European Space Agency Sentinel-2 Research Articles

3U CubeSat-Based Hyperspectral Remote Sensing by Offner Imaging Hyperspectrometer with Radially-Fastened Primary Elements.

This paper presents findings from a spaceborne Earth observation experiment utilizing a novel, ultra-compact hyperspectral imaging camera aboard a 3U CubeSat. Leveraging the Offner optical scheme, the camera's hyperspectrometer captures hyperspectral images of terrestrial regions with a 200 m spatial resolution and 12 nanometer spectral resolution across a 400 to 1000 nanometer wavelength range, covering 150 channels in the visible and near-infrared spectrums. The hyperspectrometer is specifically designed for deployment on a 3U CubeSat nanosatellite platform, featuring a robust all-metal cylindrical body of the hyperspectrometer, and a coaxial arrangement of the optical elements ensures optimal compactness and vibration stability. The performance of the imaging hyperspectrometer was rigorously evaluated through numerical simulations prior to construction. Analysis of hyperspectral data acquired over a year-long orbital operation demonstrates the 3U CubeSat's ability to produce various vegetation indices, including the normalized difference vegetation index (NDVI). A comparative study with the European Space Agency's Sentinel-2 L2A data shows a strong agreement at critical points, confirming the 3U CubeSat's suitability for hyperspectral imaging in the visible and near-infrared spectrums. Notably, the ISOI 3U CubeSat can generate unique index images beyond the reach of Sentinel-2 L2A, underscoring its potential for advancing remote sensing applications.

Four unreported emperor penguin colonies discovered by satellite

Predictions of the future emperor penguins population, linked to anthropogenic climate change, are stark. Current models suggest that if CO2 emissions continue to rise at present rates, almost all colonies will be quasi-extinct by the end of the century (Jenouvrier et al.2021). The monitoring of populations is crucial to tracking these changes and, if possible, implementing conservation measures. Recent work using satellite imagery to discover, track and monitor emperor penguin populations has proved to be a key technology in understanding the locations, numbers and trends of the species (Barbraud & Weimerskirch 2001, Trathan et al.2020, Jenouvrier et al.2021). It also enables the discovery of unrecorded breeding sites (Fretwell et al.2009), although there are inherent difficulties in determining what constitutes a new or undiscovered breeding colony (see Supplemental Material S1). In 2019, eight previously unreported emperor penguin breeding sites were found using the European Space Agency's Sentinel-2 satellite, a medium-resolution satellite with a spatial resolution of 10 m per pixel (Fretwell & Trathan 2021), bringing the number of known extant breeding locations to 61. Here, I report on the discovery of a further four breeding sites using Sentinel-2 and Maxar WorldView-2 imagery.

Automatic 10 m Forest Cover Mapping in 2020 at China’s Han River Basin by Fusing ESA Sentinel-1/Sentinel-2 Land Cover and Sentinel-2 near Real-Time Forest Cover Possibility

Given the increasingly fragmented forest landscapes, it is necessary to map forest cover with fine spatial resolution in a large area. The European Space Agency (ESA) released the 10 m global land cover map in 2020 based on Sentinel-1 and Sentinel-2 images, and Dynamic World provides near real-time possibilities of many land cover classes based on Sentinel-2 images, but they are not designed particularly for forest cover. In this research, we aimed to develop a method to automatically estimate an accurate 10 m forest cover map in 2020 by fusing the ESA forest cover map and Dynamic World near real-time forest cover possibilities. The proposed method includes three main steps: (1) generating stable forest samples, (2) determining the threshold T and (3) producing the fused forest cover map. China’s Han River Basin, dominated by complex subtropical forests, was used as the study site to validate the performance of the proposed method. The results show that the proposed method could produce a forest cover map with the best overall accuracy of 98.02% ± 1.20% and more accurate spatial details compared to using only one of the two data sources. The proposed method is thus superior in mapping forest cover in complex forest landscapes.

Determining the Deformations of the Earth's Surface after the Earthquakes in Turkey-Syria of 06 February 2023 - Initial Results

Two devastating earthquakes of magnitude MW 7.8 and MW 7.5 hit Turkey on February 6, 2023. The earthquakes occurred in the East Anatolian Fault Zone, westwards of Gaziantep, Turkey. The quake was followed by intensive aftershock activity. This study presents initial results from interferometric processing of SAR data from the European Space Agency Sentinel-1 mission revealing the affected area and the magnitude of the deformations that occurred after the earthquakes. The produced maps are combination of data from several sources. Data of the epicenters of the events are from EMSC1, and the active faults are from EDAF2, both considered to be the driving forces of these ground movements. SAR data were used to produce information showing the actual surface displacements. It needs to be highlighted that the said information reflects not the consequences of the single seismic event but the effect of all earthquakes that took place in the investigated region between the dates of registration by the SAR instrument. In this paper, the presented results are only part of the larger set of interferograms and maps of the derived from them vertical displacements. 

InSAR-based mapping of ground deformation caused by industrial waste disposals: the case study of the Huelva phosphogypsum stack, SW Spain

Close to the city of Huelva, SW Spain, and near the Atlantic Ocean, there is a phosphogypsum (PG) stack that accumulates 100 Mt of wastes and extends over 1000 ha. The stack lies directly over estuarine unconsolidated sediments with no protective layer in between. Here, we evaluate for the first time the structural stability of the PG stack, monitoring the deformation suffered by the salt-marsh basement. Through the web-based Geohazard Exploitation Platform (GEP) of the European Space Agency (ESA), a specific differential SAR interferometry (DInSAR) algorithm known as Parallel Small Baseline Subset (P-SBAS) has been used to process 279 ESA Sentinel-1 images acquired between October 2016 and June 2021. Resulting displacement maps and time-series curves reveal vertical displacements of up to 16 cm/year. This vertical motion has been associated to subsidence. In parallel with subsidence, horizontal movements > 2.5 cm/year have been also accounted and linked to talus destabilization. The analysis also demonstrates that the Huelva PG stack is vulnerable to adverse weather condition. The present study demonstrates that the InSAR-based methods are effective tools for monitoring the stability and ground motion of large waste stockpiles.

Monitoring of Coastal Aquaculture Sites in the Philippines through Automated Time Series Analysis of Sentinel-1 SAR Images

With the unprecedented expansion of aquaculture around the world, there is a critical need to monitor its progress. In Palawan, Philippines, coastal aquaculture is gaining momentum towards increasing fish production, a pressure that presents a challenge to the sustainability of these areas. In this paper, we explore the application of Earth observation methods to map coastal aquaculture development in Palawan and evaluate the extent of its change. The European Space Agency Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral instrument sensor data were applied in fully automatic mode to build maps of Palawan coastal aquaculture. The maps were validated using Google Earth high-resolution optical images and in situ observations in Malampaya Sound, and demonstrated a successful detection rate of 72%, while the false alarm rate was less than 7.5%. Objects only 5 m across, four times smaller than the spatial resolution of the Sentinel-1 sensors, were successfully detected using the developed methodology, thus exceeding the capabilities of other published methods that are limited to detecting large groups of aquaculture structures. The maps revealed aquaculture structures in high quantities in Malampaya Sound, Taytay Bay, and other locations in the coastal waters of Palawan, Philippines. A significant change of aquaculture spatial distribution was identified by comparing aquaculture maps generated with an interval of three years. This new automated methodology was validated as robust for mapping aquaculture objects in Palawan, Philippines, and can be applied to aquaculture studies in other regions worldwide.

Exceptional landscape-wide cyanobacteria bloom in Okavango Delta, Botswana in 2020 coincided with a mass elephant die-off event

In 2020, nearly 400 elephants died within the Okavango Delta region in Botswana, creating the worst-ever elephant mass die-off event in history. This catastrophic event was widely blamed on toxic cyanobacterial blooms after lab results showed the presence of toxin-forming cyanobacteria in inland waters of the Delta. However, it did not explain why we saw this mass die-off of elephants in 2020 and not in previous years. We conducted a landscape-wide time-series analysis using freely available European Space Agency's Sentinel-2 and NASA's Landsat-8 satellite data. We used existing bio-optical models, Normalized Difference Chlorophyll Index and Green Line Height, as proxies for chlorophyll-a and phycocyanin (cyanobacteria) concentrations. We found that 2020 was an exceptional year for cyanobacteria blooms in the Okavango Delta region compared to the past three years (2017–2019). Bloom phenology indicated that the cyanobacteria blooms initiated in September-October 2019, experienced an exponential growth reaching peak in January-February 2020, and eventually senescing in June 2020. This being a notoriously data-scarce region of the world, we did not have any means to perform site-specific validation of the models. Although magnitude and timeline of the blooms coincided with the timeline of elephant death reports, our study do not confirm it to be the trigger. For the first time, we show the widespread nature of these blooms across the landscape, which may have increased the toxin exposure for elephants. We theorize that 2020 might have been the first year for such a mass die-off event, but it will certainly not be the last because warming trends under changing climate are creating increasingly suitable conditions for these blooms to be pervasive and ubiquitous. Through this preliminary study, we demonstrate the critical need for frequent and comprehensive monitoring of toxic cyanobacterial blooms in the Delta to avoid another such event in the future.

Flood damage assessment with Sentinel-1 and Sentinel-2 data after Sardoba dam break with GLCM features and Random Forest method

Accurate mapping and monitoring of flooded areas are immensely required for disaster management purposes, such as for damage assessment and mitigation. In this study, the flood damage mapping performances of two satellite Earth Observation sensors, i.e., European Space Agency's Sentinel-1 (S1) synthetic aperture radar (SAR) and Sentinel-2 (S2) multispectral optical instruments, were evaluated using the Random Forest (RF) supervised classification method and various feature types. The study area was Sardoba Reservoir (Uzbekistan) and its surroundings, in which a disastrous dam failure occurred on May 1, 2020. After the failure of a part of the earthfill dam, a large region with settlements and agricultural areas in Uzbekistan and Kazakhstan was flooded. S1 and S2 cloudless data with a short temporal interval acquired soon after the event were available for the area. Four different data availability scenarios, such as (i) only S1 pre- and post-flood data; (ii) only S2 pre- and post-flood data; (iii) S1 pre- and post-flood and S2 pre-flood data; and (iv) S1 and S2 pre- and post-flood data were evaluated in terms of classification accuracy. In addition to the polarization information of S1 and the intensity values of S2 bands, feature maps produced from these datasets, such as vegetation and water indices, textural information obtained from gray level co-occurrence matrix (GLCM), and the principal component analysis (PCA) bands were employed in the RF method. The results show that the fusion of S1 and S2 data exhibit very high classification accuracy for the flooded areas and can separate the inundated vegetation as well. The use of S2 pre-event data together with the S1 pre- and post-event data is recommended for obtaining high accuracy even when post-event optical data is not available.

Application of SAR Data for Tropical Cyclone Intensity Parameters Retrieval and Symmetric Wind Field Model Development

The spaceborne synthetic aperture radar (SAR) is an effective tool to observe tropical cyclone (TC) wind fields at very high spatial resolutions. TC wind speeds can be retrieved from cross-polarization signals without wind direction inputs. This paper proposed methodologies to retrieve TC intensity parameters; for example, surface maximum wind speed, TC fullness (TCF) and central surface pressure from the European Space Agency Sentinel-1 Extra Wide swath mode cross-polarization data. First, the MS1A geophysical model function was modified from 6 to 69 m/s, based on three TC samples’ SAR images and the collocated National Oceanic and Atmospheric Administration stepped frequency microwave radiometer wind speed measurements. Second, we retrieved the wind fields and maximum wind speeds of 42 TC samples up to category 5 acquired in the last five years, using the modified MS1A model. Third, the TCF values and central surface pressures were calculated from the 1-km wind retrievals, according to the radial curve fitting of wind speeds and two hurricane wind-pressure models. Three intensity parameters were found to be dependent upon each other. Compared with the best-track data, the averaged bias, correlation coefficient (Cor) and root mean-square error (RMSE) of the SAR-retrieved maximum wind speeds were –3.91 m/s, 0.88 and 7.99 m/s respectively, showing a better result than the retrievals before modification. For central pressure, the averaged bias, Cor and RMSE were 1.17 mb, 0.77 and 21.29 mb and respectively, indicating the accuracy of the proposed methodology for pressure retrieval. Finally, a new symmetric TC wind field model was developed with the fitting function of the TCF values and maximum wind speeds, radial wind curve and the Rankine Vortex model. By this model, TC wind field can be simulated just using the maximum wind speed and the radius of maximum wind speed. Compared with wind retrievals, averaged absolute bias and averaged RMSE of all samples’ wind fields simulated by the new model were smaller than those of the Rankine Vortex model.

Machine learning for satellite-based sea-state prediction in an offshore windfarm

Accurate wave forecasts are essential for the safe and efficient maritime operations and, in particular, the maintenance of offshore wind farms. Here, machine learning and remote monitoring from satellites are integrated to provide uniquely detailed predictions of significant wave height (SWH). C-Band Synthetic Aperture Radar images from European Space Agency Sentinel-1 satellites were combined with wave-buoy data from around the UK, using the CEFAS Wavenet. A total of 240 images in wide swarth mode were collected, that represent significant wave height ranging from 0 to 4.7 m. Image properties related to sea surface roughness in dual-polarization mode, together with the wave buoy data, trained an ensemble of artificial neural networks. The trained networks were shown to provide an effective method for the estimation of the SWH, having an RMSE = 0.23 m for SWH<3 m, which is the region of interest for offshore wind energy applications. The methodology enables information on the spatial distribution of wave height in very high resolution to be obtained. Sea-state resolved from the satellite data, using the artificial neural network shows that windfarm infrastructure directly influences wave propagation. The overall variance of significant wave height throughout a wind farm was calculated, providing information on regions of interest with considerably different wave heights as compared to the nearest wave buoy. The new model will help towards improved downscaling of general sea state forecasts, locating hotspots of different wave height properties and correct prioritization of maintenance jobs to perform in wind turbines.

Extreme Wind Speeds Retrieval Using Sentinel-1 IW Mode SAR Data

With the improvement in microwave radar technology, spaceborne synthetic aperture radar (SAR) is widely used to observe the tropical cyclone (TC) wind field. Based on European Space Agency Sentinel-1 Interferometric Wide swath (IW) mode imagery, this paper evaluates the correlation between vertical transmitting–horizontal receiving (VH) polarization signals and extreme ocean surface wind speeds (&gt;40 m/s) under strong TC conditions. A geophysical model function (GMF) Sentinel-1 IW mode wind retrieval model after noise removal (S1IW.NR) was proposed, according to the SAR images of nine TCs and collocated stepped frequency microwave radiometer (SFMR) and soil moisture active passive (SMAP) radiometer wind speed measurements. Through curve fitting and regression correction, the new GMF exploits the relationships between VH-polarization normalized radar cross section, incident angle, and wind speed in each sub-swath and covers wind speeds up to 74 m/s. Based on collocated SAR and SFMR measurements of four TCs, the new GMF was validated in the wind speed range from 2 to 53 m/s. Results show that the correlation coefficient, bias, and root mean squared error were 0.89, −0.89 m/s, and 4.13 m/s, respectively, indicating that extreme winds can be retrieved accurately by the new model. In addition, we investigated the relationship between the S1IW.NR wind retrieval bias and the SFMR-measured rain rate. The S1IW.NR model tended to overestimate wind speeds under high rain rates.

Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia

Earth observation (EO) satellites facilitate hazard monitoring and mapping over large-scale and remote areas. Despite Synthetic Aperture Radar (SAR) satellites being well-documented as a hazard monitoring tool, the uptake of these data is geographically variable, with the Australian continent being one example where the use of SAR data is limited. Consequently, less is known about how these data apply in the Australian context, how they could aid national hazard monitoring and assessment, and what new insights could be gleaned for the benefit of the international disaster risk reduction community. The European Space Agency Sentinel-1 satellite mission now provides the first spatially and temporally complete global SAR dataset and the first opportunity to use these data to systematically assess hazards in new locations. Using the example of Australia, where floods and uncontrolled bushfires, earthquakes, resource extraction (groundwater, mining, hydrocarbons) and geomorphological changes each pose potential risks to communities, we review past usage of EO for hazard monitoring and present a suite of new case studies that demonstrate the potential added benefits of SAR. The outcomes provide a baseline understanding of the potential role of SAR in national hazard monitoring and assessment in an Australian context. Future opportunities to improve national hazard identification will arise from: new SAR sensing capabilities, which for Australia includes a first-ever civilian EO capability, NovaSAR-1; the integration of Sentinel-1 SAR with other EO datasets; and the provision of standardised SAR products via Analysis Ready Data and Open Data Cubes to support operational applications.

Baseline monitoring of volcanic regions with little recent activity: application of Sentinel-1 InSAR to Turkish volcanoes

Volcanoes have dormancy periods that may last decades to centuries meaning that eruptions at volcanoes with no historical records of eruptions are common. Baseline monitoring to detect the early stages of reawakening is therefore important even in regions with little recent volcanic activity. Satellite techniques, such as InSAR, are ideally suited for routinely surveying large and inaccessible regions, but the large datasets typically require expert interpretation. Here we focus on Turkey where there are 10 Holocene volcanic systems, but no eruptions since 1855 and consequently little ground-based monitoring. We analyse data from the first five years of the European Space Agency Sentinel-1 mission which collects data over Turkey every 6 days on both ascending and descending passes. The high relief edifices of Turkey’s volcanoes cause two challenges: 1) snow cover during the winter months causes a loss of coherence and 2) topographically-correlated atmospheric artefacts could be misinterpreted as deformation. We propose mitigation strategies for both. The raw time series at Hasan Dag volcano shows uplift of ~ 10 cm between September 2017 and July 2018, but atmospheric corrections based on global weather models demonstrate that this is an artefact and reduce the scatter in the data to < 1 cm. We develop two image classification schemes for dealing with the large datasets: one is an easy to follow flowchart designed for non-specialist monitoring staff, and the other is an automated flagging system using a deep learning approach. We apply the deep learning scheme to a dataset of ~ 5000 images over the 10 Turkish volcanoes and find 4 possible signals, all of which are false positives. We conclude that there has been no cm-scale volcano deformation in Turkey in 2015–2020, but further analysis would be required to rule out slower rates of deformation (< 1 cm/yr). This study has demonstrated that InSAR techniques can be used for baseline monitoring in regions with few historical eruptions or little reported deformation.

SENTINEL-2 ғарыштық суреттерін пайдаланып ауыл шаруашылығы алқаптарын бақылау

The article presents the issues of monitoring and analysis of agricultural land using satellite imagery data. Images from the European Space Agency's Sentinel-2 satellite were used as the main research material. This satellite provides optical data that is used for many areas, such as monitoring for floods, landslides, fires, agricultural crops, etc. The area of the Northern Economic Zone of Kazakhstan, the mouth of the Yessil River, was used as the object of the study. The vegetation index NDVI (Normalized Difference Vegetation Index) was used for monitoring, and the basic principles of its application were explained. The analysis of the obtained result is presented and the diagrams show changes in the vegetation background of the studied territory. Special recommendations were given for further improvement of research methods and its effectiveness was justified.

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 (&gt;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.

Brief communication: Hurricane Dorian: automated near-real-time mapping of the “unprecedented” flooding in the Bahamas using synthetic aperture radar

Abstract. In this communication, we present application of the automated near-real-time (NRT) system called RAdar-Produced Inundation Diary (RAPID) to European Space Agency Sentinel-1 synthetic aperture radar (SAR) images to produce flooding maps for Hurricane Dorian in the northern Bahamas. RAPID maps, released 2 d after the event, show that coastal flooding in the Bahamas reached areas located more than 10 km inland, covering more than 3000 km2 of continental area. RAPID flood estimates from subsequent SAR images show the recession of the flood across the islands and present high agreement scores when compared to Copernicus Emergency Management Service (Copernicus EMS) estimates.

Modeling water quality impacts from hurricanes and extreme weather events in urban coastal systems using Sentinel-2 spectral data.

Conventional water quality measurements are nearly impossible during and immediately after extreme storms due to dangerous conditions. In this study, remotely sensed reflectance is used to develop a regression equation that quantifies total suspended solids (TSS) in near real-time after Hurricane Harvey. The application focused specifically on sediment loading and deposition and its potential impacts on the Houston Ship Channel and Galveston Bay riverine-estuarine system. The European Space Agency's Sentinel-2 satellite captured images at critical points in the storm's progression, necessitating the development of a new algorithm for this relatively new satellite mission. Several linear regressions were analyzed with the goal of developing a simple one- or two-band equation, and the final model uses the red and near infrared bands (R2 = 0.74). Results show that record flows during Harvey delivered unprecedented suspended sediment loads to the Gulf of Mexico at concentrations above 125mg/L with a mean concentration of 43mg/L across the bay. The study findings demonstrated that it took up to 11days after the storm for sediment transport to abate.

Tracing the Pre-failure Two-dimensional Surface Displacements of Nanyu Landslide, Gansu Province with Radar Interferometry雷达干涉测量对甘肃南峪乡滑坡灾前二维形变追溯

The pre-failure evolution of landslides is of great value for the analysis of landslide triggering factors and post-disaster stability assessment. At present, optical images are commonly employed to analyze the pre-failure evolution, but it is well-known that their data availability could be highly limited due to the presence of clouds. With the advance in radar remote sensing and interferometric synthetic aperture radar(InSAR), it could provide a new technical approach for landslide pre-failure detection. In this paper, the 2018 Nanyu landslide in Gansu Province is utilized to demonstrate the capability of InSAR to trace its pre-failure surface displacements using European Space Agency's Sentinel-1 radar data with a temporal interval of 12 days in different tracks. The results show that the landslide began to deform in June of 2017, and the maximum cumulative deformation reached up to 77 mm in the 13 months before the occurrence of the landslide. The time series InSAR derive displacements and the rainfall data is consistent, suggesting that the rainfall should be one of the triggering factors for the landslide. The study demonstrates the potential of radar interferometry for landslide detection, which can provide insights on landslide triggering factors, landslide disaster prevention and mitigation, and even landslide monitoring and early warning work in the future.

Relationship Between Spatio-Temporal Leaf Area Index and Crop Coefficient When Monitoring Coffee Plots in Brazil Using the Sentine-2

Robust monitoring techniques for perennial crops have become increasingly possible due to technological advances in the area of Remote Sensing (RS), and the products are available through the European Space Agency (ESA) initiative. RS data provides valuable opportunities for detailed assessments of crop conditions at plot level using high spatial, spectral, and temporal resolution. This study addresses the monitoring of coffee at the plot level using RS, analyzing the relationship between the spatio-temporal variability of the Leaf Area Index (LAI) and the crop coefficient (Kc); the Kc being a biophysical variable that integrates the potential hydrological characteristics of an agroecosystem compared to the reference crop. Daily and one-year Kc were estimated using the relation of crop evapotranspiration and reference. ESA Sentinel-2 images were pre-analyzed and atmospherically corrected, and Top-of-the-Atmosphere (TOA) reflections converted to Top-of-the-Canopy (TOC) reflectance. The TOCs resampled at the 10m resolution, and with the angles corresponding to the directional information at the time of the acquisition, the LAI was estimated using the trained neural network available in the Sentinel Application Platform (SNAP). During 75% of the monitored days, Kc ranged between 1.2 and 1.3 and, the LAI analyzed showed high spatial and temporal variability at the plot level. Based on the relationship between the biophysical variables, the LAI variable can substitute the Kc and be used to monitor the water conditions at the production area as well as analyze spatial variability inside that area. Sentinel-2 products could be more useful in monitoring coffee in the farm production area.

Estimation of flow in various sizes of streams using the Sentinel-1 Synthetic Aperture Radar (SAR) data in Han River Basin, Korea

This study proposes a novel approach of estimating stream discharge using the European Space Agency Sentinel-1 satellite data. Fifteen hydrological stations with a mean discharge ranging between 2 m3 s−1 and 305 m3 s−1 in the Han River Basin in Korea were chosen as test sites. A series of Sentinel-1 Synthetic Aperture Radar (SAR) images and observed streamflow data were selected to develop and validate the methodology. The methodology relates the stream discharge to the water area that is extracted from the satellite image analysis. The satellite image analysis involved the following sequential steps: (1) A series of SAR images for a given test site is pre-processed for thermal noise, radiometric calibration, speckle filtering and terrain correction; (2) The histogram matching technique is applied to all images to unify the backscatter distribution; (3) The polygonal area including the stream is manually delineated along the reach and the image filter with the shape of the polygon is applied to all images so that only the water area of the stream is extracted; (4) A single backscatter threshold value is applied to all images to extract a series of water area; (5) the power-law relationship between the series of the water area and the corresponding stream discharge is established and the correlation coefficient of the relationship is calculated; (6) The process of (4) and (5) was repeated to identify the optimal backscatter threshold that maximizes the correlation coefficient of the relationship. A clear relationship was developed for the 13 stations except for the two at which flow is highly influenced by hydraulic structures such as dam. At the 13 stream locations, the R value of the power-law relationship varied between 0.68 and 0.98 with a mean value of 0.89. The relationship was influenced by the geometric properties of the stream such as the size and side slope.