlinear iterative method was used to estimate the forest AGB. The results show that volume scattering is the dominant mechanism, accounting for more than 60%. Double-bounce scattering accounts for the smallest fraction, but still about 10%, which means that the contribution of the double-bounce scattering component is not negligible in forested areas because of the strong penetration capability of the long-wave SAR. The modified method provides a correlation coefficient R2 of 0.665 and a root mean square error (RMSE) of 21.902, which is an improvement of 36.42% compared to the traditional fitting method. Moreover, it enables the extraction of forest parameters at the pix scale using PolSAR data without the need for low-resolution external data and is thus helpful for high-resolution mapping of forest AGB.

Automated mapping of regolith units with support vector machine and artificial neural network using data from Landsat-8 OLI, ALOS PALSAR DEM, and Sentinel-1A radar images: the case of the Sissingué Gold Project, Northern Côte d’Ivoire

ABSTRACT The Sissingué Gold Project is in Northern Côte d’Ivoire and part of the southern extension, a large peneplain of the landscape in South Mali. The area has experienced extensive weathering and erosion over a long time, resulting in a widespread and complex regolith cover. This diverse and vast regolith cover poses considerable difficulties for exploration. To overcome this problem, we adopted mapping of regolith units using machine learning (ML) with support vector machine (SVM) and artificial neural network (ANN) algorithms. The main objectives were to (1) make the predictive regolith map of the study region using these algorithms with data obtained from Landsat-8 operational land imager, Advanced Land Observing Satellite Phased Array Type L-Band Synthetic Aperture Radar digital elevation model and Sentinel-1A; (2) show the method of pre-processing and processing the required data and (3) develop the regolith landform unit (RLU) map from the predictive regolith map and collected data based on the Relict – Erosional – Depositional model. Tests using the SVM and ANN algorithms showed that both ML tools could accurately map regolith landforms. An innovative method of optimizing data using parameters is presented. The results showed that ANN outperformed SVM with an overall accuracy of 87.01% and a kappa coefficient of 0.84, whereas the corresponding values for SVM were 86.69% and 0.84, respectively. However, the validation data obtained from SVM-based prediction exhibited a better score than that of validation data obtained from ANN-based prediction. The Sentinel-1A radar band combined with Landsat-8 data reduced the vegetation-masking effect and improved the classification results. The RLUs of this area are composed primarily of relicts at 24.91%, including lateritic residuum and soil, depositional material at 36.39% with exotic sediments and ferrierite and the remaining 38.7% of erosional material made of saprolite, colluvial fragments, and mottled zones on flanks.

Land subsidence studies in the Godavari Delta regions of the East coast of India using ALOS and Sentinel 1 data

Subsidence in coastal deltas is a challenging issue globally, influenced by both climate and anthropogenic pressures. Owing to its rich economic value, the Godavari Delta on the east coast of India attracts the human population and is therefore a hub of developmental activities. We quantified the line of sight (LOS) rate of land deformation in the Godavari Delta, India, for two different periods using the time series interferometric analysis of Advanced Land Observing Satellite Phase Arrayed L-band Synthetic Aperture Radar (ALOS PALSAR) (2007–2011) and C-band Sentinel-1A (2017–2020) datasets. This approach was based on the Small Baseline Subset (SBAS) algorithm, which constructed 76 (ALOS) and 333 (Sentinel-1A) pairs of interferograms. The results indicated that the majority of deltas had varying LOS deformation rates in both periods. The southern and northern coastline of the deltas showed subsidence (-10 to -20 mm/yr) and uplift (5 mm to 7 mm/yr), respectively, from 2007 to 2011. The uplift (11 mm/yr) during 2017–2020 was attributed to coastal erosion and accretion during the study period. Statistical analyses showed that the majority of the coastline experienced erosion from 2006 to 2011, ranging from 0 ‐30 m/yr. Conversely, during 2017–2019, accretion ranging from 0-30 m/yr was dominant. The Kakinada spit has recently experienced an uplift of 8 mm/yr. Many factors contributing to deformation, such as groundwater fluctuations, rainfall, land use and land cover (LULC) changes, and water quality, were explored. Groundwater level variations were strongly correlated with the rate of deformation. A direct relationship was also observed between water recharge through rainfall and the ground deformation rate. The subsidence rate was estimated as 0 to ‐8 mm/yr from 2007-2011 and -10 to -20 mm/yr for 2017–2020 in the Godavari Delta. Increased urban areas and land conversion from the LULC analysis were the causative factors of subsidence in this region. Thus, the Godavari Delta will become highly vulnerable, as the region has shown various LOS deformation rates. Studies on land subsidence are highly relevant for effective coastal management planning and management.

Melt Season Arctic Sea Ice Type Separability Using Fully and Compact Polarimetric C- and L-Band Synthetic Aperture Radar

Sea ice mapping using Synthetic Aperture Radar (SAR) in the melt season poses challenges, due to wet snow and melt ponds complicating sea ice type separability. To address this, we analyzed fully polarimetric (FP) and simulated compact polarimetric (CP) C- (RADARSAT-2) and L- (ALOS-2 PALSAR-2) band SAR, in the 2018 melt season in the Canadian Arctic Archipelago, for stage-wise separation of first year ice (FYI) and multiyear ice (MYI). SAR scenes at both near- (19.1–28.3°) and far- (35.8–42.1°) range incidence angles and coincident high-resolution optical scenes were used to assess the impact of surface melt ponds on separability within a landfast ice zone of diverse ice thickness. C-band provided better separability between FYI and MYI during pond onset, while L-band was superior during pond drainage due to MYI volumetric scattering. CP parameters matched FP performance across the melt season. HH and HV, commonly offered in ScanSAR mode for both frequencies, presented good separability during pond onset and drainage. Using both C-band and L-band SAR along with constraining incidence angle ranges, enhances sea ice type identification and separability. Our results can support ice type classification and seasonal stage detection for climate studies and enhance existing frameworks for ice motion vector retrievals.