L-band Synthetic Aperture Radar Research Articles

Three-dimensional and long-term landslide displacement estimation by fusing C- and L-band SAR observations: A case study in Gongjue County, Tibet, China

Recently, a large number of synthetic aperture radar (SAR) images has been introduced into landslide investigations with the growing launch of new SAR satellites, such as ALOS/PALSAR-2 and Sentinel-1. Therefore, it is appropriate to develop new approaches to retrieve three-dimensional (3D) displacements and long-term (> 10 years) displacement time series to investigate the spatio-temporal evolution and creep behavior of landslides. In this study, a new approach for the estimation of 3D and long-term displacement time series of landslides, based on the fusion of C- and L-band SAR observations, is presented. This method is applied to map 3D and long-term displacements (nearly 12 years) of the landslides in Gongjue County, Tibet in China; four sets of SAR images from different platforms (i.e., L-band ascending ALOS/PALSAR-1, C-band descending ENVISAT, and C-band ascending and descending Sentinel-1 SAR datasets) covering the period of January 2007 to November 2018 were collected and exploited. First, the assumption that the landslide moves parallel to its ground surface is used to produce 3D displacement rates and time series by fusing ascending and descending Sentinel-1 SAR images, from which the optimal sliding direction for each pixel of the slope is well estimated. Then, the long-term displacement time-series of the landslide between January 2007 and October 2018 in the estimated sliding direction is recovered by fusing L-band ALOS/PALSAR-1 and C-band Sentinel-1 SAR images. In order to fill the time gap of nearly four years between ALOS/PALSAR-1 and Sentinel-1 SAR images, the Tikhonov regularization (TR) method is developed to establish the observational equation. Moreover, to solve the problem arising from ALOS/PALSAR-1 and Sentinel-1 images with different wavelengths, incidence angles and flight directions, the measurements from ALOS/PALSAR-1 and Sentinel-1 images are both projected to the estimated optimal sliding direction to achieve a unified displacement datum. Our results from ascending and descending Sentinel-1 images suggest that the maximum displacement rates of the study area in the vertical and east-west directions from December 2016 to October 2018 were greater than 70 and 80 mm/year, respectively, and 2D displacement results reveal that the displacement patterns and movement characteristics of all the detected landslides are not identical in the study area. Specifically, the 3D displacement results successfully revealed the spatiotemporal displacement patterns and movement direction of each block of the Shadong landslide, and long-term displacement time series showed for the first time that the maximum cumulative displacement exceeds 1.3 m from January 2007 to October 2018. Moreover, the kinematic evolution and possible driving factors of landslides were investigated using 2D and 3D and long-term displacement results, coupled with hydrological factors and unidimensional constitutive models of the rocks.

Soil Moisture Retrieval Using Polarimetric SAR Data and Experimental Observations in an Arid Environment

Soil moisture is a critical component for Earth science studies, and Synthetic Aperture Radar (SAR) data have high potential for retrieving soil moisture using backscattering models. In this study, polarimetric SAR (PALSAR: Phased Array type L-band Synthetic Aperture Radar) data and polarimetric decompositions including span, entropy/H/alpha, and anisotropy, in combination with surface properties resulting from field and laboratory measurements, are used to categorize the natural surface condition and discriminate the backscatter parameter in the test site for applying the inversion soil moisture retrieval. The work aims to introduce the better of two examined models in the research for soil moisture retrieval over the bare land and sparse vegetation in arid regions. After soil moisture retrieval using the two different models, the results of comparison and validation by field measurement of soil moisture have shown that the Oh model has a more realiable accuracy for soil moisture mapping, although it was very difficult to find the best model due to different characteristics in land cover. It seems the inversion model, with the field observation and polarimetric SAR data, has a good potential for extracting surface natural conditions such as surface roughness and soil moisture; however, over- and under-estimation are observed due to land cover variability. The estimation of accurate roughness and moisture data for each type of land cover can increase the accuracy of the results.

Mapping forest in the southern Great Plains with ALOS-2 PALSAR-2 and Landsat 7/8 data

Southern Great Plains (SGP) of the United States, comprising the states Kansas, Oklahoma, and Texas, spans diverse climatic regions. In recent decades, woody plant continues to expand and form forest (above 2 m in height) across the SGP. However, our knowledge of the forest amount and distribution in this region is very limited. This study aims to map forest, especially evergreen forest (above 2 m in height) in the SGP for the time period of 2015–2017. Annual mosaic data of HH and HV polarization backscattering (25 m) from Phased Arrayed L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard Advanced Land Observing Satellite-2 (ALOS-2), along with their difference (HH-HV) and ratio (HH/HV) were utilized. With the four bands (HH, HV, difference, ratio) of 2017, decision rules of forest were developed based on 30 randomly selected forest plots (as of 2017) across the study area. With the decision rules, a PALSAR-2 based forest map was created for each year from 2015 to 2017. Then an annual maximum normalized difference vegetation index (NDVI) threshold of 0.5, derived from Landsat 8 data of 2017 and the 30 forest plots, was used to filter potential commission error of rough surface and building in each PALSAR-2 based forest map. After that, a forest map circa 2016 was generated, in which each forest pixel was identified as such at least twice during 2015 and 2017. Lastly, a threshold of seasonal NDVI change (0.3) was derived to extract evergreen forest out of the forest map circa 2016. Accuracy assessment for the result forest map suggests a user’s accuracy of 99.2% and a producer’s accuracy of 88.7% for forest. Accuracy assessment for the evergreen forest map suggests a user’s accuracy of 97.3% and a producer’s accuracy of 90.5% for evergreen forest. The result forest map, especially the evergreen forest map, paves the way for follow-up studies on forest resource and woody plant encroachment in the SGP.

Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

Construction of the 998.64-km Linzhi–Ya’an section of the Sichuan–Tibet Railway has been influenced by landslide disasters, threatening the safety of Sichuan–Tibet railway projects. Landslide identification and deformation analysis in this area are urgently needed. In this context, it was the first time that 164 advanced land-observing satellite-2 (ALOS-2) phased array type L-band synthetic aperture radar-2 (PALSAR-2) images were collected to detect landslide disasters along the entire Linzhi–Ya’an section. Interferogram stacking and small baseline interferometry methods were used to derive the deformation rate and time-series deformation from 2014–2020. After that, the hot spot analysis method was introduced to conduct spatial clustering analysis of the annual deformation rate, and the effective deformation area was quickly extracted. Finally, 517 landslide disasters along the Linzhi–Ya’an route were detected by integrating observed deformation, Google Earth optical images, and external geological data. The main factors controlling the spatial landslide distribution were analyzed. In the vertical direction, the spatial landslide distribution was mainly concentrated in the elevation range of 3000–5000 m, the slope range of 10–40°, and the aspect of northeast and east. In the horizontal direction, landslides were concentrated near rivers, and were also closely related to earthquake-prone areas, fault zones, and high-precipitation areas. In short, rainfall, freeze–thaw weathering, seismic activity, and fault zones are the main factors inducing landslides along this route. This research provides scientific support for the construction and operation of the Linzhi–Ya’an section of the Sichuan–Tibet Railway.

A COMPARISON OF RANDOM FOREST AND LIGHT GRADIENT BOOSTING MACHINE FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF LANDSAT, ALOS PALSAR, AND AIRBORNE LIDAR DATA

Abstract. Sustainable forest management is a critical topic which contributes to ecological, economical, and socio-cultural aspect of the environment. Providing accurate AGB maps is of paramount importance for sustainable forest management, carbon accounting, and climate change monitoring. The main goal of this study was to leverage the potential of two machine learning algorithms for predicting AGB using optical and synthetic aperture radar (SAR) datasets. To achieve this goal random forest (RF) and light gradient boosting machine (LightGBM) models were deployed to predict AGB values in Huntington Wild Forest (HWF) in Essex County, NY using continuous forest inventory (CFI) plots. Both models were trained and evaluated based on airborne light detection and ranging (LiDAR) data, Landsat imagery, advanced land observing satellite (ALOS) phased array type L-band Synthetic Aperture Radar (PALSAR), and their combination. The integration of airborne LiDAR, optic, and SAR datasets provided the best results in terms of root mean square error (RMSE) and mean bias error (MBE). The RF model outperformed the LightGBM in all scenarios (LiDAR, Landsat 5, ALOS PALSAR, and their combination). The RF model was able to predict AGB values with the RMSE of 51.90 Mg/ha and MBE of −0.189 Mg/ha for the combination of LiDAR, optic, and SAR data, while LightGBM estimated the AGB values with the RMSE of 52.78 Mg/ha and MBE of −0.253 Mg/ha. LightGBM is more sensitive to noise and there are lots of hyperparameters that need to be tuned which highly affect its performance.

Tropospheric dispersive phase anomalies during heavy rain detected by L-band InSAR and their interpretation

The split-spectrum method (SSM) can largely isolate and correct for the ionospheric contribution in the L-band interferometric synthetic aperture radar (InSAR). The standard SSM is performed on the assumption of only the first-order ionospheric dispersive effect, which is proportional to the total electron content (TEC). It is also known that during extreme atmospheric events, either originated from the ionosphere or in the troposphere, other dispersive effects do exist and potentially provide new insights into the dynamics of the atmosphere, but there have been few detection reports of such signals by InSAR. We apply L-band InSAR into heavy rain cases and examine the applicability and limitation of the standard SSM. Since no events such as earthquakes to cause surface deformation took place, the non-dispersive component is apparently attributable to the large amount of water vapor associated with heavy rain, whereas there are spotty anomalies in the dispersive component that are closely correlated with the heavy rain area. The ionosonde and Global Navigation Satellite System (GNSS) rate of total electron content index (ROTI) map both show little anomalies during the heavy rain, which suggests few ionospheric disturbances. Therefore, we interpret that the spotty anomalies in the dispersive component of the standard SSM during heavy rain are originated in the troposphere. While we can consider two physical mechanisms, one is runaway electron avalanche and the other is the dispersive effect due to rain, comparison with the observations from the ground-based lightning detection network and rain gauge data, we conclude that the rain dispersive effect is spatiotemporally favorable. We further propose a formulation to examine if another dispersive phase than the first-order TEC effect is present and apply it to the heavy rain cases as well as two extreme ionospheric sporadic-E events. Our formulation successfully isolates the presence of another dispersive phase during heavy rain that is in positive correlation with the local rain rate. In comparison with other dispersive phases during Sporadic-E episodes, the dispersive heavy rain phases seem to have the same order of magnitude with the ionospheric higher order effects.

Seasonal deformation monitoring of the northern Yellow River Delta based on InSAR technology.

Due to overexploitation of natural resources in the northern Yellow River Delta and the resulting land subsidence, interferometric synthetic aperture radar (InSAR) technology is utilized to analyze the deformation trend. Based on Advanced Land Observation Satellite PHASE Array Type L-band Synthetic Aperture Radar and Sentinel-1 data, this paper obtains the annual subsidence rate in two time periods (2007-2010 and 2017-2020) and verifies the correctness by comparing the ascending and descending orbit results. Subsequently, the 11 deformation interference pairs of the three-month interval are extracted to analyze the time series displacement of deformation areas. The results show that there are three large-scale subsidence areas with maximum annual subsidence of 250 mm, all of which are located in the oil or brine exploitation areas, and each deformation area displays a larger linear rate from January to May and then displays different nonlinear deformation from June to December.

Examining the Impact of Different DEM Sources and Geomorphology on Flash Flood Analysis in Hyper-Arid Deserts

Digital elevation models (DEMs) are the cornerstone for hydrological and geomorphological modeling. Herein, two Nile-tributary catchments (Wadi Al Rishrash and Wadi Atfeh) in Egypt are selected to examine the contribution of different DEMs to the accuracy of hydrological and geomorphological analyses in the hyper-arid Sahara. DEMs sources include: Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) (12.5 m resolution), ALOS World 3D with 30 m resolution (AW3D30), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER DEM with 30 m resolution) and the Shuttle Radar Topography Mission (SRTM with 30 and 90 m resolution), in addition to topographic map-derived DEM (90 m resolution). Using a hypothetical uniformly-distributed 10 mm rainfall event, the estimated parameters, including: flow duration, time to peak and peak discharge rates, are almost similar for the different DEMs and thus technical aspects related to sources and resolutions of the datasets impose insignificant control on quantitative flash-flood analyses. Conversely, variations in geological and geomorphological characteristics of the catchments show more significant control on the hydrograph magnitudes as indicated by the different parameters of the two catchments. These findings indicate that understanding the geological and hydrological evolution of the catchment is essential for integrated management strategies of floods especially in the Saharan–Arabian deserts and in similar conditions of hyper-aridity and scarce in situ data worldwide.

Lognormal Random Field models to identify temporal Land cover changes using full polarimetric L-Band SAR imagery

Multiplicative Autoregressive Random Field (MAR) based texture models have been identified as one of the most appropriate models for SAR intensity images to capture the stochastic spatial interaction among neighboring pixels. But very few studies have tested their viability particularly in disaster applications. In this paper, we analyse the MAR texture models for their advantageous in land cover change detection compared to the changes resulting from logarithm of SAR image intensity and speckle filtered SAR imagery. The paper shows that lognormal random fields with multiplicative spatial interactions in the form of MAR models can be an effective alternative to suppress speckle noise and model SAR image intensity in time series data analysis. The pre and post disaster observational data of the Tohoku earthquake, in the east coast of Japan, acquired by the Advanced Land Observation Satellite (ALOS)/phased array type L-band synthetic aperture radar (PALSAR) were synthesized using MAR model based texture measures. Two of the main texture descriptors of the MAR model were considered primarily in this study. Those are the neighborhood weighting and the noise variance parameters. A 2nd order neighborhood configuration was used to estimate them. We present a variogram based analysis, structural similarity index measure (SSIM), and the mean ratio detector (MRD) as three different approaches to analyse the changes in land cover using radar texture. The change detection results of the MRD were further tested using area error proportion (AEP), root mean square error (RMSE) and correlation coefficient (CC), keeping normalized ratio, principle component analysis (PCA) and adaptive Lee filtered polarimetric intensity based change as the references.

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

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

Multi-sensor datasets-based optimal integration of spectral, textural, and morphological characteristics of rocks for lithological classification using machine learning models

We present an optimal integration of multi-sensor datasets, including Advanced Spaceborne Thermal and Reflection Radiometer (ASTER), Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and digital elevation model for lithological classification using Machine Learning Models (MLMs). Different input features such as spectral, spectral and transformed spectral, spectral and morphological, spectral and textural, and optimum hybrid features were derived and evaluated to accurately classify different rock types found in the Chhatarpur district (Madhya Pradesh), India using the Support Vector Machine (SVM) and Random Forest (RF). The SVM achieves better classification accuracy and shows less sensitivity to the number of samples used in model development. The optimum hybrid features outperform other input features with an overall accuracy and κ coefficient of 77.78% and 0.74, which is around 15% higher as obtained using ASTER spectral data alone. Thus, the proposed multi-sensor optimal integration approach is recommended for successful lithological classification using MLMs.

External Calibration Results of the SAOCOM-1A Commissioning Phase

SAOCOM is a scientific and commercial mission developed by CONAE, the National Space Commission of Argentina. It is composed of two identical satellites, each one carrying a fully polarimetric L-band synthetic aperture radar (SAR). The main driver of the mission is the retrieval of soil moisture, an application that demands high radiometric and polarimetric accuracy. In this article, we summarize the main external calibration results obtained during the commissioning phase of the SAOCOM-1A satellite. In addition, the general calibration strategy as well as the devices and facilities that were involved in the external calibration process are described.

Oil Spill Thickness Determination from L-band Synthetic Aperture Radar

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

Comparison of Target Detectors to Identify Icebergs in Quad-Polarimetric L-Band Synthetic Aperture Radar Data

Icebergs represent hazards to ships and maritime activities and therefore their detection is essential. Synthetic Aperture Radar (SAR) satellites are very useful for this, due to their capability to acquire data under cloud cover and during day and night passes. In this work, we compared six state-of-the-art polarimetric target detectors to test their performance and ability to detect small-sized icebergs <120 m in four locations in Greenland. We used four single-look complex (SLC) ALOS-2 quad-polarimetric images from JAXA for quad-polarimetric detection and we compared with dual-polarimetric detectors using only the channels HH and HV. We also compared these detectors with single-polarimetric intensity channels and we tested using two scenarios: open ocean and sea ice. Our results show that the multi-look polarimetric whitening filter (MPWF) and the optimal polarimetric detector (OPD) provide the most optimal performance in quad- and dual-polarimetric mode detection. The analysis shows that, overall, quad-polarimetric detectors provide the best detection performance. When the false alarm rate (PF) is fixed to 10−5, the probabilities of detection (PD) are 0.99 in open ocean and 0.90 in sea ice. Dual-polarimetric or single-polarimetric detectors show an overall reduction in performance (the ROC curves show a decrease), but this degradation is not very large (<0.1) when the value of false alarms is relatively high (i.e., we are interested in bigger icebergs with a brighter backscattering >120 m, as they are easier to detect). However, the differences between quad- and dual- or single-polarimetric detectors became much more evident when the PF value was fixed to low detection probabilities 10−6 (i.e., smaller icebergs). In the single-polarimetric mode, the HV channel showed PD values of 0.62 for open ocean and 0.26 for sea ice, compared to values of 0.81 (open ocean) and 0.77 (sea ice) obtained with quad-polarimetric detectors.

Mapping potential signs of gas emissions in ice of Lake Neyto, Yamal, Russia, using synthetic aperture radar and multispectral remote sensing data

Abstract. Regions of anomalously low backscatter in C-band synthetic aperture radar (SAR) imagery of lake ice of Lake Neyto in northwestern Siberia have been suggested to be caused by emissions of gas (methane from hydrocarbon reservoirs) through the lake’s sediments. However, to assess this connection, only analyses of data from boreholes in the vicinity of Lake Neyto and visual comparisons to medium-resolution optical imagery have been provided due to a lack of in situ observations of the lake ice itself. These observations are impeded due to accessibility and safety issues. Geospatial analyses and innovative combinations of satellite data sources are therefore proposed to advance our understanding of this phenomenon. In this study, we assess the nature of the backscatter anomalies in Sentinel-1 C-band SAR images in combination with very high resolution (VHR) WorldView-2 optical imagery. We present methods to automatically map backscatter anomaly regions from the C-band SAR data (40 m pixel spacing) and holes in lake ice from the VHR data (0.5 m pixel spacing) and examine their spatial relationships. The reliability of the SAR method is evaluated through comparison between different acquisition modes. The results show that the majority of mapped holes (71 %) in the VHR data are clearly related to anomalies in SAR imagery acquired a few days earlier, and similarities to SAR imagery acquired more than a month before are evident, supporting the hypothesis that anomalies may be related to gas emissions. Further, a significant expansion of backscatter anomaly regions in spring is documented and quantified in all analysed years 2015 to 2019. Our study suggests that the backscatter anomalies might be caused by lake ice subsidence and consequent flooding through the holes over the ice top leading to wetting and/or slushing of the snow around the holes, which might also explain outcomes of polarimetric analyses of auxiliary L-band Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) data. C-band SAR data are considered to be valuable for the identification of lakes showing similar phenomena across larger areas in the Arctic in future studies.

Retrieval of Tropical Forest Height and Above-Ground Biomass Using Airborne P- and L-Band SAR Tomography

Synthetic aperture radar tomography (TomoSAR) at different radar wavelength can be used to measure different structural elements of forests. In this letter, we compared the airborne P- and L-band synthetic aperture radar (SAR) tomograms and TomoSAR-measured canopy height model (CHM) and above-ground biomass (AGB) over a tropical forest in Lope, Gabon. The SAR data sets were acquired by German Aerospace Center (DLR)'s F-SAR system during the AfriSAR2016 campaign. First, the Weighted covariance fitting-based Iterative Spectral Estimator (WISE) was applied to obtain tomograms. CHM was then retrieved based on the canopy phase center derived from the tomograms. Finally, AGB was estimated via an empirical logarithmic model developed from field measurements and the tomographic backscatter power of vegetation layers between 40 and 50 m above ground. Compared with the classical approaches of Capon and Wavelet-based Compressed Sensing, the WISE method can achieve better resolution with higher computational efficiency and reduce the ambiguity level of L-band tomograms successfully. The experimental results also show that there is no substantial difference between P- and L-band TomoCHM, while P-band tomographic intensity is more sensitive than the L-band for the inversion of tropical forest AGB at a resolution of 50 m x 50 m.

Quad-pol advanced land observing satellite/phased array L-band synthetic aperture radar-2 (ALOS/PALSAR-2) data for modelling secondary forest above-ground biomass in the central Brazilian amazon

ABSTRACT Secondary forests (SFs) are one of the major carbons sinks in the Neotropics due to the rapid carbon assimilation in their above-ground biomass (AGB). However, the accurate contribution of SFs to the carbon cycle is a great challenge because of the uncertainty in AGB estimates. In this context, the main objective of this study is to explore full polarimetric Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar-2 (ALOS/PALSAR-2) data to model SFs AGB in the Central Amazon. We carried out the forest inventory in 2014, measuring 23 field plots. Supplementary land-use classification history was used to create 120 additional independent sample plots by adjusting growth curves using SFs age and previous land-use intensity from field plots and literature database. Multiple Linear Regression (MLR) analysis was performed to select the best model by corrected weighted Akaike Information Criterion (AICw) and validated by the leave-one-out bootstrapping method. The best-fitted model has six parameters and explained 65% of the above-ground biomass variability. The prediction error was of Root Mean Square Error of the Prediction (RMSEP) = 8.8 ± 3.0 tonnes ha−1 (8.8%). The most explanatory variables for modelling secondary forest AGB were those that result from multiple scattering (Shannon Entropy), volumetric scattering (Bhattacharya decomposition), and double-bounce scattering (ratio VV/HH, vertically transmitted and received polarization/horizontally transmitted and received polarization). Including past-use of SF areas in the model with the Landsat time series classification, as the frequency of clear cuts and the number of years of active land-use before abandonment, the MLR has increased by 10%, achieving 71% of the variability explained by the model. The uncertainty report showed that ground truth AGB estimation (inventory, allometry, and plot expansion factors) might represent 50% of the errors in the modelling estimation. In contrast, Synthetic Aperture Radar (SAR) inversion models (SAR error and regression) have achieved 20%. The results showed that additional information on secondary forest land-use history could improve the performance of AGB recovery models, as well as can be used to expand the sampling units on tropical forests.

The use of remotely sensed data to reveal geologic, structural, and hydrologic features and predict potential areas of water resources in arid regions

Egypt’s arid deserts resemble the Sahara. They lack water resources because of climatic conditions, although the high topographic areas receive rainfall during sporadic storms. In this article, integrating radar Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR), Landsat-8, Sentinel-2, and Shuttle Radar Topography Mission (SRTM) data reveal the geological, geomorphic, structural, and hydrological features in Wadi Batur (WB), Egypt. This defunct tributary of the Nile basin, in the eastern Egyptian desert, covers approximately 6600 km2. Analysis of the remotely sensed data shows that the northwest–southeast structures control the structures discussed in this study, which are later captured by the east–west trend. A fused ALOS/PALSAR and Landsat-8 image reveals two water outlets for the WB: one extends between the Precambrian and Phanerozoic rocks and drains in a northwestern direction to Wadi Matula in the north, and the second cuts the sedimentary rock and drains to the west, at Idfu. Ten sub-basins were extracted by using SRTM data and arranged by their infiltration properties. Eight evidential maps were combined after assigning a weight to each, and a Geographic Information Systems (GIS) technique was used to predict the groundwater recharge potential zones (GPZs). The results show that the predicted GPZs in the WB fall into six categories, ranging from excellent to bare/absent. Importantly, about 10% of the surface areas in the WB basin are characterized by an excellent recharging potential and represent the most promising zones for future groundwater extraction. Combining various datasets derived from satellite sensors is a substantial tool to detect geological features and explore groundwater resources in arid environments.

Detecting tropical selective logging with C-band SAR data may require a time series approach

Selective logging is the primary driver of forest degradation in the tropics and reduces the capacity of forests to harbour biodiversity, maintain key ecosystem processes, sequester carbon, and support human livelihoods. While the preceding decade has seen a tremendous improvement in the ability to monitor forest disturbances from space, large-scale (spatial and temporal) forest monitoring systems have almost universally relied on optical satellite data from the Landsat program, whose effectiveness is limited in tropical regions with frequent cloud cover. Synthetic aperture radar (SAR) data can penetrate clouds and have been utilized in forest mapping applications since the early 1990s, but only recently has SAR data been widely available on a scale sufficient to facilitate pan-tropical selective logging detection systems. Here, a detailed selective logging dataset from three lowland tropical forest regions in the Brazilian Amazon was used to assess the effectiveness of SAR data from Sentinel-1, RADARSAT-2, and Advanced Land Observing Satellite-2 Phased Arrayed L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) for monitoring tropical selective logging. We built Random Forests models aimed at classifying pixel-based differences between logged and unlogged areas. In addition, we used the Breaks For Additive Season and Trend (BFAST) algorithm to assess if a dense time series of Sentinel-1 imagery displayed recognizable shifts in pixel values after selective logging. In general, Random Forests classification with SAR data (Sentinel-1, RADARSAT-2, and ALOS-2 PALSAR-2) performed poorly, having high commission and omission errors for logged observations. This suggests little to no difference in pixel-based metrics between logged and unlogged areas for these sensors, particularly at lower logging intensities. In contrast, the Sentinel-1 time series analyses indicated that areas under higher intensity selective logging (> 20 m3 ha−1) show a distinct spike in the number of pixels that included a breakpoint during the logging season. BFAST detected breakpoints in 50% of logged pixels and exhibited a false alarm rate of approximately <5% in unlogged forest. Overall our results suggest that SAR data can be used in time series analyses to detect tropical selective logging at high intensity logging locations (> 20 m3 ha−1) within the Amazon.

Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data

Fuel load is the key factor driving fire ignition, spread and intensity. The current literature reports the light detection and ranging (LiDAR), optical and airborne synthetic aperture radar (SAR) data for fuel load estimation, but the optical and SAR data are generally individually explored. Optical and SAR data are expected to be sensitive to different types of fuel loads because of their different imaging mechanisms. Optical data mainly captures the characteristics of leaf and forest canopy, while the latter is more sensitive to forest vertical structures due to its strong penetrability. This study aims to explore the performance of Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data as well as their combination on estimating three different types of fuel load—stem fuel load (SFL), branch fuel load (BFL) and foliage fuel load (FFL). We first analyzed the correlation between the three types of fuel load and optical and SAR data. Then, the partial least squares regression (PLSR) was used to build the fuel load estimation models based on the fuel load measurements from Vindeln, Sweden, and variables derived from optical and SAR data. Based on the leave-one-out cross-validation (LOOCV) method, results show that L-band SAR data performed well on all three types of fuel load (R2 = 0.72, 0.70, 0.72). The optical data performed best for FFL estimation (R2 = 0.66), followed by BFL (R2 = 0.56) and SFL (R2 = 0.37). Further improvements were found for the SFL, BFL and FFL estimation when integrating optical and SAR data (R2 = 0.76, 0.81, 0.82), highlighting the importance of data selection and combination for fuel load estimation.