Sentinel Application Platform Research Articles

Estimation of Soil Moisture from Multispectral Remote Sensing Data

Soil moisture is an important parameter in agricultural and environmental studies and affects the exchange of water and energy at the boundary between the earth’s surface and the atmosphere. Accurate estimation of spatial-temporal changes in soil moisture is critical for numerous studies in the agricultural sciences. Vegetation indices, based on combinations of digital data of reflected light in the visible and near-infrared range, are suitable for the detection of water stress in plants. Therefore, they are widely used to monitor and detect drought conditions, but their accuracy is dependent on different vegetation types. The main objective of this research was to develop a novel procedure for the estimation of soil moisture using multispectral remote measurements. This study has been done using a multispectral camera Survey 3W Red+Green+NIR, which records images in three spectral channels: green (550 nm), red (660 nm) and near-infrared (850 nm). Eleven soil samples with different water content were investigated. SNAP (Sentinel Application Platform) software was used to process the captured multispectral images. It allows an easy calculation of various vegetation indices. The suitability of the normalized difference vegetation index (NDVI) for the assessment of soil moisture was evaluated. The average NDVI values did not indicate a well-established trend in relation to the SWC. A new simple vegetation index NIR/Green was successfully used for the assessment of soil moisture. The new NIR/Green index gives consistent results in relation to the real soil water content and could be used for mapping of the soil moisture with multispectral cameras.

Estimation of Soil Moisture from Multispectral Remote Sensing Data

Soil moisture is an important parameter in agricultural and environmental studies and affects the exchange of water and energy at the boundary between the earth’s surface and the atmosphere. Accurate estimation of spatial-temporal changes in soil moisture is critical for numerous studies in the agricultural sciences. Vegetation indices, based on combinations of digital data of reflected light in the visible and near-infrared range, are suitable for the detection of water stress in plants. Therefore, they are widely used to monitor and detect drought conditions, but their accuracy is dependent on different vegetation types. The main objective of this research was to develop a novel procedure for the estimation of soil moisture using multispectral remote measurements. This study has been done using a multispectral camera Survey 3W Red+Green+NIR, which records images in three spectral channels: green (550 nm), red (660 nm) and near-infrared (850 nm). Eleven soil samples with different water content were investigated. SNAP (Sentinel Application Platform) software was used to process the captured multispectral images. It allows an easy calculation of various vegetation indices. The suitability of the normalized difference vegetation index (NDVI) for the assessment of soil moisture was evaluated. The average NDVI values did not indicate a well-established trend in relation to the SWC. A new simple vegetation index NIR/Green was successfully used for the assessment of soil moisture. The new NIR/Green index gives consistent results in relation to the real soil water content and could be used for mapping of the soil moisture with multispectral cameras.

Detection of pyroclastic material and deformation of the lava dome in Mt. Sinabung with multi-temporal analysis (2010-2020)

The persistent eruptions of Mt. Sinabung resulted in the lava dome’s deformation and blanketed the surrounding area with pyroclastic material. This research focused on determining the changes in Mt. Sinabung during the prolonged eruptions from 2010-2020. A total of 22 volcanic ash samples were collected following a 1x1 km grid interval spread over from the East to South slope of Mt. Sinabung. The studied area is the most affected by the volcanic eruption. Digital elevation models from the shuttle radar topographic mission and sentinel-1 were utilized to monitor deformations of the lava dome and the distribution of pyroclastic material on Mt. Sinabung. Sentinel Application Platform and Google Earth Engine were used as the main tools in multi-temporal digital elevation model (DEM) data processing. The eruption of Mt. Sinabung from 2010 to 2020 changed the height from 2,460 m to 2,404.3 m and created a new crater (5.35 ha). The lava dome volume from 2010 to 2020 is about 2,308,041 m3 and has collapsed to produce pyroclastic material which deposits to the surrounding area of Mt. Sinabung during the eruption. The distribution of pyroclastic material increased from 2010 to 2019, covered up an area of 103.27 ha (2010), 846.48 ha (2013), 1,029.74 ha (2016), 1,235.97 ha (2017) and 1,463.62 ha (2019). The thickness of the pyroclastic material deposits at Mt. Sinabung until 2020 varied from 13,24 cm to 219 cm. The findings from this study can be used as a reference for observing topographic changes due to volcanic activities and drawing a mitigation and contingency plan for a volcanic disaster program in the active volcanic region of Indonesia.

Estimation of flood inundation in river basins of Uttar Pradesh using Sentinel 1A-SAR data on Sentinel Application Platform (SNAP)

Estimation of flood inundation in river basins of Uttar Pradesh using Sentinel 1A-SAR data on Sentinel Application Platform (SNAP)

Modelling and Mapping Oil Pollution across the Inland and Coastal Waters: A Case of Selected Segment of Gulf of Guinea

Artisanal fisheries in the Niger Delta and Nigeria’s coastal waters support the livelihood of its inhabitants, thus management of water pollution has public health significance for the regions and beyond. Oil Spills and contaminated runoff from the hinterland always find their way to the coast via the extensive network of rivers and creeks across the delta. This is a major cause of marine pollution in the region. With this understanding there is a need to map and model the dynamics of oil pollution across the coastal waters of the Niger Delta, thereby providing evidence and data support for their management. This study explored the potential of mapping oil slicks along the inland and coastal water of the Niger Delta using the SENTINEL-1 Synthetic Aperture Radar (SAR). The Interferometric Wide Swath Mode imagery Ground Range Detected Imagery for the 2nd of January 2019 was acquired for the study area, processed using Sentinel Application Platform (SNAP) and the oil spill detection algorithm was applied on the Amplitude Vertical-Vertical Band. After preprocessing, the non-polluted areas were found to have high backscatter (amplitude >18.0). For the impacted areas, lower values were recorded - amplitude decibel value of between 15.9 and 18.0. A total of 329 polygons of oil slick were identified, covering an area of 46.34 km2. The impacted surface area ranges between 0.000064 – 15 km2. The farthest impacted surface can be found around 18.25km from the Bonny coastline. Results showed that a significant proportion of the oil slicks are concentrated along the upper reaches of the Bonny River spanning Okrika and Ogu/Bolo LGA. Towards the nearshore, the oil slicks were disaggregated into several units. Contiguous units were found closer to the banks and coastline. Out of the 329 polygons, 14 were identified along the New Calabar River, emptying into the Bonny Estuary. The impacted surfaces along the New Calabar River were smaller in size compared to those found along the Bonny River. The results identified the pattern and distribution of oil slicks across a segment of the Gulf of Guinea dominated by oil and gas industry activities. This study highlighted the potential of remotely sensed data to support coastal pollution management efforts. It further highlighted the potential for spatiotemporal monitoring of oil slicks along coastal and inland waters. Challenges for deploying such a platform include the high computing power required and the lack of in-situ monitoring data for validation.

Bathymetry mapping with empirical bathymetry method based on sentinel application platform case study: Bawean Island, Central Java

Bathymetry information is an important requirement for the marine and fisheries sector in Indonesia. However, the acquisition of information is done in the traditional way, namely surveys using single/multibeam echosounder devices carried by ships. This method was inefficient, especially to cover a wider area. The development of remote sensing technology can be a solution and has become a source of data for satellite-derived bathymetry (SDB). One of the areas of concern in this research is the eastern waters of Bawean Island located in the Java Sea. This research uses the SNAP method to determine the results and accuracy of bathymetry mapping in shallow sea waters east of Bawean Island. This method is generated from regression modeling between spectral values of satellite image data and in-situ depth data. The satellite data used is the Sentinel-2A image recorded on December 9, 2022. The results showed that the Sentinel-2A satellite can produce depth information up to 27.9 meters. The coefficient of determination, R2, is 86%, and the confusion matrix accuracy value is 75.21%.

Modeling strategies and influencing factors in retrieving canopy equivalent water thickness of mangrove forest with Sentinel-2 image

Canopy equivalent water thickness (EWT) is an essential indicator of plant water status related to plant growth, temperature maintenance and transpiration. To our knowledge, this study was the first to map the mangrove canopy EWT using Sentinel-2 image at the reserve scale, aiming to explore three modeling strategies in retrieving mangrove canopy EWT, including machine learning models (Random Forest Regression (RFR) and Adaptive Boosting (Adaboost)), radiative transfer models (RTMs, PROSAIL-D and biophysical processor in Sentinel application platform) and hybrid models (PROSAIL-D + RFR and PROSAIL-D + Adaboost). We further investigated the impacts of four ecogeographical factors (species distribution, slope, elevation and distance to dam) on the spatial distribution of canopy EWT using Geodetector method. The results showed that Adaboost (R2 = 0.593, RMSE = 0.771 kg/m2 and RPIQ = 1.820), PROSAIL-D (R2 = 0.451, RMSE = 0.937 kg/m2 and RPIQ = 1.537) and PROSAIL-D + Adaboost (R2 = 0.501, RMSE = 0.890 kg/m2 and RPIQ = 0.887) was the optimal machine learning, RTM and hybrid model in retrieving mangrove canopy EWT, respectively. The red-edge3, NIR and SWIR bands of Sentinel-2 imagery were sensitive to canopy EWT. Moreover, Geodetector analysis demonstrated that species distribution had the most significant impact on canopy EWT, with the interaction between species and distance to dams contributing the most to the spatial difference of canopy EWT. In conclusion, this study suggests that Adaboost model and the hybrid method of PROSAIL-D and Adaboost were promising to map large-scale canopy water conditions in mangrove ecosystems using Sentinel-2 imagery.

Detection of atmospheric aerosols and terrestrial nanoparticles collected in a populous city in southern Brazil.

The main objective of this study is to analyze hazardous elements in nanoparticles (NPs) (smaller than 100nm) and ultrafine particles (smaller than 1µm) in Porto Alegre City, southern Brazil using a self-made passive sampler and Sentinel-3B SYN satellite images in 32 collection points. The Aerosol Optical Thickness proportion (T550) identification was conducted using images of the Sentinel-3B SYN satellite at 634 points sampled in 2019, 2020, 2021, and 2022. Focused ion beam scanning electron microscopy analyses were performed to identify chemical elements present in NPs and ultrafine particles, followed by single-stage cascade impactor to be processed by high-resolution transmission electron microscopy. This process was coupled with energy-dispersive X-ray spectroscopy and later analysis via secondary ion mass spectrometry. Data was acquired from Sentinel-3B SYN images, normalized to a standard mean of 0.83µg/mg, at moderate spatial resolution (260m), and modeled in the Sentinel Application Platform (SNAP) software v.8.0. Statistical matrix data was generated in the JASP software (Jeffreys's Amazing Statistics Program) v.0.14.1.0 followed by a K-means cluster analysis. The results demonstrate the presence of between 1 and 100nm particles of the following chemical elements: Si, Al, K, Mg, P, and Ti. Many people go through these areas daily and may inhale or absorb these elements that can harm human health. In the Sentinel-3B SYN satellite images, the sum of squares in cluster 6 is 168,265 and in cluster 7 a total of 21,583. The use of images from the Sentinel-3B SYN satellite to obtain T550 levels is of great importance as it reveals that atmospheric pollution can move through air currents contaminating large areas on a global scale.

Mapping impervious surface area increase and urban pluvial flooding using Sentinel Application Platform (SNAP) and remote sensing data.

Expansion of urban impervious surface (UIA) and increased urban pluvial flooding (UPF) have an impact on urban dynamics, socioeconomic activities, and our environment. Therefore, monitoring the increase in UIS and its effect on UPF is essential. The notion of this research is based on the mapping of impervious surface area increase in three major cities of Pakistan. There were two key objectives: (i) Mapping impervious surface area growth using the global impervious surface area index (GISAI) on Google Earth Engine from 1992 to 2022 and (ii) mapping the pluvial flood extent in selected urban areas using Sentinel-1 Ground Range Detected (GRD) data. Thus, we have utilized the GISAI for mapping urban impervious surface area (UISA) using Landsat time-series data on GEE. Our research findings revealed that about 16.8%, 23.5%, and 16.4% of the impervious surface have been increased in Islamabad, Lahore, and Karachi, respectively. Also, Lahore city has the highest overall accuracy, aiming at the GISAI of 93%, followed by Karachi and Islamabad with an overall accuracy of 86% and 85%, respectively. The results indicated that urban flooding has occurred in those areas where the ISA has grown during the last three decades. It shows significant changes in the impervious surface area that cause enhanced urban pluvial flooding in major cities of Pakistan. Also, Sentinel-1 data and the SNAP tool significantly mapped flooded areas in the selected zones. So, providing cities and local governments with increased quick flood detection capabilities is essential. It can also provide feasible policy recommendations for Pakistan decision-makers in city management. Therefore, we suggest a modeling-based solution to identify high-risk locations in major cities for upcoming UPF events.

Crop Classification and Crop Acreage Estimation Using Geospatial Technology in the Upper Gangetic Plains of Uttarakhand, India

Timely and accurate crop mapping plays an important role in food security, economic and environmental policies. Crop maps are also utilized for agro-environmental assessments and crop water usage monitoring. As a result, accurate and timely crop classification is essential for agricultural management and monitoring. Because it provides periodic large-scale observations of ground objects, satellite remote sensing has been regarded as an advanced tool to characterize crop types and their distributions on a regional scale. High-resolution, multispectral images of October 13, 2021, December 7, 2021 and March 6, 2022 of sentinel-2 satellite released by the European Space Agency (ESA) have been used for classification. Ground truth points have been collected manually with the android app ‘Mapmarker’ and Google Earth. Further, pre-processing of satellite imageries such as resampling, mosaicking and sub-setting have been done with the Sentinel Application Platform (SNAP) software. Crop classification and acreage estimation was conducted using Maximum Likelihood approach. It is the first time an attempt was made to estimate cropping intensity using geospatial technology in the upper Gangetic plains of Uttarakhand state. Rice and sugarcane areas of 108,884 ha and 11,479 ha, respectively, were estimated from the October 13, 2021 image. Pea crop area was estimated as 6,227 ha from December 7, 2021 image. Using March 6, 2022 image, wheat and mustard crop areas were estimated as 105,334 ha and 2,018 ha, respectively.

Analisis Deformasi Jalan Penghubung Antara Kelurahan Sanga-Sanga Dalam dan Kelurahan Pendingin Tahun 2015 dan 2021 Menggunakan Metode Interferometry Synthetic Aperture Radar

The rise of coal mining in the East Kalimantan area which is close to public road access is thought to affect the quality and sustainability of surrounding public road access. The aim of this research is to determine the use of remote sensing data to analyze land subsidence or deformation that occurs on the connecting road between Sanga-Sanga Dalam Village and Pendingin Village, Sanga-Sanga District, Kutai Kartanegara Regency. The method used in this research is quantitative descriptive, namely determining ground deformation on roads obtained by analyzing Sentinel-1 imagery in 2015 and Sentinel-1 imagery in 2021. Ground deformation can be identified using a satellite-based remote sensing method through interferometry of two SAR images (interferometry synthetic aperture radar or InSAR) uses the Sentinel application platform (SNAP) which is an open source software (FOSS) application. Interferometry can represent changes in the line of sight between the ground and satellites in general. Changes in line of sight can be interpreted as signals of ground deformation. The results of this research can provide adequate information regarding comparative data on land elevation on the Sanga-Sanga Dalam - Pendingin road from several points experiencing land subsidence or deformation on average of 161.3 cm with an average land subsidence rate of 23.04 cm/year . Thus, remote sensing technology using the InSAR technique can be used to determine changes that occur in a short time and at low cost.

Remote monitoring of a concrete bridge using PSInSAR

AbstractStructural health monitoring (SHM) can support decision makers in structural integrity management by providing information about occurrence of damage and its evolution in time. Recently, due to new Synthetic Aperture Radar (SAR) missions, remote sensing through active satellites provides a promising methodology capable of monitoring infrastructure assets. This paper investigates the use of Multi‐Temporal Synthetic Aperture Radar Interferometry (MT‐InSAR) processing to monitor the structural health of bridges. The full interferometry processing chain is applied using Sentinel‐1 synthetic aperture radar data and a combination of open‐source routines from the Sentinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS). Displacement time‐histories are derived from single look complex products by employing the Persistent Scatterer Interferometry (PSInSAR) technique. The PS‐InSAR technique is applied to study the behavior of a concrete bridge located over the Oglio river in Isola Dovarese, Italy, on which also GNSS receivers are installed. The GNSS data are then used to validate the PS‐InSAR measurements. The effects on PS‐InSAR results of the number of SAR images used as well of the target structure are investigated.

Utilising Sentinel-1's Orbital Stability for Efficient Pre-Processing of Radiometric Terrain Corrected Gamma Nought Backscatter.

Radiometric Terrain Corrected (RTC) gamma nought backscatter, which was introduced around a decade ago, has evolved into the standard for analysis-ready Synthetic Aperture Radar (SAR) data. While working with RTC backscatter data is particularly advantageous over undulated terrain, it requires substantial computing resources given that the terrain flattening is more computationally demanding than simple orthorectification. The extra computation may become problematic when working with large SAR datasets such as the one provided by the Sentinel-1 mission. In this study, we examine existing Sentinel-1 RTC pre-processing workflows and assess ways to reduce processing and storage overheads by considering the satellite's high orbital stability. By propagating Sentinel-1's orbital deviations through the complete pre-processing chain, we show that the local contributing area and the shadow mask can be assumed to be static for each relative orbit. Providing them as a combined external static layer to the pre-processing workflow, and streamlining the transformations between ground and orbit geometry, reduces the overall processing times by half. We conducted our experiments with our in-house developed toolbox named wizsard, which allowed us to analyse various aspects of RTC, specifically run time performance, oversampling, and radiometric quality. Compared to the Sentinel Application Platform (SNAP) this implementation allowed speeding up processing by factors of 10-50. The findings of this study are not just relevant for Sentinel-1 but for all SAR missions with high spatio-temporal coverage and orbital stability.

Using C2X to Explore the Uncertainty of In Situ Chlorophyll-a and Improve the Accuracy of Inversion Models

Quality water plays a huge role in human life. Chlorophyll-a (Chl-a) in water bodies is a direct reflection of the population size of the primary productivity of various phytoplankton species in the water body and can provide critical information on the health of water ecosystems and the pollution status of water quality. Case 2 Regional CoastColour (C2RCC) is a networked atmospheric correction processor introduced by the Sentinel Application Platform for various remote sensing products. Among them, the Extreme Case-2 Waters (C2X) process has demonstrated advantages in inland complex waters, enabling the generation of band data, conc_chl product for Chl-a, and kd_z90max product for Secchi Depth (SD). Accurate in situ data are essential for the development of reliable Chl-a models, while in situ data measurement is limited by many factors. To explore and improve the uncertainties involved, we combined the C2X method with Sentinel-2 imagery and water quality data, taking lakes in Wuhan from 2018 to 2021 as a case. A Chl-a model was developed and validated using an empirical SD model and a neural network incorporating Trophic Level Index (TLI) to derive the predicted correction result, Chl-a_t. The results indicated that (1) the conc_chl product measured by C2X and in situ Chl-a exhibited consistent overall trends, with the highest correlation observed in the range of 2–10 μg/L. (2) The corrected Chl-a_t using the conc_chl product had a mean absolute error of approximately 10–15 μg/L and a root-mean-square error of approximately 8–10 μg/L, while using in situ Chl-a had a root-mean-square error (RMSE) of approximately 15 μg/L and a mean absolute error (MAE) of approximately 20 μg/L; both errors decreased by double after correction. (3) The correlation coefficient (R) between Chl-a_t and each data point in the Chl-a model results was lower than that of SD-a_t with each data point in the SD model results. Additionally, the difference in R-value between Chl-a_t and each data point (0.45–0.60) was larger than that of SD-a_t with each data point (0.35–0.5). (4) When using corrected Chl-a_t data to calculate the TLI estimation model, both RMSE and MAE decreased, which were 1μg/L lower than those derived from uncorrected data, while R increased, indicating an improvement in accuracy and reliability. These findings demonstrated the presence of in situ errors in Chl-a measurements, which must be acknowledged during research. This study holds practical significance as some of these errors can be effectively corrected through the use of C2X atmospheric correction on spectral bands.

Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests

Canopy biophysical variables such as the fraction of canopy cover (fCOVER), fraction of absorbed photosynthetically active radiation (fAPAR), and leaf area index (LAI) are widely used for ecosystem modelling and monitoring. The Sentinel-2 mission was designed for systematic global mapping of these variables at 20 m resolution using imagery from the MultiSpectral Instrument. The Simplified Level 2 Prototype Processor (SL2P) is available as a baseline mapping solution. Previous validation over limited sites indicates that SL2P generally satisfies user requirements for all three variables over crops, but underestimates LAI over forests. In this study, Sentinel-2 fAPAR, fCOVER, and LAI products, from SL2P, were validated over 281 sites representative of most North American forest ecozones and also compared to Moderate Resolution Imaging Spectrometer (MODIS) and Copernicus Global Land Service (CGLS) products. In addition to meeting the Committee on Earth Observation Satellites Stage 3 validation requirements for these areas, our study also explores the relationship between bias in SL2P products and canopy clumping and provides empirical bias correction functions for each variable.SL2P was implemented within the Landscape Evolution and Forecasting Toolbox in Google Earth Engine both for efficiency and due to bugs in the Sentinel Application Platform implementation. SL2P was found to underestimate LAI by 20% to 50% over forests with LAI > 2; in agreement with other studies and with comparisons to MODIS and CGLS products. SL2P bias for fCOVER and fAPAR transitions from ∼0.1 at low values to ∼ − 0.1 at high values. Precision error, at one standard deviation, was ∼0.5 for LAI and slightly less than ∼0.1 for fCOVER and fAPAR. Total uncertainty was dominated by bias for LAI and was slightly greater than precision error for fCOVER and fAPAR. Target user requirements were satisfied for 51% of LAI, 37% of fCOVER and 31% of fAPAR comparisons to in-situ measurements. For all variables, accuracy exhibited weak to moderate linear relationships to clumping (r2 ≤0.52), but scatter plots indicated larger negative LAI biases over northern latitude sites where canopies exhibited greater clumping. With the exception of evergreen broadleaf forests, empirical bias correction using in-situ data reduced accuracy error by 40% for fCOVER, 57% for fAPAR and, 92% for LAI and increased the agreement rate with uncertainty requirements by up to 8%. Users of SL2P LAI over forests are recommended to apply bias correction or consider recalibrating SL2P with spatially heterogenous radiative transfer model simulations.

Application of Sntinel-1 radar interferometric images for the monitoring of vertical displacements of the earth’s surface affected by non-tidal atmospheric loading

The vertical movements of the Earth’s surface affected by non-tidal atmospheric loading (NTAL) are analyzed using satellite radar interferometry data. A clear relationship between deformation maps data derived from radar interferometry data and the GNSS time series of permanent stations has been established. The object of the study was the areas around the GNSS stations BYCH (Buchach), GORD (Horodok), CRNT (Chernivtsi). The input data were four pairs of radar interferometric images for the specified areas.Radar satellite images were obtained from the Sentinel-1A spacecraft. Data type — SLC (Single Look Complex) with vertical polarization. Acquisition mode — wideband interferometric IW (Interferometric Wide Swath). Data were processed using SNAP (Sentinel Application Platform) software. The processing yileded maps of vertical displacement of the specified territories where the earth’s surface displacement caused by influence of non-tidal atmospheric loading had taken place. The values obtained on the basis of vertical displacement maps have a high agreement with the results of time series of changes in the altitude position of permanent GNSS stations. The results obtained in the article are of both scientific and practical importance for studying the impact of non-tidal atmospheric loading in large areas. The practical significance is in improving the accuracy of terrestrial geodetic measurements’ processing, in particular high-precision levelling. The research data allow to make corrections of the results of levelling for short-period displacements affected by the influence of non-tidal atmospheric loading (NTAL).

CHALLENGES OF INSAR DEM DERIVATION WITH SENTINEL-1 SAR IN DENSELY VEGETATED HUMID TROPICAL ENVIRONMENT

Abstract. Since the launch of the Sentinel-1 Synthetic Aperture Radar (SAR) in 2014 by the European Space Agency (ESA), there has been renewed interest in SAR data for various application, including DEM derivation with SAR Interferometry (InSAR) technique. However, the success of the InSAR technique in deriving DEM with Sentinel-1 SAR which has been largely acknowledged in sparsely vegetated regions, remains controversial in the densely vegetated environment due to certain challenges, which have rarely been reported. The aim of this study is to highlight some of the obstacles of InSAR DEM derivation with Sentinel-1 SAR over densely vegetated humid tropical environment. Suitable Sentinel-1 image pairs over part of Johor, Peninsular Malaysia, determined in terms of perpendicular and temporal baselines using the Alaska Satellite Facility (ASF) online baseline computation tool, were downloaded from the ASF archive and processed accordingly using the Sentinel Application Platform (SNAP). Difficulty in acquiring suitable image pairs, phase decorrelation and poor coherence, and unwrapping problems are some of the encountered challenges of InSAR DEM derivation with Sentinel-1 SAR in the densely vegetated humid tropics reported in this study. This study shall provide to the geoscientific community insights on InSAR DEM derivation with Sentinel-1 SAR in densely vegetated environments and initiate discussion on the technique’s promise in such environments.

Integration of differential interferometric synthetic aperture radar and persistent scatterer interferometric approaches to assess deformation in enshi city, hubei, China

Land surface deformation can severely damage socioeconomic living conditions around the world. This study aimed to identify the Shaziba landslide and further assess deformation activities in Enshi city. For this purpose, the sentinel-1 C-bands data acquired in ascending directions were processed with Sentinel Application Platform and Stanford Method for Persistent Scatterers (StaMPS) software packages. Our results revealed the location of a landslide that occurred on 21 July 2020 in the Shaziba area, Enshi Prefecture. More interesting deformation results were found in Enshi city for the first time with a deformation range from −51.6 to 54.2 mm/year. We conducted a thorough observation of different urban infrastructures such as commercial and residential buildings, roads, bridges, and airports in Enshi city and along the Qingjiang River to evaluate land surface deformation. Observations revealed that there are a number of influencing factors contributing to disturbing the natural environment and resources in Enshi Prefecture. Of these influencing factors, intensive rainfall is a major cause as are the infiltration of rainfall into the subsurface Silurian strata together with the load of infrastructure in the study area. If this issue is not addressed it could lead to devastating geo-hazard disasters in the future. Scientific approaches to determine various causes of frequent geo-hazards in this region are of great significance for developing early warning systems for disasters and ensuring the safety of residents’ lives and property.

Synergy of dual – polarimetric radar vegetation descriptor and Gaussian processes regression algorithm for estimation of leaf area index

ABSTRACT Gaussian Process Regression (GPR) emerged as a powerful algorithm since last decade in many applications. However, it is not fully explored in remote sensing applications, especially for predicting plant biophysical variables in the heterogeneous environment of India. This kernel-based machine learning technique has effectively replaced conventional approaches for estimating vegetation characteristics from remotely sensed data. In this work, an attempt has been made to test the ability of GPR to estimate the leaf area index (LAI) of wheat crops using the Sentinel-1 (S1) derived Dual-Polarized Radar Vegetation Index (DpRVI) and Sentinel-2 (S2) Top of Atmosphere (TOA) products. Further, the ability of the atmospheric correction procedure was tested by S2 Bottom of Atmosphere (BOA) images. To accomplish this, the field measurements of LAI were carried out from January to March 2020. Further comparisons of the GPR’s performance were made with the Artificial Neural Network (ANN) coupled with the PROSAIL (PROSPECT+SAIL) radiative transfer model, available through the Sentinel Application Platform (SNAP) Biophysical processor. The accuracy of the estimated LAI was evaluated using the statistical indicators, for example, coefficient of determination (R 2 ), Root Mean Square Error (RMSE), and Nash Sutcliffe Efficiency (NSE). The results showed that the synergy of the GPR and DpRVI provided the most accurate result (R 2 = 0.822, RMSE = 0.503 m2 m−2, NSE = 0.831) as compared to the GPR and TOA (R 2 = 0.816, RMSE = 0.596 m2 m−2, NSE = 0.803) and SNAP biophysical processor (based on ANN) (R 2 = 0.696, RMSE = 0.760 m2 m−2, NSE = 0.578). Therefore, the study demonstrated the importance of S1 SAR images and GPR as an alternative tool among the other well-established machine learning algorithms to estimate the crop biophysical parameters.

Rapid Estimation of Decameter FPAR from Sentinel-2 Imagery on the Google Earth Engine

As a direct indicator of vegetation photosynthesis, the fraction of absorbed photosynthetically active radiation (FPAR) serves as a critical input in a series of land surface models. While existing satellite FPAR products are generally at coarse resolutions ranging from 250 m to 1 km, operational FPAR products at fine resolution are urgently needed in studying land surface processes at the plot scale. However, existing methods for estimating fine-resolution FPAR were mainly designed for Landsat data, and few studies have attempted to develop algorithms for Sentinel-2 data. In particular, the operational estimation of decameter FPAR has a higher requirement for the algorithms in terms of generalizability, efficiency, accuracy, and adaptability to Sentinel-2 data. In this paper, we developed a retrieval chain on the Google Earth Engine (GEE) platform to estimate FPAR by learning the relationship between MODIS FPAR and Sentinel-2 surface reflectance. Scale-consistent multilinear models were used to model the relationship between MODIS FPAR and Sentinel-2 surface reflectance, and the model coefficients were regressed from the selected training samples. To account for the spectral and spatial characteristics of the Sentinel-2 data, we designed criteria for selecting training samples and compared different band combinations. Three strategies for band combination were used: (1) green, red, and near infrared (NIR) bands at 10 m resolution (i.e., three bands); (2) green, red, NIR, and red edge (RE) 1, RE2, and RE3 bands at 20 m resolution (i.e., five bands); and (3) green, red, NIR, RE1, RE2, RE3, shortwave infrared1 (SWIR1) and SWIR2 bands at 20 m resolution (i.e., eight bands). Meanwhile, the official Sentinel Application Platform (SNAP) method has also been implemented to estimate the Sentinel FPAR at 10 m and 20 m resolutions for comparison. Both methods were applied to the western Guanzhong area, Shaanxi Province, China, for FPAR estimation of all cloud-free Sentinel-2 images in 2021. The results show that the scaling-based method using five bands at 20 m resolution was the most accurate compared to the in situ measurements (RMSE = 0.076 and R² = 0.626), which outperformed the SNAP method at 10 m and 20 m resolutions and the scaling-based method using other strategies. The results of the scaling-based method using all three strategies were highly consistent with the MODIS FPAR product, while the SNAP method systematically underestimated FPAR values compared to the MODIS FPAR products. The proposed method is more ready-to-use and more efficient than SNAP software. Considering that the service of the MODIS sensor is overdue, the proposed method can be extended to alternatives to MODIS products, such as VIIRS and Sentinel-3 data.