Use Of Sentinel-1 Data Research Articles

Assessment of the effects of winter wheat scattering on SAR backscatter for soil moisture estimation based on a radiative transfer model

ABSTRACT This study evaluated the potential of the Single Scattering Radiative Transfer (SSRT) model coupled with the Oh model to retrieve surface soil moisture (SSM) in two drip-irrigated wheat fields in the Tensift al Haouz plain, over three growing seasons (2016–2019). This research focuses on evaluating isotropic scattering and Rayleigh scattering by calibrating the coupled SSRT_Oh model, using data gathered on the calibrated field. The data includes measured SSM at 5 cm depth, Leaf Area Index (LAI) and vegetation height (H). The aim is to fit the extinction coefficient through comparing simulated and Sentinel-1 backscatter, at VV and VH polarizations. The validated field data were used to retrieve SSM. Calibration revealed the best retrieval for Rayleigh scattering, at VV polarization, with an RMSE of 1.25 dB, a correlation coefficient (r) of 0.86, and a bias of 0.1 dB, whereas, the isotropic approach yielded r, RMSE and bias values of 0.83, 1.37 dB and −0.01 dB, respectively. Rayleigh’s performance remained unchanged throughout the inversion process for SSM retrieval, at VV polarization, with r of 0.87 against 0.86 for isotropic scattering. To enhance our findings, the introduction of dynamic extinction parameter and exclusive use of Sentinel-1 data to describe vegetation could improve SSM retrieval.

Verification of the accuracy of Sentinel-1 for DEM extraction error analysis under complex terrain conditions

The successful launch of the Sentinel-1 satellite in 2014 brought a large amount of free SAR images to researchers and scholars, and its application in the fields of ocean monitoring, land use change, natural disaster monitoring and emergency response is becoming increasingly mature and precise. The main applications of InSAR can be categorized into surface deformation monitoring and DEM generation. Sentinel-1 was initially designed for surface deformation monitoring; thus, there are fewer relevant studies on the use of Sentinel-1 data for DEM extraction. However, as the only SAR satellite whose data are currently free and openly available and whose data are constantly updated, it is highly important to study its sources of error in the DEM generation process and the accuracy of its products. In addition, the SAR data provided by the Sentinel-1 satellite has the advantages of high resolution, all-day, all-weather, providing a large data source for DEM production. Taking the Ankang area as an example, this paper analyzes the influence of the InSAR spatiotemporal baseline, ground cover, terrain factors, SAR imaging and other factors on the accuracy of the Sentinel-1-extracted DEM using multisource ground observation data to validate its feasibility for terrain mapping in complex terrain. Finally, we look forward to how to effectively improve the quality of Sentinel-1 DEM products to provide guidance and a reference for subsequent research on DEM extraction using Sentinel-1 SAR images and designation of Sentinel-1 C satellite’s parameters.

ESTIMATION OF SUGARCANE YIELD USING MULTI-TEMPORAL SENTINEL 2 SATELLITE IMAGERY AND RANDOM FOREST REGRESSION

Abstract. Advancements in remote sensing techniques have greatly enhanced crop monitoring and yield estimation, with spectral vegetation indices (VIs) serving as a key component. Our study investigates the use of Sentinel-2 data, notable for its red-edge bands, in estimating sugarcane yield in Ethiopia's Awash Basin. Utilizing 22 VIs from S2 imagery, our approach combines Random Forest (RF) regression with the Recursive Feature Elimination (RFE) algorithm to improve the accuracy of sugarcane yield predictions. The results demonstrate the superior performance of the RF-RFE method over traditional RF with full datasets and Stepwise Multiple Regression (SMR). Particularly, VIs focusing on the red-edge spectral bands of S2 - such as NDVIre1n, NDVIre2n, NDVIre3n, NDRE1 and NDRE2 - were crucial in enhancing prediction precision. These indices from the red-edge and NIR narrow bands consistently influenced yield estimations in both the Wonji-Shoa and Metehara estates. The study underscores the critical role of the RFE algorithm in optimal variable selection, reinforcing earlier findings that precise variable choice can substantially boost model accuracy. The enhanced performance of the RF model when paired with the RFE algorithm was evident, emphasizing the importance of variable selection in accurate yield predictions. Employing the Out-of-Bag RMSE (OOB_RMSE) error estimate for evaluation, we observed variations in OOB_RMSE performance with different RF parameters, identifying the ntree value of 500 as optimal for the studied regions. The RF-RFE model’s estimations showed lower errors and higher correlation coefficients, proving its efficacy over a full dataset approach, which faced challenges with traditional VIs' saturation. Our findings align with earlier studies, highlighting the efficiency of S2’s red-edge bands in diverse estimation tasks and a shift towards using freely available broadband images like S2, over hyperspectral imagery, due to reduced data redundancy and processing costs. In conclusion, our findings reveal that RF regression, particularly when integrated with the RFE algorithm, is a powerful tool in remote sensing applications. The S2 imagery is optimal VIs, predominantly from the red-edge bands, exhibit significant potential for sugarcane yield estimations. The impressive results of the RF-RFE method, evident in metrics like MAE, MAPE, RMSE, Mean percentage, and R2, advocate its invaluable role in sugarcane yield prediction, highlighting its potential for optimizing irrigation management strategies and broad-spectrum agricultural planning.

Limitations in the use of Sentinel-1 data for morphological change detection in rivers

ABSTRACT The identification of morphological changes occurring along river channels is essential to support river process understanding, assess sediment budgets and evaluate the effectiveness of river management. Among available remote sensing techniques, space-borne synthetic aperture radar (SAR) could potentially provide a powerful complement to optical imagery for this task. However, very few studies have been carried out on the use of SAR datasets to study erosion and deposition processes in river channels. In this work, we investigate the potential of change detection analysis based on Sentinel-1 data, by comparing variations of radar backscattering to river morphology changes identified through high-resolution drone acquisitions. We considered a time series of two years of Sentinel-1 data relative to a period where, despite a moderate fluvial event occurred, morphological changes have been significantly detected in multitemporal drone point clouds. Satellite optical imagery (planet.com) and hydro-meteorological data were used to support the analysis and interpret results. The results show that the spatial and temporal resolution of Sentinel-1 is currently not suitable for accurate discrimination of morphological changes related to river dynamics at local scale. Other spaceborne sensors with sub-metric ground sampling distance and/or daily revisit time would be probably suitable; however, so far, this option would need the use of commercial solutions with a consistent increase of the costs of the investigation.

Monitoring direct drivers of small-scale tropical forest disturbance in near real-time with Sentinel-1 and -2 data

Advancements in satellite-based forest monitoring increasingly enable the near real-time detection of small-scale tropical forest disturbances. However, there is an urgent need to enhance such monitoring methods with automated direct driver attributions to detected disturbances. This would provide important additional information to make forest disturbance alerts more actionable and useful for uptake by different stakeholders. In this study, we demonstrate spatially explicit and near real-time methods to monitor direct drivers of small-scale tropical forest disturbance across a range of tropical forest conditions in Suriname, the Republic of the Congo and the Democratic Republic of the Congo. We trained a convolutional neural network with Sentinel-1 and Sentinel-2 data to continuously classify newly detected RAdar for Detecting Deforestation (RADD) alerts as smallholder agriculture, road development, selective logging, mining or other. Different monitoring scenarios were evaluated based on varying sensor combinations, post-disturbance time periods and confidence levels. In general, the use of Sentinel-2 data was found to be most accurate for driver classifications, especially with data composited over a period of 4 to 6 months after the disturbance detection. Sentinel-1 data showed to be valuable for more rapid classifications of specific drivers, especially in areas with persistent cloud cover. Throughout all monitoring scenarios, smallholder agriculture was classified most accurately, while road development, selective logging and mining were more challenging to distinguish. An accuracy assessment throughout the full extent of our study regions revealed a Macro-F1 score of 0.861 and an Overall Accuracy of 0.897 for the best performing model, based on the use of 6-month post-disturbance Sentinel-2 composites. Finally, we addressed three specific monitoring use cases that relate to rapid law enforcement against illegal activities, ecological impact assessments and timely carbon emission reporting, by optimizing the trade-off in classification timeliness and confidence to reach required accuracies. Our findings demonstrate the strong capacities of high spatiotemporal resolution satellite data for monitoring direct drivers of small-scale forest disturbance, considering different user interests. The produced forest disturbance driver maps can be accessed via: https://bartslagter94.users.earthengine.app/view/forest-disturbance-drivers.

Development of an emission estimation method with satellite observations for significant forest fires and comparison with global fire emission inventories: Application to catastrophic fires of summer 2021 over the Eastern Mediterranean

In the past few decades, forest fires have increased in number and severity, especially in the Mediterranean regions of Türkiye and Greece, where significant fires caused damage to thousands of hectares of land as well as wildlife. The main objective of the present study is to develop an emission estimation method with satellite-based burned area data from significant forest fire events in the Eastern Mediterranean in July–August 2021. In the first stage of this study, pre-fire and post-fire images of the study area acquired by the Sentinel-2 satellite were processed to calculate the normalized burn rate difference index (dNBR). Then, CORINE Land Cover (CLC) data were used for detecting land cover classes in the burned areas. Atmospheric emissions of NOx, CO, SO2, total suspended particulate matter (TSP), particulate matter with diameters that are equal to or smaller than 2.5 μm (PM2.5), and black carbon (BC) were estimated using the EMEP/EEA Tier 2 - technology-specific approach method, in which burned area maps were retrieved using Sentinel-2 imageries and later combined with land cover type and burning efficiency to estimate the quantity of burning biomass emissions. Emission factors were then used to estimate the fires' trace gas and aerosol emissions. The results showed that the highest burned areas were found in the western Mediterranean region in Türkiye and Central Greece (⁓50,000 ha). The atmospheric emissions from these fires were calculated to be similar in both countries. Furthermore, emission amounts were compared with three different global fire emission inventories including GFAS, GFED, and FINN. The emissions obtained from the GFAS database were the highest emissions of the four emission estimation approaches and our estimated emissions were close to the GFAS. Emissions calculated from the other two databases (FINN and GFED) mostly provided underestimated emissions. The emission uncertainties in this study mainly originated from assumptions regarding the inclusion of burned area efficiency in emission calculations, the landcover dataset, and the emission factors used. Overall, this study is considered a new approach to emission calculations using Sentinel-2 data. This research provides further insight into the use of Sentinel-2 data in emission calculation applications at the local to regional scales.

Total Stem Biomass Estimation Using Sentinel-1 and -2 Data in a Dense Coniferous Forest of Complex Structure and Terrain

Accurate above-ground biomass (AGB) estimation across multiple spatial and temporal scales is essential for mitigating climate change and optimizing forest management strategies. The aim of the present study was to investigate the potential of Sentinel optical and Synthetic Aperture Radar (SAR) data in reliably estimating the plot-level total stem biomass (TSB), which constitutes the dominant material among the different tree components of AGB (stem, branches, and leaves). The study area was located in a dense coniferous forest characterized by an uneven-aged structure and intense topography. A random forest (RF) regression analysis was performed to develop TSB predictive models using Sentinel-1 and -2 images in an individual and combined manner. Consequently, three RF models were produced and evaluated for their predictive performance through the k-fold cross-validation (CV) method. The results showcased that the individual use of Sentinel-1 contributed to the production of the most accurate plot-level TSB estimates (i.e., coefficient of determination-R2 = 0.74, relative mean square error (RMSE) = 1.76 Mg/1000 m2, mean absolute error (MAE) = 1.48 Mg/1000 m2), compared to the use of Sentinel-2 data individually and the Sentinel-1 and -2 combination. In fact, the synergistic use of optical and SAR data led to the generation of an RF model that only marginally underperformed the SAR model (R2 = 0.73 and R2 = 0.72, respectively).

Combination of Sentinel-1 and Sentinel-2 Data for Tree Species Classification in a Central European Biosphere Reserve

Microwave and optical imaging methods react differently to different land surface parameters and, thus, provide highly complementary information. However, the contribution of individual features from these two domains of the electromagnetic spectrum for tree species classification is still unclear. For large-scale forest assessments, it is moreover important to better understand the domain-specific limitations of the two sensor families, such as the impact of cloudiness and low signal-to-noise-ratio, respectively. In this study, seven deciduous and five coniferous tree species of the Austrian Biosphere Reserve Wienerwald (105,000 ha) were classified using Breiman’s random forest classifier, labeled with help of forest enterprise data. In nine test cases, variations of Sentinel-1 and Sentinel-2 imagery were passed to the classifier to evaluate their respective contributions. By solely using a high number of Sentinel-2 scenes well spread over the growing season, an overall accuracy of 83.2% was achieved. With ample Sentinel-2 scenes available, the additional use of Sentinel-1 data improved the results by 0.5 percentage points. This changed when only a single Sentinel-2 scene was supposedly available. In this case, the full set of Sentinel-1-derived features increased the overall accuracy on average by 4.7 percentage points. The same level of accuracy could be obtained using three Sentinel-2 scenes spread over the vegetation period. On the other hand, the sole use of Sentinel-1 including phenological indicators and additional features derived from the time series did not yield satisfactory overall classification accuracies (55.7%), as only coniferous species were well separated.

Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensing. In this study, Sentinel-2 data were used for estimating the AGB in pure stands of Carpinus betulus (L., common hornbeam) located in the Hyrcanian forests, northern Iran. For this purpose, the diameter at breast height (DBH) of all trees thicker than 7.5 cm was measured in 55 square plots (45 × 45 m). In situ AGB was estimated using a local volume table and the specific density of wood. To estimate the AGB from remotely sensed data, parametric and nonparametric methods, including Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF), were applied to a single image of the Sentinel-2, having as a reference the estimations produced by in situ measurements and their corresponding spectral values of the original spectral (B2, B3, B4, B5, B6, B7, B8, B8a, B11, and B12) and derived synthetic (IPVI, IRECI, GEMI, GNDVI, NDVI, DVI, PSSRA, and RVI) bands. Band 6 located in the red-edge region (0.740 nm) showed the highest correlation with AGB (r = −0.723). A comparison of the machine learning methods indicated that the ANN algorithm returned the best ABG-estimating performance (%RMSE = 19.9). This study demonstrates that simple vegetation indices extracted from Sentinel-2 multispectral imagery can provide good results in the AGB estimation of C. betulus trees of the Hyrcanian forests. The approach used in this study may be extended to similar areas located in temperate forests.

MAPPING COASTAL AND WETLAND VEGETATION COMMUNITIES USING MULTI-TEMPORAL SENTINEL-2 DATA

Abstract. Operational monitoring of complex vegetation communities, such as the ones growing in coastal and wetland areas, can be effectively supported by satellite remote sensing, providing quantitative spatialized information on vegetation parameters, as well as on their temporal evolution. With this work, we explored and evaluated the potential of Sentinel-2 data for assessing the status and evolution of coastal vegetation as the primary indicator of ecosystem conditions, by mapping the different plant communities of Venice lagoon (Northeast Italy) via a rule-based classification approach exploiting synoptic seasonal features of spectral indices and multispectral reflectance. The results demonstrated that coastal and wetland vegetation community type maps derived for two different years scored a good overall accuracy around 80%, with some misclassification in the coastal areas and overestimation of salt marsh communities coverage, and that virtual collaborative environments can facilitate the use of Sentinel-2 data and products to multidisciplinary users.

Mapping Perennial Crops in Africa: A Case Study of Oil Palm in Ghana

Forests in Sub-Saharan Africa are experiencing some of the highest rates of deforestation and degradation in the world, with most natural forest species being replaced by cropland and plantation monoculture. In this work, a method was developed that combined the Synthetic Aperture Radar (Sentinel-1) and optical satellite imagery (Sentinel-2) data to accurately map natural forest and perennial crops (oil palm) in Ghana. This was done using all three variables including spatial, spectral, and temporal variables to assess the most important variables in characterizing oil palm and natural forest, as well as the added value of sentinel-1 SAR data in a sentinel-2 optical-based classification. In this workflow, the Gray level co-occurrence matrix (GLCM) was calculated as representing textural/spatial variables, a yearly median composite to represent the spectral variables, and raining and dry season composites of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) to represent the temporal variables for the Sentinel-2 data. In terms of the SAR data, rainy and dry season composites of NDVI and NDMI were calculated. With all these variables together, a characterization of the study area was conducted based on reference data of the land use land cover classes including oil palm, natural forests, and croplands (others) using Random Forest classifier. The variable importance of the Random Forest model was investigated to identify the top 10 most important variables. Results from this study showed that spectral variables followed by spatial variables are the most important and need to be considered when characterizing oil palm and natural forest, which is consistent with some pieces of literature. The use of sentinel-2 data achieved an acceptable classification accuracy (75%); whereas, sentinel-1 SAR further increased the accuracy (up to 85%) as compared to sentinel-2 only.

To What Extent Can a Sediment Yield Model Be Trusted? A Case Study from the Passaúna Catchment, Brazil

Soil degradation and reservoir siltation are two of the major actual environmental, scientific, and engineering challenges. With the actual trend of world population increase, further pressure is expected on both water and soil systems around the world. Soil degradation and reservoir siltation are, however, strongly interlinked with the erosion processes that take place in the hydrological catchments, as both are consequences of these processes. Due to the spatial scale and duration of erosion events, the installation and operation of monitoring systems are rather cost- and time-consuming. Modeling is a feasible alternative for assessing the soil loss adequately. In this study, the possibility of adopting reservoir sediment stock as a validation measure for a monthly time-step sediment input model was investigated. For the assessment of sediment stock in the reservoir, the commercial free-fall penetrometer GraviProbe (GP) was used, while the calculation of sediment yield was calculated by combining a revised universal soil loss equation (RUSLE)-based model with a sediment delivery ratio model based on the connectivity approach. For the RUSLE factors, a combination of remote sensing, literature review, and conventional sampling was used. For calculation of the C Factor, satellite imagery from the Sentinel-2 platform was used. The C Factor was derived from an empirical approach by combining the normalized difference vegetation index (NDVI), the degree of soil sealing, and land-use/land-cover data. The key research objective of this study was to examine to what extent a reservoir can be used to validate a long-term erosion model, and to find out the limiting factors in this regard. Another focus was to assess the potential improvements in erosion modeling from the use of Sentinel-2 data. The use of such data showed good potential to improve the overall spatial and temporal performance of the model and also dictated further opportunities for using such types of model as reliable decision support systems for sustainable catchment management and reservoir protection measures.

Evaluating the Potentiality of Sentinel-2 for Change Detection Analysis Associated to LULUCF in Wallonia, Belgium

Land Use/Cover changes are crucial for the use of sustainable resources and the delivery of ecosystem services. They play an important contribution in the climate change mitigation due to their ability to emit and remove greenhouse gas from the atmosphere. These emissions/removals are subject to an inventory which must be reported annually under the United Nations Framework Convention on Climate Change. This study investigates the use of Sentinel-2 data for analysing lands conversion associated to Land Use, Land Use Change and Forestry sector in the Wallonia region (southern Belgium). This region is characterized by one of the lowest conversion rates across European countries, which constitutes a particular challenge in identifying land changes. The proposed research tests the most commonly used change detection techniques on a bi-temporal and multi-temporal set of mosaics of Sentinel-2 data from the years 2016 and 2018. Our results reveal that land conversion is a very rare phenomenon in Wallonia. All the change detection techniques tested have been found to substantially overestimate the changes. In spite of this moderate results our study has demonstrated the potential of Sentinel-2 regarding land conversion. However, in this specific context of very low magnitude of land conversion in Wallonia, change detection techniques appear to be not sufficient to exceed the signal to noise ratio.

POTENTIALITIES OF SENTINEL-1 FOR MAPPING AND MONITORING GEOLOGICAL AND CRYOSPHERIC PROCESSES IN THE PATAGONIA REGION (CHILE)

Abstract. This work shows two examples on the use of Sentinel-1 data for monitoring different natural processes, like active geohazards or glacier dynamics in the Patagonia region. Sentinel-1 is a two-satellite constellation, launched by the European Space Agency (ESA), that provides SAR imagery with interferometric capabilities. It is in operation since 2014 and has supposed a significant improvement in the exploitation of these type of data for applications like natural hazards mapping and monitoring. The acquisition policy, that guarantees an acquisition each few days (12 days in Patagonia region) for both ascending and descending trajectories, and the data distribution policy, that allows free access to the images without legal constrains, are the main reasons for this improvement. These two aspects allowed not only to assure the data in the past but also to perform monitoring plans at medium-long term. In this work we show the potentialities of the use of these data in the Patagonia region through the application of two different techniques in two different application test sites: urban areas and glaciers.

Use of Sentinel-1 Data in Flood Mapping in the Buna River Area

Floods are one of the disasters that cause many human lives and property. In Albania, most floods are associated with periods of heavy rainfall. In recent years, Synthetic Aperture Radar (SAR) sensors, which provide reliable data in all weather conditions and day and night, have been favored because they eliminate the limitations of optical images.
 In this study, a flood occurred in the Buna River region in March 2018, was mapped using SAR Sentinel-1 data. The aim of this study is to investigate the potential of flood mapping using SAR images using different methodologies. Sentinel-1A / B SAR images of the study area were obtained from the European Space Agency (ESA).
 Preprocessing steps, which include trajectory correction, calibration, speckle filtering, and terrain correction, have been applied to the images. RGB composition and the calibrated threshold technique have been applied to SAR images to detect flooded areas and the results are discussed here.

Exploring the Use of Sentinel-2 Data to Monitor Heterogeneous Effects of Contextual Drought and Heatwaves on Mediterranean Forests

The use of satellite data to detect forest areas impacted by extreme events, such as droughts, heatwaves, or fires is largely documented, however, the use of these data to identify the heterogeneity of the forests’ response to determine fine scale spatially irregular damage is less explored. This paper evaluates the health status of forests in southern Italy affected by adverse climate conditions during the hot and dry summer of 2017, using Sentinel-2 images (10m) and in situ data. Our analysis shows that the post-event—NDVI (Normalized Difference Vegetation Index) decrease, observed in five experimental sites, well accounts for the heterogeneity of the local response to the climate event evaluated in situ through the Mannerucci and the Raunkiaer methods. As a result, Sentinel-2 data can be effectively integrated with biological information from field surveys to introduce continuity in the estimation of climate change impacts even in very heterogeneous areas whose details could not be captured by lower resolution observations. This integration appears to be a successful strategy in the study of the relationships between the climate and forests from a dynamical perspective.

Sentinel-2 Data for Land Cover/Use Mapping: A Review

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

Assessment of the Sentinel-1 based ground motion data feasibility for large scale landslide monitoring

In this paper, a systematic procedure to assess the feasibility of Advanced Differential Interferometric SAR (A-DInSAR) technique for landslide monitoring using SAR images acquired by Sentinel-1 sensors is presented. The methodology is named “Assessment of the advanced differentiaL interferometric synthetic aperture radar technique Feasibility for large scale lAndslide monitoring – ALFA” and it is structured in two main phases, which includes pre-processing and post-processing elaborations. The methodology was developed and tested in the Alpine sector of the Piedmont region in Italy, which represents a landslide prone area. In particular, ALFA represents a methodology based on previous algorithms available in the literature to assess the a-prior feasibility assessment and post-processing analysis of A-DInSAR data for landslide, which introduces three novel aspects such as (1) a systematic scheme suitable within regional practices; (2) the use of Sentinel-1 data and the development of (3) an index to take into account of the kind of distribution of the measuring points along the landslide. The approach was applied over an area extended about 5300 km2 affected by 5703 landslides mapped in the database of the Piedmont Region (Landslides information system in Piedmont—SIFRAP). Sentinel-1 images covering the period 2014–2018 were analysed. The results show the potential of the Sentinel-1 data for large-scale landslide monitoring. The developed methodology presents reliable tools that could be useful as feasibility for the use of Sentinel-1 data for landslide monitoring at regional and national scale.

Detecting the burned area in southern Kalimantan by using the sentinel-1 polarimetric SAR and landsat-8 OLI optic

Synthetic Aperture Radar (SAR) imageries data have turned out to be one of the essential sources for forest fire mapping, especially in tropical region since the smoke haze obstruct data acquisition by optical sensor. Despite these limitations, until now, the use of optical sensors still dominates in monitoring forest and land fires in the world. The Sentinel-1 satellites presently offer unreservedly accessible and freely available, world coverage and fast recurrent time (6-12 days), gives Sentinel-1 images the possibility to be broadly utilized for observing the Earth’s surface, including forest and land fire phenomenon. However, the use of sentinel-1 data for monitoring and mapping forest and land fires in the tropics of Indonesia, is still limited and has not been widely implemented. This study investigated the use of Sentinel-1, synergy with optical Landsat-8 OLI (Operational Land Imager) data, to identify the burned area, in the tropical region of Indonesia, during 2019 fire season. A pair of Landsat-8 OLI, collected before and after fires, has been used to delineate the boundaries of sample location of burned area. Then, the difference of reflectance and Normalized Burn Ratio were analyzed. A series of Sentinel-1 images, collected before and during/after fires, has been utilized to produce the backscatter values among images. Fire incident causes landcover changes from vegetated land to bareland. This changes can affect the reflectance detected from Landsat-8 OLI. This changes also influence the backscatter recognized from SAR sensor. Then analysis of SAR backscatter on the location of the burned area detected from Landsat-8 was performed. The synergy between SAR polarimetric and optical reflected data, creates a valuable tool for identifying and interpreting burned area following a fire event.

Investigating the potential use of Sentinel-1 data for monitoring wetland water level changes in China's Momoge National Nature Reserve.

BackgroundInterferometric Synthetic Aperture Radar (InSAR) has become a promising technique for monitoring wetland water levels. However, its capability in monitoring wetland water level changes with Sentine-1 data has not yet been thoroughly investigated.MethodsIn this study, we produced a multitemporal Sentinel-1 C-band VV-polarized SAR backscatter images and generated a total of 28 interferometric coherence maps for marsh wetlands of China’s Momoge National Nature Reserve to investigate the interferometric coherence level of Sentinel-1 C-VV data as a function of perpendicular and temporal baseline, water depth, and SAR backscattering intensity. We also selected six interferogram pairs acquired within 24 days for quantitative analysis of the accuracy of water level changes monitored by Sentinel-1 InSAR. The accuracy of water level changes determined through the Sentinel-1 InSAR technique was calibrated by the values of six field water level loggers.ResultsOur study showed that (1) the coherence was mainly dependent on the temporal baseline and was little affected by the perpendicular baseline for Sentinel-1 C-VV data in marsh wetlands; (2) in the early stage of a growing season, a clear negative correlation was found between Sentinel-1 coherence and water depth; (3) there was an almost linear negative correlation between Sentinel-1 C-VV coherence and backscatter for the marsh wetlands; (4) once the coherence exceeds a threshold of 0.3, the stage during the growing season, rather than the coherence, appeared to be the primary factor determining the quality of the interferogram for the marsh wetlands, even though the quality of the interferogram largely depends on the coherence; (5) the results of water level changes from InSAR processing show no agreement with in-situ measurements during most growth stages. Based on the findings, we can conclude that although the interferometric coherence of the Sentinel-1 C-VV data is high enough, the data is generally unsuitable for monitoring water level changes in marsh wetlands of China’s Momoge National Nature Reserve.