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• The potential of optical and SAR data was explored for estimating grassland height. • The RF, GBRT, and CART models were used to estimate grassland height. • GBRT model performed than RF and CART models for grassland height estimation. • The optical remote sensing data performed better than SAR remote sensing data. • Coupling optical and SAR data can effectively improve the estimation accuracy. Grassland height is an important indicator used to evaluate ecological environments in grasslands. The study explored the potential of optical and SAR (synthetic aperture radar) remote sensing data in estimating grassland height with machine learning methods and constructed the grassland height remote sensing inversion model. Then the mean grassland height in August 2015 was estimated with the inversion model in Inner Mongolia Autonomous Region, China. Among classification and regression tree (CART), random forest (RF), and gradient boosting regression tree (GBRT) models, the GBRT model had the highest estimation accuracy. There was little correlation between SAR data and grassland height, and SAR data produced poor accuracy for grassland height estimation. The grassland height could be better estimated by optical remote sensing data. The best estimation accuracy (GBRT model: for training data: R2 = 0.71, RMSE = 3.58 cm, P < 0.01; for test data: R2 = 0.58, RMSE = 3.94 cm, P < 0.01) was achieved by the combination of optical and SAR remote sensing data. However, SAR data played an auxiliary role, and the estimation of grassland height was mainly realized by optical data. The average vegetation height of the grasslands in Inner Mongolia in August 2015 was 19.15 cm, gradually decreasing from northeast to southwest. The study proposed a high-precision method for estimating grassland height, which provided a basis for studying grassland environments and conditions. [ABSTRACT FROM AUTHOR]

The Suez Canal is a crucial international waterway due to its strategic location. The significant traffic flow not only stimulates economic development along the coast but also leads to a high frequency of oil spill accidents, which negatively impact the ecosystem and natural resources. Synthetic aperture radar (SAR) is an important remote sensing technology for monitoring oil spills, offering all-day and all-weather capabilities. However, special disturbances (SD) caused by imaging conditions, sensor parameters, and other factors can affect image quality, reducing the accuracy and efficiency of oil spill detection. To mitigate the negative impact of SD, an original oil spill detection framework was developed, based on the analysis of these disturbances, to detect oil spills at the northern entrance of the Suez Canal from 2015 to 2019. The framework included an advantageous featureset with SD adaptability and designs a classifier, Boosting Random Support Vector Machine (BRSVM), which combines a boosting strategy with Support Vector Machine (SVM). The study found that the superiority of the featureset was pivotal in oil spill detection. The classification accuracy and F -1 score achieved by BRSVM were 94.72 % and 95.33 %, respectively, outperforming other algorithms in functionality. These results indicate that the proposed framework holds significant potential for applications requiring large-scale, automated oil spill detection. • Special Disturbances(SD), including speckle noise and blurred boudaries, are affected by SAR imaging process and environment. • The existence of SD put a limitation in oil spill motoring and auto-detecting. • Image quality, which reflects the impact of SD on SAR images, is analyzed to divide comparison groups. • The superiority and stability served as metrics to construct the SD-less-impacted featureset. • A model combined SVM with boosting is proposed, allowing abnormal samples caused by SD be effectively exclusion. [ABSTRACT FROM AUTHOR]

Coastal ecosystems are under increased pressure from climate change and anthropogenic impacts. Tidal flat maps are necessary for managing, protecting, and restoring coastal ecosystems. Most of the existing tidal flat mapping approaches that are dependent on modelled tidal elevations, suffer high uncertainty over offshore, especially over areas where the tidal conditions have been changed by human-made structures and anthropogenic impacts. Meanwhile, these approaches separate the tidal flats in estuaries by a coastal distance rather than actual distance of saltwater intrusion. In this study, we propose a tidal flat mapping approach, using a new method to avoid the dependence on tidal elevations. The proposed approach synthesizes large-area satellite observations over a time period, to acquire the high and low tidal datum, and then classifies land and water under each tidal datum utilizing an adaptive binary classifier. The tidal flats are identified as areas being water during high tides and being land during low tides. Furthermore, the proposed approach uses the distribution of salt vegetation (i.e., mangroves) as a salinity proxy to particularly identify the tidal flats in estuaries. The proposed approach was applied for mapping the tidal flats in southern China (covering over a half of the coastline of China) utilizing Sentinel-1 Synthetic Aperture Radar (SAR) data from July 2016 to July 2018. The results achieved an overall accuracy of 92.4% and an agreement rate of 70.5% with the most recent global tidal flat map. The proposed approach does not depend on the modelled tidal elevations or large sample sets and could be easily applied for mapping tidal flats globally. [ABSTRACT FROM AUTHOR]

The eastern Mediterranean region is a vital hub for oil transportation and production because of its strategic location between Europe, Asia, and Africa. But its unique attributes, including narrow shipping routes, heavy marine traffic, and proximity to vulnerable ecosystems, render it particularly susceptible to accidental oil spills. In this research, an oil spill detection model, along with bathymetric and oceanographic parameters, was used to track oil spills that occurred at the Syrian Baniyas Station in the Eastern Mediterranean on August 23, 2021. Furthermore, the study employed a pairwise comparison matrix (PWCM) to assess the relative importance of wind speed and direction, water depth, and sea surface temperature (SST) in the dispersion of oil spills. Analysis of Sentinel-1 data obtained prior to, during, and after the incident revealed the accumulation of oil slicks along the Syrian coast from Baniyas to Latakia for up to twenty days. The spilled oil reached the coast of Cyprus six days after the incident. The study determined that wind speed and direction played a critical role in the dispersion of spilled oil, while water depth and SST were comparatively less significant factors in this process. The overall accuracy (OA) and Kappa coefficient (KC) for land, water, and oil slick classes derived from the random forest (RF) algorithm ranged from 90 % to 98 % and from 0.86 to 0.98, respectively. The spread of oil slicks at the incident location was revealed by the decorrelation stretch and band ratios of Sentinel-2 MultiSpectral Instrument (MSI) data. The accidental oil spill could have negative effects on the organic carbon cycle, chlorophyll production, and ecosystem productivity. It is essential to consider the vulnerability of specific regions in the Eastern Mediterranean to oil spills when developing adaptation strategies. • SAR data was used to track accidental oil spills in the Eastern Mediterranean. • Environmental parameters were monitored to assess the incident's impacts. • Wind conditions played a critical role in the dispersion of oil spills. • This study validates the Sentinel sensor's efficacy in mapping oil spills. [ABSTRACT FROM AUTHOR]

• Introduction of a novel model that integrates OBIA and the attention mechanism for precise orchard mapping. • The contribution of SAR is studied in orchard classification. • The model obtains more accurate classification results than RNN-based and CNN-based deep learning models. • The uncertainty of the super-pixel size on classification accuracy is investigated. Reliable and accurate classification of orchards is important for the dynamic monitoring of large-scale orchards and food security evaluation. At present, the very similar spectral profiles of different fruit trees and the high susceptibility of optical data to interference by weather conditions limit the resolution of orchard classification. Synthetic Aperture Radar (SAR) imagery provides an advanced solution to this problem, with the advantages of being immune to weather conditions and advances in deep learning techniques. This paper presents an orchard classification model (STCM) with optical and SAR fusion, which integrates the advantages of the Simple Non-Iterative Clustering (SNIC) super-pixel algorithm with a deep learning algorithm based on a multi-headed attention mechanism, and it achieves the best classification accuracy of above 0.82 in comparison with existing deep learning models. Meanwhile, the model has exceptional classification accuracy and robustness in a study area with fragmented plots, many fruit tree species, and various distributions of optical images. In the absence of optical data, the classification accuracy of the STCM model using only SAR data is around 0.70, which makes the model have a promising potential application value. This study provides a technical solution for accurately obtaining different orchard categories in high-resolution remotely sensed orchard images. The study helps to improve the management and operation of orchards and provides a strong basis for decision-making in the fruit industry to enhance the sustainability of the global fruit industry. [ABSTRACT FROM AUTHOR]

Urban heat islands (UHI) represent one of the substantial human-induced challenges endangering urban livelihoods. UHI and climate change have significant interactions. Progressively rising warming tendencies are exacerbating higher temperatures in UHI regions. Using time-series data, we examined annual and seasonal day-night UHI intensity and its driving factors in Greater Cairo (GC), which is considered Egypt's megacity, between April 2021 and March 2022. Since Sentinel-1 synthetic aperture radar (SAR) proposes unique horizons for mapping urban areas, we used SAR data and the Random Forest (RF) algorithm for urban footprint extraction in the search region. Sentinel-3 SLSTR thermal images were utilized to derive annual and seasonal day-night land surface temperatures (LST) during the research period. A spatio-temporal assessment of tropospheric NO 2 , SO 2 , CO, and aerosol concentrations was performed via the Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI), MODIS, and Google Earth Engine (GEE) applications. The annual average UHI intensity was higher in Egypt's megacity during the nighttime (3.27 K) than during the daytime (0.50 K). This finding indicates that driving forces are boosting temperatures in urban regions at night. The seasonal average day-night UHI intensities have the highest values during the winter, followed by the summer. Significant influencing factors concerning UHI intensity varied between the daytime and nighttime. Moreover, NDBI, PV, NDVI, altitude, and wind speed were the driving factors that were influential during the day and at night. These results can be used to establish effective adaptation measures for combating the impacts of climatic change and UHI. • This study investigated annual and seasonal day-night UHI using satellite data. • The study used Sentinel-1 SAR data to map urban footprint. • The relationship between UHI and driving factors varied between day and night. • This study verifies the capability of the Sentinel data to examine UHI intensity. • This study provides essential information to help UHI mitigation. [ABSTRACT FROM AUTHOR]

The objective of the paper is to investigate the sensitivity of Landsat OLI and C-band radar signals to monitor an agricultural area affected by soil salinization and land degradation. The chosen test area - Braila Plain has the special particularities such as: dry climate, high annual average temperatures (9-110C), very dry and hot summers which cause a large potential evapotranspiration and conduct to a moisture deficit in soil, alkaline soils, winter winds with an average speed of 2.7 - 3.4 m/s. The soil type and climate conditions favor the culture of maize (50%), wheat and successive crops (16%), alpha-alpha (18%), sugar beet (6%), sunflower (7%), vegetables and other crops (3%). Taking into account the soil type, climate conditions and geomorphological characteristics of the studied area, the paper focuses on evaluation of Sentinel-1 sensor capabilities to monitor soil degradation and surface soil moisture. A multi-temporal series of Sentinel-1 data gathered from October 2014 until January 2015 is used. Crop growing stages are investigated with multi-temporal Landsat OLI and MODIS data. The normalized difference vegetation index (NDVI), specific leaf area index, land thermal index, soil moisture index and soil salinity information are retrieved from Landsat data. The potential evapotranspiration is computed from MODIS data to evaluate the effects of soil salinity on growing crops. The results confirm soil degradation and the synergy of using multi-spectral and radar data for crops monitoring. [ABSTRACT FROM AUTHOR]

Landscapes in the East African Rift System are formed by complex effects of the active continental extension zone. These effects are caused by the Somalian micro-plate's eastward drift away from the Nubian plate, as well as by volcanic, erosional and depositional processes. Tectonic processes in this region have significantly contributed to the formation of the current drainage systems and landforms. This study focuses on the morphotectonics of the Makuyuni catchment with an analysis of topography, drainage networks, stream longitudinal profiles and lineaments. This analysis reveals a morphostructural control with an N–S trend for the uplifted Masai Block, as well as tectonic deformation in the Makuyuni catchment area (NE of Lake Manyara). Whereas basin asymmetry analysis shows basin tilting associated with active faulting and uplifting near the Essimingor volcanic cone, in this catchment the steepness and concavity indices, coupled with lineaments obtained from interpretations of Synthetic Aperture Radar satellite scenes, show an uplifting along micro-faults. Hypsometry curves reveal that subcatchments on the right side of the Makuyuni River are in a mature equilibrium phase, whereas those at the left side are in a younger stage of maturity. An investigation of base level and statistical moments of the hypsometric curves provides evidences for the spatial distribution of gully erosion phenomena. Such erosion processes are due to tectonic deformation in the northern parts of the Makuyuni catchment. These results of regional tectonic instability suggest that tectonic processes are a significant factor for the current landscape evolution in the Lake Manyara basin. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents some new concepts in the petroleum systems of the northern Labrador and southern Baffin Island offshore region of eastern Canada. The focus of this work is the region of the Hekja O-71 gas discovery of 1979 by Aquitaine, one of only five wells drilled between 1976 and 1980 within an area covering some 166,000 square kilometers within the Saglek Basin. This study emerged from a broad scale re-examination of the petroleum potential of this area “from the crust up” using regional reflection, refraction and potential field data. An opportunity to use SAR (synthetic aperture radar) data from the RADARSAT-1 Earth observation satellite was taken to incorporate alternative data sources to support this exploratory review and resource assessment. Examination of the final map of interpreted slick-like features on the sea surface, revealed a close correlation of some of these occurrences to several bathymetric features which have underlying seismic signatures similar to previously identified gas hydrate “pipes” or chimney anomalies as seen in data from offshore Nova Scotia and in the Irish Sea. Globally, many active marine hydrocarbon seeps appear on the seafloor as “pockmarks”; in this case no seafloor depressions were seen on conventional multi channel 2-D seismic data associated with active seepage. Instead, very distinct mound-like structures are seen associated with seeps at two locations east of Hudson Strait. The presence of significant cold water coral in close proximity to the active seepage sites may show a link between seafloor petroleum seepage and coral reef development similar to that observed offshore Norway. Close examination of the near surface seismic character led to the interpretation of what was originally seen as seismic noise in vintage data, as likely being fluid escape chimneys or gas pipes, along the eastern edge of the Hekja structural complex, where favourable hydrocarbon trapping conditions are present. No active seeps were seen there; this may be due to other factors, such as the nature of the fluids that are escaping or the possibility of self sealing activity by biological agents such as bacterial matting with associated carbonate hard-ground formation. One seep anomaly was noted relatively close to the major basaltic eruptive complex near the Gjoa G-37 well. This may be due to the presence of volcaniclastic- hosted hydrocarbons similar in style to those recently discovered at the Rosebank field in Paleocene volcanics in the Faroe-Shetland Basin. The occurrence of an active seep in a volcanically-dominated terrain raises the potential for a radical shift in accepted types of petroleum leads and where they may occur. The presence of apparent oil seepage versus the past history of finding only gas-prone accumulations is a strong indicator that a second, oil-prone petroleum system exists in the Saglek Basin. The signs of active petroleum systems as shown by oil seep anomalies with confirming seismic evidence of discrete sea floor structures in close proximity makes a compelling argument for re-examining these areas with an eye towards stratigraphic instead of structural plays. [Copyright &y& Elsevier]

Abstract: For estimating forest biomass and mapping of forest carbon content, an accurate classification of tropical forest stages is an important step. The successional stage of regenerating forests in a forest age map was a viable surrogate for forest biomass in an area north of Manaus City, Brazil. The forest stages considered were: (i) mature forest, (ii) regenerating forest <3 years, (iii) regenerating forest 3–5 years, (iv) regenerating forest 6–10 years and (v) regenerating forest 11–18 years. Areas of pasture were mapped also. Synthetic Aperture Radar (SAR) bands and optical TM (Thematic Mapper) bands were used to classify those classes, using the forest age map as the reference. The remotely sensed data comprised 20 bands (SAR bands from JERS-1, SIR-C and XSAR and optical bands from Landsat/TM) upon which discriminant analysis (DA) were used. DA results pointed to increased class discrimination when using SAR and TM data in relation to SAR data only. The bands selected were used as input to a neural network based classifier. Classification accuracy using SAR bands alone was around 30% for the six land cover classes. When regenerating forest stage classes were merged into a single class, the classification accuracy increased to around 80%. SAR data alone was unable to discriminate regenerating forest stages, having limited ability to discriminate between the subtle tonal/textural characteristics of each stage. A data set comprising TM and SAR bands showed increased classification accuracy in relation to SAR data alone, although some confusion between regenerating forest stages was still present. Following merging of regenerating forest stages into young (0–5 years) and intermediate (6–18 years), the overall accuracy was around 87%. The combination of SAR and TM bands were essential for the discrimination between regenerating forest stages. Pasture and mature forest were discriminated accurately in both SAR data alone and in the combined SAR and TM data. [Copyright &y& Elsevier]

Multi-temporal remote sensing imagery has been regarded as an effective tool to monitor cropland. But optical sensors often miss key stages for crop growth because of clouds, which poses challenges to many studies. The synergistic of SAR and optical data is expected to lift this problem, especially in areas with persistent cloud cover. However, due to the different characteristics of optical and SAR sensors, it is difficult to build a relationship between the two with most existing methods, let alone construct the long-time correlations to fill optic observation gaps using SAR data. Inspired by deep learning, this study presents a novel strategy to learn the relationship between optical and SAR time series based on the sequence of contextual information. To be specific, we extended the conventional CNN-RNN to build Multi-CNN-Sequence to Sequence (MCNN-Seq) model, and formulate the correlation between the optic and SAR time series sequences. We verified the MCNN-Seq model and found that the accuracy of the predicted optical image was determined by crop types and phenological stages, both in the spatial and temporal domain, respectively. For several crops, such as onion, winter wheat, corn, and sugar beet, our predictions are fitting well with R2 0.9409, 0.9824,0.9157, and 0.9749, respectively. Compared to CNN and RNN, the simulation accuracy achieved by the MCNN-Seq model is much better in terms of R2 and RMSE. In general, results demonstrate that deep learning models have the potential to synergize SAR and optical data and provide replaceable information when the optical data has a long data gap due to the persistent clouds. • The MCNN-Seq is developed to synergize SAR-Optical data in long time series. • The SAR-NDVI estimator built on SAR-Optical correlation aims to fill NDVI gaps. • The SAR-Optical synergistic is self-adaptive according to crop types. • Experiments on croplands demonstrate its capacity in filling optical values. [ABSTRACT FROM AUTHOR]

One important issue in wildlife conservation is the identification of the groups of species that need greater conservation measures. In this regard, maps of habitat degradation are useful tools. However, water bird habitat mapping in wetland ecosystem is difficult due to the large extent and complexity of wetlands and the narrow boundary between the habitats of different groups. In this paper, the capabilities of synthetic aperture radar (SAR) data were applied to overcome these limitations and to provide a practical scheme to conserve water bird habitats. The significance of this paper is the novel application of the SAR capabilities for water bird habitat conservation. These data were used to separate the habitat of the three water bird groups and to determine the destroyed habitats that should be protected for water birds in wetlands with unstable conditions. Using Sentinel-1 (S1) data, habitat maps for the water birds were created during the inundation period in the Hamoun-e-Hirmand. These habitats included the shrub swamps, flooded meadows, and open water. Based on the four-month flooded period of the wetland and the S1 image archive, habitat maps were created for six dates. The SAR backscattering coefficients of these three habitat classes were first analyzed to investigate the potential of the S1 images to differentiate between the habitat classes. A support vector machine (SVM) was then developed to separate the habitat classes. The accuracy of the six produced maps was calculated to be between 81% and 87.5%, which confirmed the ability of the S1 images to differentiate between water bird habitat classes. Based on the habitat degradation analysis, Anatidae, Charadriiformes, Ciconiiformes, Gruidae , Pelecanidae and the diving ducks need greater conservational measures in their feeding habitats (flooded meadow and open water). This paper introduced S1 data as an effective tool for mapping the habitats of each group of water birds in a wetland. [ABSTRACT FROM AUTHOR]

Due to the penetration capacity of synthetic aperture radar (SAR) data through clouds and hazy atmospheric circumstances like fog, smog, light rain, mist etc., it has ability to continuous observation of flood events for producing accurate, rapid and cost effective flood mapping. A study using time series RADARSAT SAR images in flood water detection, monitoring of spatial extent and propagation of flood inundation were described and analysed in this paper. The SAR images were first calibrated, geometrically corrected and filtered. Afterward, threshold method was applied to extract the inundated areas from the SAR images. In threshold method, density slicing technique was used to separate the open water and non-water (land) areas from the images. Later, to delineate the actual flooded area, permanent water bodies (e.g. river, lake, ponds etc.) were subtracted from the open water. Flood maps were super-imposed and analysed to find out the nature of spatial extent, duration of flood and to show how flooding spread through time. This study illustrates that the SAR data along with GIS can be used effectively for flood water mapping, monitoring and analysing the propagation of flood water in a flood prone area. Therefore, the findings of this study will help to take initiative to reduce the flood hazard impact and increase the flexibility in the process of flood management. [ABSTRACT FROM AUTHOR]