Article
Geography, Physical
Juan Guerra-Hernandez, Lana L. Narine, Adrian Pascual, Eduardo Gonzalez-Ferreiro, Brigite Botequim, Lonesome Malambo, Amy Neuenschwander, Sorin C. Popescu, Sergio Godinho
Summary: This study used ICESat-2 satellite data combined with other data and models to estimate and map canopy height and aboveground biomass in Mediterranean forest areas. The results suggest that a multi-sensor approach may be used to extrapolate ICESat-2 derived estimates of aboveground biomass.
GISCIENCE & REMOTE SENSING
(2022)
Article
Environmental Sciences
Alberto Udali, Emanuele Lingua, Henrik J. Persson
Summary: This study focused on continuous monitoring of a hemi-boreal Swedish forest using multitemporal satellite images, particularly the C-band synthetic aperture radar data for forest type and tree species classification. The results showed promising classification accuracy for forest type, but lower accuracy for tree species, with winter images performing similarly to images from the entire year.
Article
Environmental Sciences
Ignacio Borlaf-Mena, Ovidiu Badea, Mihai Andrei Tanase
Summary: This study examined the ability of Sentinel-1 C-band to distinguish between forests and other common land use classes in two different sites, and found that adding coherence features can improve the accuracy of temperate forest classification.
Article
Remote Sensing
T. C. van Hateren, M. Chini, P. Matgen, L. Pulvirenti, N. Pierdicca, A. J. Teuling
Summary: High spatial resolution soil moisture (SM) datasets are valuable for various applications, such as hydrological extremes monitoring, weather prediction, and precision agriculture. Remotely sensed SM has advantages over in situ data in large scale applications due to its gridded estimates and less labor-intensive nature. This study explored the potential and limits of high spatial resolution active microwave SM observations using Sentinel-1 C-band SAR data. The results demonstrated that Sentinel-1 data with a spatial resolution of 60 x 60 m2 or coarser can accurately estimate temporal within-field SM variability, making them valuable for monitoring agricultural fields.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Environmental Sciences
Yao Gao, Xiuqing Liu, Wentao Hou, Yonghui Han, Robert Wang, Heng Zhang
Summary: This study analyzed the characteristics of saline soil in extremely arid regions using dual-band quadrature-polarimetric SAR images, discussed potential relationships between polarimetric parameters and salinity, and established a regression model for monitoring salt content.
Article
Geochemistry & Geophysics
Oliver Cartus, Maurizio Santoro, Urs Wegmuller, Nicolas Labriere, Jerome Chave
Summary: This study found that the accuracy of biomass retrieval using Sentinel-1 repeat-pass coherence images is highest when images are acquired in extended periods of dry conditions, and can be improved by combining multiple images. The difference in retrieval accuracy between multitemporal 6- and 12-day repeat-pass coherence images is minimal, suggesting that both intervals are suitable for AGB mapping in semiarid forest areas.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Agronomy
Marcel M. El Hajj, Kasper Johansen, Samer K. Almashharawi, Matthew F. McCabe
Summary: This study investigates the potential of using Sentinel-1 Synthetic Aperture Radar (SAR) data to estimate and map the water uptake rate in a high-density olive orchard in Saudi Arabia. The results show that the water uptake rate can be estimated and mapped at the plot level using random forest regression, and it correlates with vapor pressure deficit. This research provides the first exploration of using SAR data to infer the water uptake rate in commercial scale orchards, offering valuable insights for improved water management and irrigation control.
AGRICULTURAL WATER MANAGEMENT
(2023)
Article
Geosciences, Multidisciplinary
Subrata Nandy, Ritika Srinet, Hitendra Padalia
Summary: This study successfully mapped forest canopy height by integrating different satellite data and investigated the impact of incorporating canopy height information into AGB models on prediction accuracy. The results demonstrated that incorporating canopy height information significantly improved the accuracy of AGB predictions.
GEOPHYSICAL RESEARCH LETTERS
(2021)
Article
Environmental Sciences
David Mengen, Thomas Jagdhuber, Anna Balenzano, Francesco Mattia, Harry Vereecken, Carsten Montzka
Summary: This study proposes a novel approach for estimating volumetric soil moisture content for agricultural areas using multi-orbit Sentinel-1 C-band time series. The approach achieves a temporal resolution of one to two days and utilizes a short-term change detection method. The method reduces the impact of varying incidence angles on the backscattering signal through incidence angle normalization and Fourier Series transformation. The algorithm also corrects for vegetational changes using the C-band co-polarized backscattering signal. The method shows promising results and can be applied globally in a cloud-processing environment.
Article
Geosciences, Multidisciplinary
Remi Madelon, Nemesio J. Rodriguez-Fernandez, Hassan Bazzi, Nicolas Baghdadi, Clement Albergel, Wouter Dorigo, Mehrez Zribi
Summary: High-resolution (around 10-100m) surface soil moisture observations are important for various applications. This study adapted the (SMP)-M-2 algorithm to work at 1 km resolution and extended its application to herbaceous vegetation types. The algorithm combines Sentinel-1 and Sentinel-2 data and shows good agreement and accuracy compared to other datasets.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2023)
Article
Environmental Sciences
Andrea Monti-Guarnieri, Clement Albinet, Alessandro Cotrufo, Niccolo Franceschi, Marco Manzoni, Nuno Miranda, Riccardo Piantanida, Andrea Recchia
Summary: This paper presents a review and method for extracting actively sensed data and identifying Radio Frequency Interferences (RFI) in TOPSAR acquisition mode. By generating measurements of Earth Brightness Temperature (BT) and RFI equivalent temperature, and cross-comparing results from different sensors, precise SAR data denoising and RFI mitigation can be achieved.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Werner Alpers, Yuan Zhao, Alexis A. Mouche, Pak Wai Chan
Summary: This paper investigates radar signatures of rain over the ocean, providing evidence that the high radar backscatter in bright patches is caused by volume scattering from non-spherical hydrometeors within the melting layer, rather than surface scattering. Analysis of SAR images and radar data validates this theory, highlighting the importance of using cross-polarization SAR images to detect radar backscattering from the melting layer.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Environmental Sciences
Fernando Pech-May, Raul Aquino-Santos, Jorge Delgadillo-Partida
Summary: Floods are increasing in frequency and danger worldwide, with climate change and land use being major contributing factors. In Mexico, floods occur annually in various regions, causing significant losses and negative impacts on multiple industries. This paper presents a strategy using satellite imagery, the U-Net neural network, and ArcGIS platform to classify flooded areas in Tabasco, Mexico. Results demonstrate that U-Net performs well despite limited training samples, with increased precision as training data and epochs increase.
Article
Environmental Sciences
Pedro Torralbo, Rafael Pimentel, Maria Jose Polo, Claudia Notarnicola
Summary: This study explores the possibilities of using Sentinel-1 SAR imagery to monitor snowmelt dynamics and streamflow response in semi-arid mountains. The results demonstrate that SAR imagery can accurately capture the different stages of snowmelt throughout the year and reveal a linear connection between melting dynamics and streamflow.
Article
Chemistry, Physical
S. Meenakshi, R. Saravanan, N. Srinivasan, D. Dhayanithi, Nambi Venkatesan Giridharan
Summary: The magneto-electric ceramic composite of (1-x)BaTiO3 + xZnFe(2)O(4) (x = 0.2, 0.4, 0.6, 0.8) was synthesized by solid state method, displaying complex electrical and magnetic properties. As the ferrite content increased, the composite transitioned from lossy capacitance to resistive capacitance, with an increase in dielectric constant but also dielectric loss. The Ti-O bond played a significant role in the electrical characterization, while the composite exhibited small ferromagnetic properties, with saturation magnetization increasing with ferrite content.
JOURNAL OF ALLOYS AND COMPOUNDS
(2021)
Article
Environmental Sciences
Diego Urbina-Salazar, Emmanuelle Vaudour, Anne C. Richer-de-Forges, Songchao Chen, Guillaume Martelet, Nicolas Baghdadi, Dominique Arrouays
Summary: This study presents a reliable method for mapping soil organic carbon (SOC) content over wide regions by using Sentinel-2 (S2) temporal mosaics of bare soil and soil moisture products, along with other environmental covariates. The models using all the covariates showed the best performance, and the results provided valuable information on the spatial variability of SOC.
Article
Environmental Sciences
Manizheh Rajab Pourrahmati, Nicolas Baghdadi, Ibrahim Fayad
Summary: The GEDI LiDAR system is an effective tool for estimating forest biophysical parameters, particularly canopy height, at a global scale. This study found that GEDI canopy height estimation is more accurate for needleleaf forests compared to broadleaf and mixed forests. The study also identified the impact of foliage condition and plant area index on GEDI canopy height accuracy, suggesting the importance of filtering GEDI data based on seasonal acquisition time for specific forest types.
Review
Environmental Sciences
Anne C. Richer-de-Forges, Qianqian Chen, Nicolas Baghdadi, Songchao Chen, Cecile Gomez, Stephane Jacquemoud, Guillaume Martelet, Vera L. Mulder, Diego Urbina-Salazar, Emmanuelle Vaudour, Marie Weiss, Jean-Pierre Wigneron, Dominique Arrouays
Summary: Soils are a finite resource facing threats due to human activities, making it necessary to map and monitor them to prevent degradation. The development of digital soil mapping (DSM) approaches, supported by remote sensing (RS) data, has allowed for high-resolution mapping of soils and assessing changes over time. French (inter)national research has played a significant role in pioneering the use of RS imagery in DSM.
Article
Environmental Sciences
Michel Le Page, Thang Nguyen, Mehrez Zribi, Aaron Boone, Jacopo Dari, Sara Modanesi, Luca Zappa, Nadia Ouaadi, Lionel Jarlan
Summary: The difficulty of calculating the daily water budget of irrigated fields is due to uncertainty in irrigation amounts and timing. Automated detection of irrigation events using SAR and optical satellite observations can simplify this process. This study analyzed the performance of an established algorithm using a large irrigation dataset and found that the frequency of SSM observations and irrigation events affected the scores, and replacing the SSM model improved the F-score and narrowed the error on cumulative seasonal irrigation.
Article
Environmental Sciences
Yen-Nhi Ngo, Dinh Ho Tong Minh, Nicolas Baghdadi, Ibrahim Fayad
Summary: Estimating tropical forest height using remote sensing data can help understand carbon cycles. Our study shows the potential value of using RS data to extrapolate GEDI LiDAR measurements. We found that selected RS features and GEDI RH metric can estimate vegetation heights accurately. Combining GEDI and RS data is a promising method to map CHM values.
Article
Environmental Sciences
Mohamad Hamze, Bruno Cheviron, Nicolas Baghdadi, Dominique Courault, Mehrez Zribi
Summary: This study developed an approach using Sentinel-1 SAR data and the Optirrig crop growth and irrigation model to detect irrigation dates and amounts for maize crops in Southern France. The method analyzed changes in surface soil moisture derived from SAR data to detect irrigation events at the plot scale. The results showed relatively high accuracy in detecting irrigation dates, but varied performance in detecting irrigation amounts depending on the climatic conditions.
Article
Environmental Sciences
Mouad Ettalbi, Nicolas Baghdadi, Pierre-Andre Garambois, Hassan Bazzi, Emmanuel Ferreira, Mehrez Zribi
Summary: Soil moisture maps are indispensable for hydrological, agricultural, and risk assessment purposes, and can now be developed at high spatial resolution using Sentinel-1 SAR data. This paper presents an improved and fully autonomous method for high-resolution soil moisture mapping in bare agricultural areas, without relying on a weather forecasting framework. The proposed solution utilizes neural network techniques and radar data integration to estimate soil moisture accurately, and the results show that it outperforms the traditional method using a priori weather information.
Article
Environmental Sciences
Hayfa Zayani, Youssef Fouad, Didier Michot, Zeineb Kassouk, Nicolas Baghdadi, Emmanuelle Vaudour, Zohra Lili-Chabaane, Christian Walter
Summary: Understanding the spatial and temporal variability in soil organic carbon (SOC) content is important for assessing soil fertility as well as related parameters. This study evaluated the combination of remote sensing time series with laboratory spectral measurements using machine and deep-learning algorithms to improve predictions of SOC content. Different models and approaches were utilized, and the use of additional information such as soil moisture and laboratory indices improved model performance
Article
Environmental Sciences
Oscar Rojas-Munoz, Jean-Christophe Calvet, Bertrand Bonan, Nicolas Baghdadi, Catherine Meurey, Adrien Napoly, Jean-Pierre Wigneron, Mehrez Zribi
Summary: Surface soil moisture (SSM) observed by satellites is an essential component of the Earth system. In this study, aggregated SSM observations from Sentinel-1 and Sentinel-2 are assimilated into the ISBA land surface model using the LDAS-Monde tool. The assimilation of SSM alone has a small impact on simulated soil moisture, but a marked impact is observed when leaf area index (LAI) is assimilated, especially when combined with SSM.
Article
Remote Sensing
Maryam Teimouri, Mehdi Mokhtarzade, Nicolas Baghdadi, Christian Heipke
Summary: This study proposes a novel method for generating virtual training labels (VTL) using self-organizing maps (SOM) to sub-divide crop training samples and assign labels to unlabeled pixels. The results of crop classification experiments show that the method is effective in generating VTL and improving classification accuracy.
PFG-JOURNAL OF PHOTOGRAMMETRY REMOTE SENSING AND GEOINFORMATION SCIENCE
(2023)
Article
Geochemistry & Geophysics
Mehrez Zribi, Karin Dassas, Vincent Dehaye, Pascal Fanise, Emna Ayari, Michel Le Page
Summary: The objective of this study is to analyze the variations in global navigation satellite system reflectometry (GNSS-R) data based on land cover. Airborne measurements using the global navigation satellite system reflectometry instrument (GLORI), a polarimetric instrument, were conducted at an agricultural site in Urgell, Spain in July 2021. In situ measurements of soil and vegetation properties were obtained simultaneously. The study discusses the behavior of copolarization (RR) and cross polarization (RL) reflectivity as a function of land use and estimates the distribution of coherent and incoherent components in the reflected power for different land cover types.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Geosciences, Multidisciplinary
Remi Madelon, Nemesio J. Rodriguez-Fernandez, Hassan Bazzi, Nicolas Baghdadi, Clement Albergel, Wouter Dorigo, Mehrez Zribi
Summary: High-resolution (around 10-100m) surface soil moisture observations are important for various applications. This study adapted the (SMP)-M-2 algorithm to work at 1 km resolution and extended its application to herbaceous vegetation types. The algorithm combines Sentinel-1 and Sentinel-2 data and shows good agreement and accuracy compared to other datasets.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2023)
Article
Engineering, Electrical & Electronic
Saeideh Maleki, Nicolas Baghdadi, Cassio Fraga Dantas, Sami Najem, Hassan Bazzi, Nuria Pantaleoni Reluy, Dino Ienco, Mehrez Zribi
Summary: This study aims to improve the accuracy of rapeseed field detection using Sentinel-1 time series data and addressing ground sample collection challenges. Various solutions, including model transfer and limited training samples, are proposed and evaluated. Different algorithms and their performance in different scenarios are examined, with a focus on the impact of image count and phenological shift.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)