Soil moisture estimation with sentinel 1 satellite. Data mining Jan 1, 2024 · Palmisano et al.

Data mining Jan 1, 2024 · Palmisano et al. The study area extends over the Kairouan Plain in the center of Tunisia (9°23ʹ−10°17ʹE, 35°1ʹ−35°55ʹN) (Figure 1). Remote sensing is one of the most important methods used to estimate soil moisture. T1 - Estimation of field-scale soil moisture content and its uncertainties using Sentinel-1 satellite imagery. developed an approach to estimate soil moisture based on Sentinel-1 and Sentinel-2 data, and the accuracy of soil moisture estimation ranged from (R = 0. The consideration of using Sentinel-1 in combination with Sentinel-2 to estimate soil moisture gained attention even before the launch of the satel lites [32]. With the advancement of Synthetic Aperture Radar (SAR) technology and backscattering models, retrieval of SSM over the land surface at higher spatial resolution became effective and accurate. Remote sensing technology has potential for large-scale and high spatial soil moisture mapping. , 2021). This study aims to estimate surface soil moisture in arid areas using Sentinel-1A SAR data. N1 - ITC Dissertation: 412. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. (2022) proposed a two-step SSM estimation method for bare soils that uses C-band (Sentinel-1) SAR data. Abstract Due to improper agricultural and soil management, there has been a drop in crop yield over the last few years and food security has become a major issue. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial Jan 19, 2022 · This study estimates soil moisture content (SMC) using Sentinel-1A/B C-band synthetic aperture radar (SAR) images and an artificial neural network (ANN) over a 40 × 50-km2 area located in the Geum River basin in South Korea. In order to develop a robust and accurate soil moisture retrieval model, an attempt has been made with Sentinel-1 SAR data over wheat crop by incorporating two Oct 28, 2021 · We present a new perspective on Earth’s land surface, providing a normalised microwave backscatter map from spaceborne Synthetic Aperture Radar (SAR) observations. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. (2017) highlighted the importance of phase referencing in order to estimate soil moisture. This study aimed to explore the possibility of taking advantage of freely available Sentinel-1 (S1) and Sentinel-2 (S2) EO Feb 2, 2024 · We assessed the spatial and temporal variations of surface soil water content (0–0. These ML methods include random forest (RF) and linear Apr 21, 2016 · Hello everyone, does anyone of you know how to use Sentinel-1-Data to map soil moisture? I know that there are many algorithms like SMOSAR or Multi-temporal Bayesian. • 2. The product covers the period from January 2007 to December 2018 with a spatial sampling of 12. In the first step, the atmospherically corrected interferometric phase and coherence were calculated by exploiting nearby Global Navigation Satellite System (GNNS) observations. RSASE. Today, most remotely sensed soil moisture products have difficulties in resolving irrigation signals at the plot scale. Machine learning (ML) techniques are a powerful empirical approach used in many earth science domains to develop excellent soil properties prediction models . • A D-vine copula quantile regression (DVQR) is used for soil moisture estimation. 84. Jan 1, 2023 · Soil moisture is an important variable in agricultural and hydrological applications. , 2020). N2 - The soil moisture content (SMC) expresses the amount of water in the unsaturated zone. Dec 15, 2022 · For example, in case of natural scenario like fully developed and complex canopies, m c ≈ 0 and β c ≈ 0. , 2010; Pulvirenti et al. To delve into this, we collected 350 soil samples from depths ranging from 0 to 30 cm in the northwest region of Iran, measuring SOC levels. In this study Sentinel-1 data acquired on 27 February 2020 was downloaded from Copernicus website and LANDSAT-8 OLI Nov 30, 2023 · Surface soil moisture (SSM) is one of the factors affecting plant growth. This study examines the potential of C-band Sentinel-1 SAR data to derive SSM in Aug 1, 2021 · The 12 advanced statistical and machine learning algorithms were used to estimate soil moisture using the Sentinel-1 model to estimate surface soil moisture from satellite images on a large Mar 7, 2021 · This paper presents the potential for soil moisture (SM) retrieval using Sentinel-1 C-band Synthetic Aperture Radar (SAR) data acquired in Interferometric Wide Swath (IW) mode along with Land Surface Temperature (LST) estimated from analysis of LANDSAT-8 digital thermal data. However, obtaining the best accuracy of SM estimates requires investigating the contribution of vegetation canopy to the accuracy of retrieved SM. Y1 - 2022/3/24. The Sentinel-1 mission, the first of European Space Agency’s five Copernicus Missions, is a constellation of two polar-orbiting satellites launched on April Nov 17, 2021 · Abstract. com 45. The Surface soil moisture (SM) is a crucial variable representing the water content in soil at the topmost soil layer. • The hypothesis of a universal landscape model for VMC and SMC was not supported. Notably, for multivariate DA, the timing and frequency of assimilation data are related to the crop growth stages [ 11 , 12 , 31 ]. In this study, we developed a new nonlinear Erf-BP neural network method to establish a soil-moisture-content-estimation model with integrated multiple-resource remote-sensing data Dr. Soil moisture (SM) is a crucial hydrologic factor that affects the global cycle of I imagine you figured out how retrieve soil moisture by now, but anyway, the article "Synergistic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution" might be help Nov 4, 2023 · Recently, several studies have concentrated on mapping soil moisture using S-2 data and in conjunction with Sentinel-1 data . AU - Benninga, H. 0274, respectively, proving a strong correlation between the experimental soil moisture and satellite-derived soil moisture for mixed Sentinel-2 satellite imagery by Google Earth Engine José Rodolfo Quintana-Molina1*, LST of TOTRAM and can be implemented for soil moisture estimation using Sentinel-2 imagery. INTRODUCTION Soil moisture observation measurements has been assumed as important for hydrology Jun 2, 2014 · For validation, a worldwide in situ soil moisture monitoring program should be implemented. Sahebi Vayghan2*, I. Methods involving direct soil moisture measurement in the field or requiring laboratory tests are commonly used. Jun 21, 2017 · A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. Soil. January 2022; the satellite images is learned, missing optical features are re- Jun 27, 2024 · Following this, NDVI and STR indices are derived using Sentinel satellite images, and soil moisture images are generated using the OPTRAM model with a spatial resolution of 20 m. Esmaeili Sarteshnizi1, S. Mar 15, 2021 · A probabilistic SAR Sentinel-1 soil moisture retrieval at canopy covers is developed. It is an important parameter affecting a wide range of physical science, and thus its monitoring, estimation, and prediction are important in the context of land management [] and agricultural production [1,2], and its changing profile affects the occurrence of natural disasters such as droughts, floods, and Mar 1, 2022 · To estimate the surface soil moisture (SM) using a combination of new spectral indices and methods of Random Forrest (RF) and Multiple Linear Regression (MLR), 11 pedotransfer functions (PTF 1-11) were developed by combining basic soil properties (clay, silt/sand, and bulk density) and spectral indices of Sentinel-2 satellite. The final soil moisture product has a spatial resolution of 30 m and a temporal resolution same as the Landsat-8 (i. This study focused on sample optimization in both quantity and quality. It is characterized by a vast alluvial flat landscape covering 3,000 km 2. Apr 28, 2020 · Estimation of volumetric soil moisture content using the models was performed using two types of radar image (Sentinel 1) and optical image (Sentinel 2), in which optimized bands of Sentinel-2 of accurate soil moisture estimation. However, the influences Sep 1, 2023 · Soil moisture was estimated in rain-fed and irrigated wheat fields using Sentinel-1 data. Jun 8, 2023 · In this work, superficial soil moisture is estimated from SAR data at the field scale on agricultural fields over which the relationship between the co-polarized backscattering coefficient (γ0VV) and the measured soil moisture (SSMv) is both direct and inverse. 3390/rs13163293 Aug 20, 2021 · The new constellation of synthetic aperture radar (SAR) satellite, Sentinel-1, provides images at a high spatial resolution (up to 10 m) typical of radar sensors, but also at high time resolutions Fast soil moisture content (SMC) mapping is necessary to support water resource management and to understand crop growth, quality, and yield. Surface soil moisture estimate fromSentinel-1 andSentinel-2… 1 3 Sentinel-1 mission provides dense time-series of SAR data, making possible to relate short term changes in the backscattering coecient to SSM variations (Balenzano et al. Apr 1, 2024 · The experimental results after incorporating the spatial–temporal constraints demonstrate the efficacy of the proposed model for mapping surface soil moisture over agricultural regions, with significantly improved R 2 = 0. The proposed methodology is based on the change detection technique, applied to a series of measurements over a three Feb 8, 2023 · We propose a new architecture based on a fully connected feed-forward Artificial Neural Network (ANN) model to estimate surface soil moisture from satellite images on a large alluvial fan of Mar 25, 2023 · Data Availability Statement. It contains global daily soil moisture data with a spatial resolution of 1 km, in cm 3 /cm 3, from February 2000 to December 2020 easy access to information on key agricultural parameters such as soil moisture. In this letter, we propose a dual-temporal dual-channel (DTDC) algorithm for soil moisture retrieval by using time-series observations from the Sentinel-1 C-band synthetic aperture radar. , 2019) is used for the precipitation data. Marcel Urban demonstrates how to derive soil moisture estimations from Sentinel-1 SAR data and relate them with in situ information. , 2021; Gao et al. This resulted in a second-order power function between s S 1 and the surface area over which the Sentinel-1 σ 0 observations are averaged. 5 km. According to the results, the highest coefficient of determination (R2) for the Sentinel-2 is related to band 6 with 84%. In past studies, several researchers took potential use of multi-temporal optical data and dual-polarized SAR data to assess drought by estimating soil moisture. , MODIS, Landsat, and Sentinel-2) can provide proxies for estimating soil moisture at high Mar 1, 2021 · This paper presents the potential for soil moisture (SM) retrieval using Sentinel-1 C-band Synthetic Aperture Radar (SAR) data acquired in Interferometric Wide Swath (IW) mode along with Land Aug 25, 2021 · DOI: 10. , 2016); and ASCAT scatterometer onboard the Metop-A satellite (Wagner et al. 'Land in Focus' Massi Aug 21, 2020 · Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Sep 1, 2017 · Synergetic methodology for estimation of soil moisture over agricultural area using Landsat-8 and Sentinel-1 satellite data Remote Sensing Applications: Society and Environment, Volume 15, 2019, Article 100250 Jan 1, 2022 · Soil Moisture Estimation Using Sentinel-1/-2 Imagery Coupled With CycleGAN for Time-Series Gap Filing. J Ecol Rural Environ 36(6):752–761. Concurrently, we obtained vegetation indices from Landsat 8 and Sentinel-2 satellite images. 062). This study evaluates the potential of Sentinel-1A satellite images to estimate soil moisture in a semi-arid region. The study is based on Sentinel-1 (S-1) and Sentinel-2 (S-2) data acquired between July and early December 2017, on a semi-arid area about 3000 km 2 in central Tunisia. org/10. 022 Corpus ID: 238701812; Machine learning algorithms for soil moisture estimation using Sentinel-1: Model development and implementation @article{KumarChaudhary2021MachineLA, title={Machine learning algorithms for soil moisture estimation using Sentinel-1: Model development and implementation}, author={Sumit Kumar Chaudhary and Prashant Kumar Srivastava and Dileep This paper discusses the combined use of remotely sensed optical and radar data for the estimation and mapping of soil texture. Introduction. F. Artificial neural networks were utilized to estimate soil moisture Sep 15, 2021 · This study presents an assessment of a pre-operational soil moisture product at 1 km resolution derived from satellite data acquired by the European Radar Observatory Sentinel-1 (S-1), representing the first space component of the Copernicus program. Study area description. To train the model, we used input features such as radar backscatter values in Vertical–Vertical (VV) and Vertical–Horizontal (VH) polarisation, incidence angle from Sentinel-1, Normalised Difference May 29, 2024 · Accurate spatiotemporal monitoring and modeling of soil moisture (SM) is of paramount importance for various applications ranging from food production to climate change adaptation. Results and discussions4. Soil moisture is an important variable in ecological, hydrological, and meteorological studies. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models for winter wheat crop water content, respectively. Malinong, Australia . Sentinel-1 (A/B) satellite provides high-resolution (∼10 m) synthetic aperture radar data, and its high revisit capability (6 days at the equator) with multi-track data provides great potential for global soil moisture monitoring. • Interdependence structures among variables are analysed and modelled by the DVQR. This soil moisture data generated from Sentinel-1 SAR and SMAP L-band Radiometer to Oct 18, 2022 · A semi-empirical model developed to estimate volumetric soil moisture in bare soils during the dry season using C-band synthetic aperture radar (SAR) imagery acquired from the Sentinel-1 European satellite platform at a 20 m spatial resolution revealed that the linear combination of 0 VV σ +0 VH σ showed a significantly higher R2 than the individual polarimetric channels. Mar 17, 2023 · Radar satellite imagery has been widely used to obtain soil moisture (SM) estimates of high accuracy. Subsequently, we employed The experiments demonstrate that the proposed methodology outperforms the compared state-of-the-art methods if there are missing optical and synthetic-aperture radar (SAR) images. In order Oct 18, 2022 · In the third step, for each DS the ordering of surface soil moisture (SSM) levels of SAR acquisitions based on interferometric coherence is calculated. Feb 24, 2023 · Using an artificial neural network (ANN), Ref. Ulaby (1998) sug-gested that the SAR data with higher wavelengths and lower incidence angle has greater potential to reduce the influence of vegetation cover and surface roughness. Introduction . In this study Dec 2, 2020 · This paper presents the potential for soil moisture (SM) retrieval using Sentinel-1 C-band Synthetic Aperture Radar (SAR) data acquired in Interferometric Wide Swath (IW) mode along with Land Superficial soil moisture is a key hydrological variable playing a main role in the fluxes of water and heat between land and atmosphere. The proposed methodology is used to support a hydrological simulation with the HEC-HMS model, applied at the Keramianos basin which is ungauged for SMC. To take advantage of the great availability of Sentinel-1 high- Aug 20, 2021 · The new constellation of synthetic aperture radar (SAR) satellite, Sentinel-1, provides images at a high spatial resolution (up to 10 m) typical of radar sensors, but also at high time resolutions (6–12 revisit days), representing a major advance for the development of operational soil moisture mapping at a plot scale. Zwieback et al. 785, RMSE = 0. e. These methods, however, are laborious and time-consuming and often give only point-by-point results. Download: Download high-res image (131KB) Download: Download full-size image; Fig. Daily Integrated Multi-satellitE Retrievals for GPM (IMERG) final run V06 product (Huffman et al. 08. The possibility of improving the spatial resolution of Soil Moisture (SM) mapping from microwave satellite radiometers is extremely interesting for hydrological studies in small catchments as well as applications to precision farming. Since the inception of remote sensing in scientific agriculture management, optical remote sensing along with field data has been used for Dec 22, 2017 · The C-band radar instruments onboard the two-satellite GMES Sentinel-1 constellation provide global measurements with short revisit time (about six days) and medium spatial resolution (5 × 20 m Furthermore, Maps of data used by sentinel-1 and sentinel-2 images were obtained. A machine learning model was This study evaluates the potential of Sentinel-1A satellite images to estimate soil moisture in a semi-arid region. Oct 1, 2021 · Same as Sentinel 1 soil moisture product, it is relative soil moisture represented by degree of saturation ranging from 0% to 100%. These results indicate that the LME model can be effectively applied to estimate soil moisture from multi-temporal Sentinel-1 images, which is useful for monitoring flood and drought disasters, and for improving stream flow prediction over ungauged zones. Measurements of Earth's surface soil moisture (Θ) at global scales and at spatial resolutions of 20 × 20 km 2 or coarser are currently provided as products of the Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA) (Kerr et al. • Different methodological alternatives were proposed for applying change detection methods at the field scale. Oct 14, 2021 · Considering variations in surface soil moisture (SSM) is essential in improving crop yield and irrigation scheduling. , 2017). 7328, RMSE = 0. Here, we present a method to retrieve surface soil moisture (SSM) from the Sentinel-1 (S-1) satellites, which carry C-band Synthetic Aperture Radar (CSAR) sensors that provide the richest freely available SAR data source so far, unprecedented in accuracy and coverage. Moisture, Sentinel-1 SAR data, LST, SMI, Backscattering coefficient, LANDSAT 8 OLI, TIRS data. This study deals with modeling SM with the random forest (RF) algorithm using datasets comprising multispectral data from Sentinel-2, Landsat-8/9, and hyperspectral data from the CoSpectroCam sensor (CSC, licensed Soil Moisture Content Estimation Based on Sentinel-1 SAR Imagery Using an Artificial Neural Network and Hydrological Components Jeehun Chung 1, Yonggwan Lee 1, Jinuk Kim 1, Chunggil Jung 2 and Seongjoon Kim 3,* 1 Department of Civil, Environmental and Plant Engineering, Graduate School, Konkuk University, Jan 1, 2021 · Soil moisture observations are of broad scientific interest and practical value for a wide range of applications. 6 min of orbital period. Marrakech, Marocco . The Sentinel-1 radar satellite programme provides an opportunity to monitor water availability (as surface soil moisture) from space on an operational basis at unprecedented fine spatial and temporal resolutions. asr. PY - 2022/3/24. The Sentinel satellite images of soil moisture and NDVI are then integrated into the TOTRAM equation, resulting in the production of LST with a spatial resolution of 20 m. PolSARpro) which is helpful? Thank you for your efforts! Jan 28, 2021 · Firstly, a traditional Oh model was applied to estimate soil moisture content after removing vegetation influence by a water cloud model. • The combination of Sentinel optical and radar data improved model performance. Jul 21, 2019 · In the second part, the feasibility of the neural networks (NNs) for estimation of the soil surface moisture, from Sentinel-1 images is investigated. Jul 1, 2019 · Information about the accurate soil moisture is crucial for agriculture. Satellite synthetic aperture radar (SAR) is well suited for monitoring water content at fine spatial resolutions on the order of 1 km or higher. Our At the same time TC was also used to estimate the observation errors of the satellite soil moisture retrievals by applying it to triplets built among first layer SWAT soil moisture (this was assumed as the reference against which rescale observations), one active soil moisture retrieval (either ASCAT or SCATSAR) and the passive soil moisture Oct 11, 2021 · With satellite observations increasingly available 16, optical and near-infrared satellite sensors (e. Datta et al. This study aimed to compare the eectiveness of Landsat 8 and Sentinel 2 satellite images in estimating soil surface moisture between Gorgan and Aqqala. 095) to (R = 0. On the one hand, a How to download and extract soil moisture data from Sentinel-1 and SMAP. , 2010, Vereecken et al. Satellite data provide useful information regarding soil moisture and have been frequently used to estimate soil moisture in recent years. Five different high-resolution satellite soil moisture products are considered (see Table 1). 05 m3/m3 for the Guyuan and In this wo rk, the algorithm SM2RAIN (Soil Moisture to Rain) for rainfall estimation is applied to a high resolution soil moisture product derived from Sentinel -1, named S1 -RT1, characterized by 1 km spatial resolution (500 m spacing), and to the 25 km Aug 22, 2018 · Soil moisture is a key environmental variable, important to, e. Nov 6, 2021 · The RISAT-1 derived soil moisture has R² =0. In this study, an algorithm based on Artificial Neural Networks (ANN) is proposed, with the aim of improving significantly the spatial resolution of the Soil Oct 1, 2023 · A rise in soil moisture variation was recorded on January 25 as a result of irrigation. 53, whereas SENTINEL-1 shows R² =0. , 2014), in particular during droughts and heatwaves (Miralles et al. During the field campaigns, 58 field parcels were visited to analyze a total of 142 measurements. 2019. Sep 16, 2022 · SMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. Apr 13, 2022 · The objective of the study was to estimate soil moisture (SM) from Sentinel-1 (S-1) satellite images acquired over wetlands. in retrieving soil moisture using KOMPSAT-5 and Sentinel-1 remotely sensed data at high-spatial resolution over agricultural fields, with subsequent uses for crop growth and yield estimation. Our objective was to develop and test an operational approach to The results of this study compare optimized soil moisture estimation method with other soil moisture products on Korean mountainous area. 25°spatial resolution. , 2019). Fast soil moisture content (SMC) mapping is necessary to support water resource management and to understand crop growth, quality, and yield. , 2023). These data sets include: surface and subsurface soil moisture (mm), soil moisture profile (%), surface and subsurface soil moisture anomalies (-). Remote Sensing, 13 (16), 3293. Accurate data of SM are essential for many applications such as drought monitoring, vegetation modelling, weather forecasting, and agriculture management. Moreover, changes in soil moisture were found to be associated with a loss of coherence. They used VV, and VH polarised images as the input features Jan 10, 2024 · A convolutional neural network (CNN) architecture that can predict soil moisture content over agricultural areas from Sentinel-1 images is proposed and results show that VV polarization is better than VH polarization for soil moisture retrieval, and that Sigma naught, GammaNaught, and Beta naught have the same influence on soil moisture estimation. 0351 vol This study also concluded that using multiple polarization in the machine Aug 17, 2023 · Soil moisture (SM) is a crucial hydrologic factor that affects the global cycle of energy, carbon, and water, as well as plant growth and crop yield; therefore, an accurate estimate of SM is important for both the global environment and agriculture. , 2018). the radiometer on the Soil Moisture Active Passive (SMAP) satellite, where the goal is remote sensing over land (soil Jul 29, 2023 · Soil moisture estimates in a grass field using Sentinel-1 radar data and an assimilation approach. Permanent water bodies and built-up areas are masked out with white color. Feb 18, 2022 · Soil moisture content (SMC) plays an essential role in geoscience research. Google Scholar Sep 26, 2020 · Request PDF | On Sep 26, 2020, Linghai Jiang and others published An Improved Change Detection Method for Soil Moisture Retrieval using Sentinel-1 and Smap Data | Find, read and cite all the Feb 27, 2024 · Soil moisture (SM) is an important quantity to examine in terms of agriculture, meteorology, and hydrology to understand the evaporation cycle and drought mechanisms. Satellite-based SM data have been provided by the National Aeronautics and Space Administration (NASA)’s Soil Moisture Active Passive (SMAP) and Sentinel 1 and Sentinel 2 data for soil moisture estimation using the WCM [35,50,51], the change detection method [5,53], a neural network technique [29], and a machine learning regression The recent deployment of ESA’s Sentinel operational satellites has established a new paradigm for remote sensing applications. Finally, to realize the full potential of satellite-based soil moisture estimation for watershed applications, it will be necessary to continue sensor development, improve image availability and timely delivery, and reduce image cost. An inversion algorithm is adapted to the charateristics of the single field and applied to SAR signal differences. We use a change detection approach accompanied by vegetation correction to estimate soil moisture over shrublands and croplands. Exactly at the time when satellite passes over the study area, we have collected soil samples at 37 different locations and measured the soil moisture from 5 cm below the ground surface using ML3 theta probe. 2. Due to the high-speed computing Sep 1, 2023 · This paper presents the retrieval of high resolution ( ∼ 1 km) soil moisture data from Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter measurements using a new bistatic radiative transfer modeling framework (RT1) previously only tested for scatterometer data. Temporal variation of ground measured LAI and soil moisture from January to March 2020. 0451 vol, and MAE = 0. Soil moisture estimation through Jul 18, 2019 · As a limitation of S1 data for root soil moisture monitoring, the high-resolution SMAP/Sentinel-1 soil moisture product may be used to further improve the crop yield estimation at the regional scale. The current study focuses on identifying a lossless ML model among RF, SVM, and KNN to estimate surface soil moisture during the pre-monsoon rabi crop season (January to March) using the Sentinel-1 SAR data. 2021. The Sentinel-1 Global Dec 1, 2020 · Sentinel-1’s radiometric uncertainty (s S 1, in dB) was estimated by the standard deviation of Sentinel-1 σ 0 observations from a target which is assumed time-invariant (Benninga et al. This study aimed to compare the effectiveness of Landsat 8 and Sentinel 2 satellite images in estimating soil surface moisture between Gorgan and Aqqala. Sep 17, 2019 · However, comparative analyses of optical and SAR-based vegetation water content models are lacking, especially those that use data from the Sentinel-1 satellite. In the fourth step, for each DS the coherence due to SSM variations is calculated. Sentinel-1 is a dual-polarized radar remote sensing satellite with the potential ability to rapidly and continuously monitor crop water content . • Three change detection methods were evaluated. Soil moisture (SM) is an important variable in the water cycle as it controls the exchange of both water and energy between the land surface and the atmosphere (Seneviratne et al. To ESTIMATION OF SOIL MOISTURE USING SENTINEL-1 AND SENTINEL-2 IMAGES R. 1. In the past, the results often lacked a sufficient spatial resolution for a local application. Surface soil moisture controls the partitioning of precipitation into runoff and infiltration, energy is dissipated through the evaporation and transpiration of surface and root-zone moisture, and transpiration is linked to CO 2 uptake by plants. 100250 Corpus ID: 199094318; Synergetic methodology for estimation of soil moisture over agricultural area using Landsat-8 and Sentinel-1 satellite data An integrated SM model is developed across various crop surfaces by using a variety of input data and DNN (which can learn the complexity and nonlinearity of the various data), which suggests that precipitation affects surface erosion and water layer formation, and vegetation adds complexity to the SM estimate. For a country like India, with a huge population to cater, the problem becomes more serious. https://doi. The C-band radar instruments onboard the two-satellite GMES Sentinel-1 constellation provide global measurements with short revisit time (about six days) and medium spatial resolution (5 × 20 m), which are appropriate for watershed scale hydrological applications. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. With the new Sentinel-1 sensor it seems possible to derive more detailed SM-maps Dec 31, 2023 · Soil organic carbon (SOC) stands out as a crucial indicator for assessing soil properties due to its direct impact on soil productivity. , 1999 Jun 4, 2024 · Li BX, Chen XY (2020) Synergic Use of Sentinel-1 and Sentinel-2 Images for soil moisture retrieval in vegetation covered agricultural areas of Jingxian county of Heibei Province. Although the Sentinel-1 C-band microwave data is sensi-tive to vegetation cover during soil moisture estimation, the Jan 13, 2023 · The aim of this study is to estimate surface soil moisture at a spatial resolution of 500 m and a temporal resolution of at least 6 days, by combining remote sensing data from Sentinel-1 and Sep 1, 2023 · A positive relationship with increased sensitivity between soil moisture and interferometric phase, compared to the two other observables, was reported. 1016/j. 307, RMSE = 0. This study aims to use Sentinel-1 radar backscatter and Sentinel-2 multispectral imagery to estimate SSM at high spatial (10 m) and temporal resolution (at Develop, implement and test soil moisture retrieval methods using Sentinel-1 dataThe C-band Sentinel-1 (S-1) European Radar Observatory, with its two satellites (S-1A & B), is the only operating SAR mission with monitoring capabilities, frequent revisit and large geographical coverage that will guarantee data continuity over the next decades. 825 and 0. Soil moisture Estimation. Therefore, earth observation (EO) plays a key role due to its ability of almost real-time monitoring of large areas at a low cost. , DpRVI c ≈ 1. Sevilla, Spain Dec 28, 2023 · Estimating various parameters such as the physical parameters of the soil using remote sensing is not easy. In this study, Modified Dubois Model (MDM) semi-empirical model with Topp's model is used for retrieval of soil moisture. This paper aims to explore the potential of Sentinel-1 for estimating surface soil moisture using a multi-temporal approach. For evaluation of the models, all data were employed. SAR-derived remote sensing products can be valuable input features for estimating SM. The global water, energy and carbon cycles are linked through the moisture contained in the soil surface and root-zone. Two products are based on Sentinel-1 observations only. An alternative its ability to estimate soil moisture because of its suscep - tibility to volume dispersion, geometric alignment, and vegetation features. Its spatial and temporal variations are indeed crucial for applications such as environmental modelling and agricultural management. The hydrological components characterized by the antecedent precipitation index (API) and dry days were used as input data as well as SAR (cross-polarization (VH) and Aug 1, 2019 · DOI: 10. Sentinel-2 data are more suitable for retrieving soil moisture over wheat-covered areas . Nov 20, 2020 · Satellite Data and Pre-processing. This study would be the base for improvement of soil moisture estimation algorithm on the Korean peninsula. Feb 17, 2023 · The volume of water trapped in the voids of a soil mass contributes to soil moisture. , farmers, meteorologists, and disaster management units. In contrast, SSM can vary across a field due to uneven precipitation, soil variability, etc. JULES-CHESS simulation Sep 15, 2021 · 1. ( 2017) estimated soil moisture using Sentinel-1 images, vegetation indices, and surface temperature from Landsat 8 images. Secondly, support vector regression (SVR) and generalized regression neural network (GRNN) models were used to establish the relationships between various remote sensing features and real soil moisture. Jun 6, 2017 · 1 Introduction. The differences of Jun 1, 2023 · 1. Therefore, this study seeks to explore the feasibility of soil moisture estimation at high-resolution (around 10 m) using Sentinel-1 remote sensing radar data. Feb 17, 2023 · The GSSM1 km dataset can be accessed at: https://figshare. An effective method for improving the accuracy of soil moisture retrieval is ligent statistical models such as M5 tree decision-making algorithms should be used for estimation. In the present work the Landsat-8, Sentinel-1 satellite data and Modified Water Cloud Model (MWCM) have been used to Feb 15, 2022 · 4. Evaluation of the satellite and ground datasets. But what are the basic steps I schould follow? What would be the best toolbox (s1tbx, s2tbx, s3tbx) / method / software (e. Post-launch, the idea has been explored Dec 1, 2022 · The objectives of this study were: (1) to determine the effective soil moisture depth to which the CN is most related; (2) to evaluate a discrete and a linear relationship between AMC and soil May 19, 2022 · Soil moisture is an essential parameter for a better understanding of water processes in the soil–vegetation–atmosphere continuum. However, the challenge of an operational soil moisture product with high spatial resolution and high spatiotemporal coverage remains elusive (Peng et al. The Dec 1, 2013 · The R2 and RMSE of the model were observed to be 0. In the present work the Landsat-8, Sentinel-1 satellite data and Modified Water Cloud Model (MWCM) have been used to retrieve soil moisture from an agriculture area. The scientific community has made significant progress in estimating soil moisture from satellite-based Earth observation data, particularly in operationalizing coarse-resolution (25-50 km) soil moisture products. Using maps of data shows the potential of applied filters to sentinel-1 and bands used for sentinel-2 in the estimation of soil moisture. Therefore, intelligent statistical models such as M5 tree decision-making algorithms should be used for estimation. 5, results in a maximum value of DpRVI c, i. Sep 1, 2023 · Mira et al. Jul 1, 2020 · The active sensor systems able to cover frequencies suitable for RS soil moisture estimations include Sentinel 1-A satellite, which provides C-band images (Şekertekin et al. Several methodologies are often considered in the inversion of SAR signals: machine learning techniques, such as Sep 22, 2021 · We apply the Support Vector Regression (SVR) machine learning model to estimate surface roughness on a large alluvial fan of the Kosi River in the Himalayan Foreland from satellite images. , 2016); C-band RADARSAT-1/2; Soil Moisture Active Passive (SMAP) missions (Chan et al. • Vegetation type models showed promise for VMC in shrubland conditions. A Sensitivity Analysis to quantify the effects of parameters on the dual-polarized backscatters of Sentinel-1 based on a Water Cloud Model (WCM) and multiple global SA methods and indicates that VV- and VH- polarized backscatter at small incidence angles are the optimal options for soil moisture and surface roughness estimation, respectively Mar 11, 2022 · Surface Soil Moisture (SSM) is a key factor for understanding the physical process between the land surface and atmosphere. In the Sentinel-1 approach, several Jun 3, 2022 · Knowledge of soil moisture at various stages of agricultural production plays a fundamental role in better managing water resources. Sentienl-1 comprises a constellation of two polar-orbiting satellites (Sentinel-1A and 1B), revolving in near-circular sun-synchronous orbit at 693 km altitude with 98. 54 proposed a regression-based machine learning model to estimate surface soil moisture from Sentinel-1 images at 12 days temporal resolution. NASA SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid, V03 is used for soil moisture data (Das et al. g. The Sentinel-1, a polar orbiting satellite system mission is a part of the Global Monitoring for Environment and Security (GMES) program of the European Space Agency (ESA) and the European Commission (EC) and 2 days ago · The NASA-USDA Global soil moisture data provides soil moisture information across the globe at 0. In addition to satellite acquisitions, texture Jan 28, 2020 · Data description Sentinel-1 SAR data. com Jun 1, 2024 · We modelled vegetation and soil moisture content with Sentinel-1 and 2 satellite data. A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. Therefore, earth observation (EO) plays a key role due to its ability of Resulted soil moisture is applied with jet color map for better interpretation. , 2010), the Soil Moisture Active Passive (SMAP) mission of the National Aeronautics and Space Administration (NASA Dec 26, 2022 · Soil moisture plays a significant role in the global hydrological cycle, which is an important component of soil parameterization. It also confirms the possibility of two different polarization σ°HH and σ°VV backscatter involving MDM. 25°x0. 1016/J. We used the Integral Equation Model (IEM) coupled with the Water Cloud Model (WCM) (herein referred to as the IWCM) to estimate surface SM using soil moisture, methodologies and surface soil moisture (SSM) products that exploit satellite earth observation data have been developed (Balenzano et al. Oct 1, 2023 · This study aims to use Sentinel-1 radar backscatter and Sentinel-2 multispectral imagery to estimate SSM at high spatial (10 m) and temporal resolution (at least 5 days) over an agricultural domain. The study was carried out during the years 2015–2017 in the Biebrza Aug 4, 2023 · Soil moisture plays an important role in crop yield estimation, irrigation management, etc. After pre-processing the remote sensing data, backscattering . This algorithm utilizes the ancillary information of vegetation water content derived from optical images and assumes no variation on the surface roughness during the two consecutive radar measurements Apr 23, 2017 · Simple method for soil moisture estimation from sentinel-1 data, 2016. , 2019, Teuling, 2018). The main objective of this study has been to examine the capabilities of integration of the optical and radar images for Jun 27, 2019 · Soil moisture map over the Kairouan Plain, derived from the combined use of Sentinel-1 and Sentinal-2 data, with the upper maps representing 01/12/2017 (in the left) and 20/09/2017 (in the right). (2021) analyzed the sensitivity of Sentinel-1 SAR data towards soil moisture and found that the good sensitivity with soil moisture underneath wheat and barley crops. Soil moisture direct measurements lack spatial representativeness, while soil moisture spatialized information could be Jul 12, 2021 · Quality improvement of satellite soil moisture products by fusing with in-situ Soil moisture in the Biebrza wetlands retrieved from Sentinel-1 imagery. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints Aug 1, 2019 · Information about the accurate soil moisture is crucial for agriculture. Except for this, the soil moisture variation was subtle throughout the study. • The nonlinear DVQR outperforms the linear MLQR over most vegetation covers • Reliable up-to-date information on the current water availability and models to evaluate management scenarios are indispensable for skilful water management. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results. 1. The S1-RT1 product is obtained by applying a first-order radiative transfer model to Sentinel-1 observations and it has been recently developed by the Vienna University of Technology, TU Wien (Quast et al. Manitoba, Canada . Red color represents low soil moisture (dry soil-0%) whereas blue represents high soil moisture (wet soil-60%). 2. 18° of inclination and 98. Alexakis et al. , 16 days). A model for monitoring winter wheat Sentinel-1A satellite which was launched in 2014 by the European Space Agency (ESA) as crucial for reconnaissance surface soil moisture estimation wherein there is paucity of Jul 1, 2023 · The time series Sentinel-1 C-band radar signal and soil moisture show strong correlations, and the RMSE of the estimated soil moisture from radar data was lower than 0. However, offline remote sensing data processing is time-consuming and resource-intensive, and significantly hampers the efficiency and timeliness of soil moisture mapping. 3. Jazirian3 1 Department of Surveying Engineering, Marand Technical College, University of Tabriz, Tabriz, Iran - Rouhollahesmaili77@gmail. 1 m) using seven in situ soil moisture probes and Sentinel-1 satellite data over the 2016 and 2017 cropping seasons in irrigated fields of the Central Sand Plains, Sep 17, 2019 · This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. After pre-processing the remote sensing data Apr 25, 2018 · Soil moisture (SM) is a significant parameter influencing various environmental processes in hydrology, ecology, and climatology. Mar 20, 2020 · The aim of this study is to estimate surface soil moisture at a spatial resolution of 500 m and a temporal resolution of at least 6 days, by combining remote sensing data from Sentinel-1 and optical data from Sentinel-2 and MODIS (Moderate-Resolution Imaging Spectroradiometer). vsos mbgt xtdz lbw wav yubqe bqwd ydcjo lcyi kvoiuhf