Backscatter Values Research Articles

A radar backscatter simulation of a forest canopy using 3D physical structures derived from LiDAR scanning

ABSTRACT This study aims to explore the forest aboveground biomass relationship to C-band backscatter. A one-hectare area in Wytham Woods, located west of Oxford, was selected for this research. The area has a total of 525 trees of seven different tree species. The Michigan Microwave Canopy Scattering Model (MIMICS), a two-layer radiative transfer model, was applied to simulate canopy backscatter responses in this deciduous UK forest. Model parameters related to forest structure were derived from previously published terrestrial laser (LiDAR) data. Simulated backscatter was performed for co-polarized and cross-polarized modes at a C-band frequency range and incidence angles (20° to 45° at 5° increments). The research includes five objectives: i) backscatter sensitivity analysis from the variation of different model parameters; ii) backscatter seasonal effect under spring–summer (leaf-on) and autumn–winter (leaf-off) periods; iii) backscatter comparison between species as well as between simulated and satellite observations in a spatial pattern; iv) relationship between simulated backscatter and aboveground biomass; and v) relationship between MIMICS forest structure parameters and simulated backscatter. Sensitivity analysis results showed significant differences in backscatter from changes in leaf distribution, leaf thickness and water content (leaf, trunk and soil) for both polarization modes in leaf-on scenarios. Leaf-off scenarios presented significant differences from changes in branch distribution but only for the co-polarized mode. Seasonal variations presented significant backscatter differences between spring–summer and autumn–winter scenarios; additionally, backscatter differences among species for each seasonality in the co-polarized mode were observed. The computation of the grid resolution (20 m × 20 m) showed a range of backscatter values depending on the grid and incidence angle. Higher backscatter values were observed for the satellite data than for the simulated data. Finally, the aboveground biomass and the MIMICS input parameters presented high random variability and little systematic co-variation with the total backscatter on both simulated seasons, suggesting that more robust allometric equations for biomass estimation are required.

Large-Scale Network-Based Observations of a Saharan Dust Event across the European Continent in Spring 2022

Between 14 March and 21 April 2022, an extensive investigation of an extraordinary Saharan dust intrusion over Europe was performed based on lidar measurements obtained by the European Aerosol Research Lidar Network (EARLINET). The dust episode was divided into two distinct periods, one in March and one in April, characterized by different dust transport paths. The dust aerosol layers were studied over 18 EARLINET stations, examining aerosol characteristics during March and April in four different regions (M-I, M-II, M-III, and M-IV and A-I, A-II, A-III, and A-IV, respectively), focusing on parameters such as aerosol layer thickness, center of mass (CoM), lidar ratio (LR), particle linear depolarization ratio (PLDR), and Ångström exponents (ÅE). In March, regions exhibited varying dust geometrical and optical properties, with mean CoM values ranging from approximately 3.5 to 4.8 km, and mean LR values typically between 36 and 54 sr. PLDR values indicated the presence of both pure and mixed dust aerosols, with values ranging from 0.20 to 0.32 at 355 nm and 0.24 to 0.31 at 532 nm. ÅE values suggested a range of particle sizes, with some regions showing a predominance of coarse particles. Aerosol Optical Depth (AOD) simulations from the NAAPS model indicated significant dust activity across Europe, with AOD values reaching up to 1.60. In April, dust aerosol layers were observed between 3.2 to 5.2 km. Mean LR values typically ranged from 35 to 51 sr at both 355 nm and 532 nm, while PLDR values confirmed the presence of dust aerosols, with mean values between 0.22 and 0.31 at 355 nm and 0.25 to 0.31 at 532 nm. The ÅE values suggested a mixture of particle sizes. The AOD values in April were generally lower, not exceeding 0.8, indicating a less intense dust presence compared to March. The findings highlight spatial and temporal variations in aerosol characteristics across the regions, during the distinctive periods. From 15 to 16 March 2022, Saharan dust significantly reduced UV-B radiation by approximately 14% over the ATZ station (Athens, GR). Backward air mass trajectories showed that the dust originated from the Western and Central Sahara when, during this specific case, the air mass trajectories passed over GRA (Granada, ES) and PAY (Payerne, CH) before reaching ATZ, maintaining high relative humidity and almost stable aerosol properties throughout its transport. Lidar data revealed elevated aerosol backscatter (baer) and PLDR values, combined with low LR and ÅE values, indicative of pure dust aerosols.

Evaluation of speckle filtering configurations on Sentinel-1 SAR backscatter Analysis Ready Data (S1ARD) preparation framework on the Google Earth Engine platform for supporting rice monitoring activities

Implementing the Sentinel-1 SAR backscatter analysis ready data (S1ARD) preparation framework to Sentinel-1 C-band SAR data in the Google Earth Engine platform potentially enhances the quality of SAR data, facilitating the advancement of wide-scale, large-impact, and continuous SAR-supported RS applications such those for rice monitoring activities. Nevertheless, there is a lack of published works assessing different speckle filtering configurations available within the S1ARD preparation framework, particularly those directly associated with rice monitoring activities. This study quantitatively evaluated the performance of available speckle filtering parameters on the S1ARD preparation framework, analyzed their derived backscatter values over a rice-growing cycle, and utilized produced datasets as inputs to classify distinct classes of rice transplanting periods in two study areas by applying the random forest classifier. The backscatter analysis demonstrated that the mono-temporal speckle filtering frameworks yielded elevated backscatter values compared to the unfiltered dataset, which exhibited higher values than those derived by the multi-temporal frameworks. Furthermore, filtered datasets increased classification accuracies ranging from 9.30 - 13.95% and 17.23 - 25.94% in study area 1 and between 4.69 - 15.63% and 9.28 - 29.75% in study area 2, for OA and Kappa, respectively, than those produced by unfiltered datasets. Overall, the multi-temporal speckle filtering framework with a Lee filter, 15 number-of-image, and a 7 x 7 window configuration was recommended to apply to the S1ARD preparation framework to assist SAR-supported RS-based rice monitoring activities. Finally, the findings of this work offer direct guidance and recommendations about the behavior and contributions of Sentinel-1 C-band SAR data applied with distinct speckle filtering configurations yielded by benefiting the S1ARD preparation framework for aiding SAR-supported RS-based rice monitoring activities.

Retrieval of snow and soil properties for forward radiative transfer modeling of airborne Ku-band SAR to estimate snow water equivalent: the Trail Valley Creek 2018/19 snow experiment

Abstract. Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth observation mission. The snow scientific community has shown that Ku-band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent developments of tools like the snow micropenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer (SMRT) model, it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada, was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-band radar backscatter at a 100 m scale. Airborne Ku-band data were acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using RADARSAT-2 and TerraSAR-X data, soil background roughness properties were retrieved (msssoil=0.010±0.002), and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, were retrieved. Using the geometrical optics surface backscatter model, the retrieved effective soil permittivity increased from C-band (εsoil=2.47) to X-band (εsoil=2.61) and to Ku-band (εsoil=2.77) for the TVC domain. Using the SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve snow water equivalent (SWE) at the 100 m scale.

A method to assess the cloud-aerosol transition zone from ceilometer measurements

Cloud and aerosol contribution to the Earth's radiative budget constitutes one of the most significant uncertainties in future climate projections. To distinguish between clouds and cloud-free air, atmospheric scientists have been using a wide range of instruments, techniques, and algorithms with various detection thresholds. However, since the change from a cloudy to a cloud-free atmosphere may be gradual and, in some cases, far from obvious, recent research is questioning where the threshold between both phases should be established. These considerations lead to contemplate a transition zone (TZ) between cloud and cloud-free conditions. In the present study, backscatter profiles obtained by a Vaisala CL31 ceilometer were processed to assess the transition zone. First, two widely used cloud detection algorithms were applied and compared: the method provided by the ceilometer manufacturer (Vaisala) and Cloudnetpy, the algorithm from ACTRIS Cloudnet, a project devoted to aerosol, clouds, and trace gases research. Second, a sensitivity analysis was applied to the backscatter and signal-to-noise ratio thresholds used for cloud detection in Cloudnetpy. This methodology has allowed us to assess the vertical distribution of clouds, aerosols, the TZ, and its frequency of occurrence. Results indicate a gradual transition in backscatter retrievals from cloud to cloud-free, where particles detected near cloud boundaries induced higher backscatter values than those found further away. Depending on the thresholds used, we observed a 9.3% (with an estimated range of uncertainty of 5.420%) variation in cloud occurrence which can be attributed to TZ conditions. Analysing the whole backscatter profile, we found as many TZ conditions as cloudy values, which emphasises the importance of studying the vertical distribution of the TZ. Moreover, the analysis of TZ occurrence in height and time revealed that such conditions concentrate below 800m during night periods, although annual height-hour distributions involve a remarkable variability among seasons. These findings highlight the importance of either including an additional phase between ‘pure clouds’ and ‘pure aerosols’ or treating them as a continuum of suspended particles in the atmosphere.

Integrating deep learning, satellite image processing, and spatial-temporal analysis for urban flood prediction

Urban flooding is escalating worldwide due to the increasing impervious surfaces from urban developments and frequency of extreme rainfall events by climate change. Traditional flood extent prediction and mapping methods based on physical-based hydrological principles often face limitations due to model complexity and computational burden. In response to these challenges, there has been a notable shift toward satellite image processing and Artificial Intelligence (AI) based approaches, such as Deep Learning (DL) models, including architectures like Convolutional Neural Networks (CNN). The objective of this research is to predict near real-time (NRT) flood extents within urban areas. This research integrated CNN (U-Net) with Sentinel-1 satellite imagery, Digital Elevation Model (DEM), hydrologic soil group (HSG), imperviousness, and rainfall data to create a flood extent prediction model. To detect flooded areas, a binary raster map was created using calibrated backscatter values derived from the VV (vertical transmit and vertical receive) polarization mode of Sentinel-1 imagery, which was highlighted as having a significant impact on backscatter behavior and prediction results. Application of the model was demonstrated in urban areas of Miami-Dade County, Florida. The results demonstrated the capability of the model to provide rapid and accurate flood extent predictions at a spatial resolution of 10 m, with an overall accuracy of 97.05 %, F-1 Score of 92.49 %, and AUC of 93 % in the study area. The U-Net model’s flood predictions were compared with historical floodplain data and then using GIS overlay analysis, resulting in a Ground Truth Index of 84.05 % that shows the accuracy of the model in identifying flooded areas. The research incorporated crucial flood-influencing data (including rainfall) to the flood extent prediction models and expanded the focus models beyond major rainfall events only to encompass a wider range of flood events. The presented NRT flood extent mapping model has a broad range of applicability, including, but not limited to, the continuous monitoring of flood events and their potential impacts on civil infrastructure assets (e.g., construction, operation, and maintenance of roads and bridges), early warning systems for timely evacuation and preparedness measures, and insurance risk assessment.

Detection of molecular backscattering with a tapered fiber amplifier based coherent heterodyne lidar

Fiber based coherent heterodyne lidars are highly valued and robust tools especially in sensing of wind speed and turbulence in the atmosphere. The magnitude of aerosol backscattering is also possible to be analysed from the data. However, the aerosol backscattering values cannot be calibrated without the data of molecular backscattering reference, which has not been available earlier due to power and bandwidth limitations. We present the detection of aerosol and molecular backscattering simultaneously with a fiber based coherent lidar instrument utilising a tapered fiber amplifier that yields to a pulse peak power of 1.9 kW at the wavelength of 1053 nm. Further, our receiver bandwidth of 1.5 GHz enables the spectral analysis of aerosol and molecular scattering spectra, which are recorded and analysed for multiple altitudes up to 1 km. The results demonstrate the potential of coherent heterodyne lidars to extend their capabilities toward backscattering and extinction analysis.

ANALYSIS OF NOVASAR-1 S-BAND DATA IN DEVELOPING AN ALTERNATIVE LAND COVER MAPPING

Abstract. The Advanced Science and Technology Institute of the Department of Science and Technology (DOST-ASTI), through its Synthetic Aperture Radar and Automatic Identification System (SARwAIS) Project, has gained access to S-Band SAR images acquired by the NovaSAR-1 satellite of UK’s Surrey Satellite Technology Ltd. (SSTL) To help maximize the utility of these images especially in the aspect of terrain-related applications, their viability as potential alternatives to datasets like optical satellite images and other SAR images in characterizing land cover types was evaluated. Statistical analyses on the backscatter values from the tri-polarization ScanSAR datasets using Multivariate Analysis of Variance (MANOVA) and its corresponding post-hoc tests showed that there is a significant difference on the mean backscatter values at 0.05 level of significance. Moreover, Tukey’s honestly significant difference (Tukey’s HSD) test determined which pairs contribute to the significant difference. Using the Random Forest algorithm resulted in an accuracy of 66.93% without further optimization and/or reduction in the number of land covers being classified. Despite the relatively unremarkable accuracy score, it still showed potential for data augmentation with optical satellites for land cover mapping and other terrain-related applications.

FISHPOND STATUS MAPPING USING RADAR REMOTE SENSING

Abstract. The identification and monitoring of the status of all fishponds with Fishpond Lease Agreements (FLAs) in the Philippines is limited mainly to site visits. However, this approach is tedious, time-consuming, and costly due to the sheer number and locations of fishponds. This study explores the use of radar remote sensing in expediting the mapping of fishpond status (i.e., active, abandoned). Historical Sentinel-1A SAR images acquired from 2022–2023 covering identified fishpond areas were analysed. Several temporal statistics of their sigma nought backscatter values were calculated per polarization (i.e., VV and VH). Non-fishpond areas were masked out, and then Principal Component Analysis was applied to the stacked temporal statistics images. Afterwards, K-Means Cluster classification was applied to the first 7 components of the resulting PCA images to generate 4 classes. The resulting class with relatively low backscatter values is identified as water; “intermittent” water if their backscatter values vary greatly; and non-water or vegetated if their backscatter values are relatively high. Status was determined based mainly on the dominant class in each plot. Water class dominated plots are considered active; non-water or vegetated class dominated plots and plots that are covered mostly by “intermittent” water class are labelled as abandoned. Based on field-validated data, active fishponds, and old, abandoned fishponds (e.g., already have mangroves, or have no water control structures) were easier to correctly map, while abandoned fishpond with certain characteristics were harder to correctly identify. The approach is promising and can help BFAR map and monitor the fishponds in the country.

COMPARISON OF THE USE OF SENTINEL-1 SAR AND ALOS-2 PALSAR-2 IN MANGROVE ABOVEGROUND BIOMASS ESTIMATION IN SAN JUAN, BATANGAS, PHILIPPINES

Abstract. This study compares the potential of Sentinel-1 and ALOS-2 PALSAR-2 in estimating mangrove aboveground biomass (AGB) in San Juan, Batangas, Philippines. Mangrove forests are essential coastal ecosystems that are facing growing threats. One way of conserving them is by creating policies that can protect them. To do this effectively, information like AGB can be used as a guide. However, conventional AGB estimations are labor-intensive and ecologically disruptive. Conversely, remote sensing technologies, such as synthetic aperture radar (SAR), offer a more efficient alternative. Sentinel-1, operating in C-band, and ALOS-2 PALSAR-2, operating in L-band, are two prominent SAR platforms with global coverage, offering data for land cover classification, forest monitoring, and forest biomass estimation. This research used the backscatter values of Sentinel-1 and ALOS-2 PALSAR-2 as predictor variables in estimating mangrove AGB by correlating them to the observed AGB from a mangrove survey. The models developed using these platforms yielded limited accuracy, with low coefficient of determination (R2) (Sentinel-1 = 0.13; ALOS-2 PALSAR-2 = 0.12) and RMSE (Sentinel-1 = 8.72 Mg ha-1; ALOS-2 PALSAR-2 = 8.78 Mg ha-1). Potential sources of errors were identified, including small sample size and data noise. On the results, Sentinel-1 demonstrates a slightly better performance in terms of the R2 and RMSE in the modeling while ALOS-2 PALSAR-2 performed better in the validation, however, both still yielded suboptimal AGB estimates compared to other studies. Refining the models by incorporating additional parameters, exploring machine learning, and considering other data sources are recommended to enhance AGB estimation.

Beyond assimilation of leaf area index: Leveraging additional spectral information using machine learning for site-specific soybean yield prediction

Assimilating external observations of crop state in cropping system models is essential for making spatially explicit predictions of crop variables relevant in precision agriculture. Satellite-based leaf area index (LAI) estimates have been the most frequent variable used as a proxy of actual crop growth. However, additional information beyond LAI, like canopy N content, water content, and structure, can be retrieved from satellite observations. Including such variables by data assimilation directly is difficult because many crop models do not have corresponding state variables or the relationship between the observations and the process that regulates crop growth is unclear. Therefore, other approaches are required to include such information. In this study, we investigate the improvement in the predicted yield and feature impact on model outputs by using a hybrid approach that combines observations from Sentinel-1 and 2 time-series with the outputs from a process-based model embedded in a data assimilation framework and uses the Gradient-boosted trees regressor (GBTR) as predictive model. We used two regions with soybean fields: the US (13 K points) and Uruguay (400 K points). We found an advantage when using the GBTR as the predictive model (reduced RRMSE by ∼16%) compared to data assimilation. Adding the vegetation indices had a marginal improvement (reduced RRMSE by ∼1%), while the impact of adding reflectance and backscatter values was negative. The satellite-based features had a very small importance score, while features' impact on prediction was predominantly unclear, explaining the marginal predictive power added by satellite-based features. We found that features from the reproductive stages had the highest importance, while the importance of an index related to drought stress (NMDI) across the growing season provided insights for further improvement of data assimilation methods. However, more studies are required to better disentangle pathways towards further improvement in constraining crop models by ingesting satellite observations.

Time series analysis of L-band PALSAR-2 images in Istanbul and Kocaeli, Turkey

ABSTRACT Conducting long measurements of infrastructure deformation is a critical engineering task. Conventional methods are both time-consuming and expensive, limiting their use for large-scale applications. The synergy of synthetic aperture radar (SAR) and geographic information systems (GIS) offers a complementary approach. This study focuses on the feasibility of using time series analysis of L-band PALSAR-2 images to discover land displacements in Istanbul and Kocaeli, significant industrial and residential areas in Turkey. PALSAR-2 phase and intensity information were analyzed. For phase analysis, 14 L-band images from 2014 to 2021 were taken into account. Small baseline subset (SBAS) analysis was performed using 44 pairs, and results of the velocity, coherence and backscattering values are presented. Coherence of all pairs and their correlations were calculated. Principal Component Analysis (PCA) reduced the dimension of coherence pairs, enhancing feature extraction and the final geocoded velocity map revealed a fastest subsidence rate of −58 mm/yr and a mean subsidence of −20 mm/yr. These findings were confirmed through mean vertical velocity from Sentinel-1 datasets and field observations. The results showed that immature land subsidence in the mentioned areas are growing slowly, which can be taken as a serious risk in future.

Utilizing Multi-Temporal ALOS/PALSAR Backscatter Data to Detect Deforestation in the Indonesian Tropical Forest

Deforestation detection mapping has been analyzed using Synthetic Aperture Radar (SAR) data from the ALOS PALSAR satellite. SAR is a promising tool for deforestation mapping in tropical forest regions because microwave energy can penetrate clouds. This study examined the effect of SAR polarization on the accuracy of deforestation mapping and the seasonal variation of radar backscattering values in Riau Province, Indonesia. Image differencing was used to determine the threshold value for distinguishing between forest and non-forest areas. The difference between HV (horizontal transmit-vertical receive) and HH (horizontal transmit-horizontal receive) polarization was used as the discriminant variable. The correlation between radar backscattering value and accumulated rainfall was also analyzed to assess the effect of seasonal change. The findings demonstrated that the accuracy of deforestation detection mapping using HV polarization outperforms that achieved with HH polarization. Moreover, seasonal variations in rainfall were observed to influence radar backscatter signatures in bare soil, acacia, and oil palm, though no such influence was detected in forested areas. The accuracy of deforestation detection mapping, employing the thresholding method, reached a rate of 92.21%. During the period from November 2007 to July 2009, forested areas in Riau experienced a reduction, decreasing to 6.77%, and further decreasing to 6.08% by October 2010.

Forward modelling of synthetic-aperture radar (SAR) backscatter during lake ice melt conditions using the Snow Microwave Radiative Transfer (SMRT) model

Abstract. Monitoring of lake ice is important to maintain transportation routes, but in recent decades the number of in situ observations have declined. Remote sensing has worked to fill this gap in observations, with active microwave sensors, particularly synthetic-aperture radar (SAR), being a crucial technology. However, the impact of wet conditions on radar and how interactions change under these conditions have been largely ignored. It is important to understand these interactions as warming conditions are likely to lead to an increase in the occurrence of slush layers. This study works to address this gap using the Snow Microwave Radiative Transfer (SMRT) model to conduct forward-modelling experiments of backscatter for Lake Oulujärvi in Finland. Experiments were conducted under dry conditions, under moderate wet conditions, and under saturated conditions. These experiments reflected field observations during the 2020–2021 ice season. Results of the dry-snow experiments support the dominance of surface scattering from the ice–water interface. However, conditions where layers of wet snow are introduced show that the primary scattering interface changes depending on the location of the wet layer. The addition of a saturated layer at the ice surface results in the highest backscatter values due to the larger dielectric contrast created between the overlying dry snow and the slush layer. Improving the representation of these conditions in SMRT can also aid in more accurate retrievals of lake ice properties such as roughness, which is key for inversion modelling of other properties such as ice thickness.

Mapping mangrove above ground carbon stock of benoa bay bali using sentinel-1 satellite imagery

Mangrove forest ecosystems distributed in tropical countries play an important role in carbon sequestration. The presence of mangrove forests is estimated to be capable of reducing CO2 levels caused by air pollution. The process of photosynthesis absorbs CO2 gas from the atmosphere and stores it as organic matter in the form of plant biomass. The amount of carbon stock stored in mangrove forests can be estimated using a regression model based on satellite imagery pixel values and above ground biomass (AGB) measurements. This study used the backscattering value of SAR Sentinel 1 images and field AGB measurements to map carbon stocks in the mangrove forest ecosystem of Benoa Bay, Bali. The processed backscattering value is the result of VV and VH polarization in the SAR Sentinel 1 image acquired in 2022. Meanwhile, AGB is calculated using the allometric equation based on the mangrove’s species found at the study site. The biomass at the study location was estimated to be in the range of 64.18 Mg/Ha to 175.24 Mg/Ha, with an average of 115.34 ± 25.33 Mg/Ha. Therefore, the carbon stock of Benoa Bay can be estimated to have values in the range of 30.16 MgC/Ha to 82.36 MgC/Ha, with an average of 54.21 ± 11.09 MgC/Ha. In terms of estimating AGB biomass based on SAR pixel values, the VH polarization produces a better model (R2 = 0.635) than the VV polarization.

Exploring diel vertical migration and spatiotemporal variation of zooplankton backscattering strength using an acoustic Doppler current profiler instrument in the Halmahera Sea, Indonesia

Given its critical role in marine ecosystems, this study comprehensively examined zooplankton distribution and behavior in the Halmahera Sea. The temporal and spatial dynamics of zooplankton acoustic backscatter values were analyzed using a 153.6 kHz vessel-mounted acoustic Doppler current profiler (ADCP). Analysis was supplemented by biological sampling with a bongo plankton net. Further evaluation included the analysis of oceanographic and bathymetric data. The acoustic, oceanographic, and biological sampling data were obtained from the Jala Citra I ?Aurora? survey expedition in 2021, while the bathymetry data were obtained from the General Bathymetric Charts of the Ocean (GEBCO). The raw ADCP data, represented as digital counts, were transformed into mean volume backscattering strength (MVBS) expressed in decibels (dB) using sonar equations to yield a measure proportional to zooplankton biomass. Temporal observations revealed a diel vertical migration (DVM) pattern in zooplankton aggregation, characterized by movements responding to the daily solar cycle. Spatial observations indicated a higher zooplankton density in semi-enclosed waters than in open water. The high values of acoustic backscatter are not attributed to a single species of zooplankton. Biological sampling identified that Oncaea spp. and Oithona spp., a species from the Cyclopoida order, exhibit the highest abundance. The study concludes that the ADCP, based on acoustic backscatter measurements and data sampling, is an effective tool for detecting the presence and behavior of zooplankton.

Acoustic characterization of fish and macroplankton communities in the Seychelles-Chagos Thermocline Ridge of the southwest Indian ocean

In this study, we describe the dynamics of the sound scattering layers (SSLs), particularly those of fish and macroplankton communities in the epipelagic layer, in the Seychelles-Chagos Thermocline Ridge (SCTR) of the southwest Indian Ocean using hydroacoustic data, net sampling, and oceanographic information. Overall, the acoustic backscattering values of the fish community were considerably higher than those of the macroplankton. Both communities were more densely distributed in the SCTR than in its surrounding region. On the vertical profile, the acoustic peak of the fish community was at 17 m during the day; however, considerably high values of up to 82 m were observed at night. Below 26 m, macroplankton was seldom found, regardless of the time. Hydrographic properties, such as temperature, salinity, dissolved oxygen (DO), and chlorophyll fluorescence, in the SCTR, were similar; however, an area at 5–8°S, 67°E was cooler, saltier, and had slightly lower DO. Weak or moderate positive correlations were observed between acoustic and hydrographic features. Based on all net samples, the most abundant taxon in terms of the total number of samples was found to be krill (Euphausiacea, 81%), followed by lantern fish (Myctophum punctatum, 12%). Understanding the dynamics of SSLs, particularly epipelagic organisms, will help to better clarify the important ecological roles of these organisms and their ability to facilitate vertical incorporation into marine food webs.

A landslide dating framework using a combination of Sentinel-1 SAR and -2 optical imagery

Landslides are mass movements of rock or soil down a slope, which may cause economic loss, damage to natural resources and frequent fatalities. To support risk management, landslide dating methods can provide useful knowledge about the date of the landslide and the frequency of occurrences, and thus potential triggers. Remote sensing techniques provide opportunities for landslide dating and are especially valuable in remote areas. However, the use of optical remote sensing is frequently hampered by cloud cover, decreasing the success rate and accuracy of dating. Here, we propose a landslide dating framework that combines the advantages of optical and SAR remote sensing satellites, because optical monitoring provides spectral changes on the ground and microwave observations provide information on surface changes due to loss of coherence. Our method combines Sentinel-1 and -2 satellite data, and is designed for cases wherein the landslide causes vegetation decrease and terrain deformation resulting in changing Normalized Difference Vegetation Index (NDVI) and SAR backscatter values. This landslide dating framework was tested and evaluated against 60 published landslides across the world. We show that the mean accuracy of landslide dating reaches 23 days when using combined Sentinel-1 and -2 imagery, which is a pronounced improvement compared to using only optical Sentinel-2 images resulting in an accuracy of 51 days. This study highlights that a combination of optical and SAR remote sensing monitoring is a promising technique for dating landslides, especially in remote areas where monitoring equipment is limited or which are frequently covered by clouds. Our method contributes to identifying failure mechanism by providing reliable date ranges of landslide occurrence, assessing landslide hazard and constructing landslide early warning systems.

MONITORING OIL SPILL PROGRESSION AND OIL SPILL VOLUME USING SATELLITE IMAGES

Abstract. Satellite imagery is an indispensable tool in operational oil spills mapping. The smooth surface of oil sleeks make them visible in Synthetic Aperture Radar (SAR) imagery due to the low backscatter values making the oil spills distinctly dark in SAR images compared to the surrounding water. Oil extent delineation using SAR is very common but aside from using SAR images, optical satellite images are also of great help by capitalizing on the spectral characteristics of the oil surface to isolate the oil extents. The delineated extents are post-processed in a Geographic Information System (GIS) software where the oil spill area is calculated and published as oil extent maps. Though the area can be calculated, estimating the volume of oil present in the extent map remains to be a challenge. To answer this problem, this paper proposes a Physics-based solution to calculate the thickness of the oil by analysing local maxima in the reflectance spectrum of oil-covered water. Using this technique, oil thickness was estimated from Sentinel-2 multispectral images of an oil spill. It yielded an average oil thickness estimate of 0.560 ± 0.164 microns. The calculated thickness is used to estimate the volume of spilled oil.

Optimising carbon fixation through agroforestry: Estimation of aboveground biomass using multi-sensor data synergy and machine learning

As agricultural land expansion is the primary driver of deforestation, agroforestry could be an optimal land use strategy for climate change mitigation and reducing pressure on forests. Agroforestry is a promising method for carbon sequestration. With recent advancements in geospatial and data science technology, the ability to predict aboveground biomass (AGB) and assess ecosystem services in agroforestry is rapidly expanding. This study was conducted in the Belpada Block of Balangir, Odisha, a forest-dominated region of eastern India. We recorded species occurrence and measured plant parameters, including Circumference at Breast Height (CBH), height, and geolocation, in 196 plots (0.09 ha) in agroforestry intervention sites and noted the tree species. This study used Sentinel-1 and Sentinel-2 multi sensor data to achieve data synergy in AGB estimation. Three machine learning models were used: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The RF model exhibited the highest level of prediction accuracy (R2 = 0.69 and RMSE = 17.07 Mg/ha), followed by the ANN model (R2 = 0.63 and RMSE = 19.35 Mg/ha), SVM model (R2 = 0.54, RMSE = 21.97 Mg/ha. The spectral vegetation indices that are (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Modified Simple Ratio (MSR), Modified Soil-Adjusted Vegetation Index (MSAVI), Difference Vegetation Index (DVI), and SAR backscatter values, were found important variables for AGB prediction. The findings revealed that agroforestry interventions and plantations resulted in an average carbon stock increase of 15 Mg/ha over five years in the study area. The Plant Value Index (PVI), which indicates the importance of species in the local economy and biomass carbon storage, showed that Tectona grandis was the dominant species with the highest PVI value (88.35), followed by Eucalyptus globulus (56.87), Mangifera indica (53.75), and Azadirachta indica (15.45). This approach enables the expansion of monitoring efforts to assess carbon stock in agroforestry systems, thereby promoting effective management strategies.