Article
Environmental Sciences
Ashish Joshi, Shefali Agrawal, Prakash Chauhan
Summary: In this paper, the geolocation accuracy improvement for NovaSAR-1 ground range imagery acquired through TLE orbit source has been achieved by generating orthorectified products using SAR Rigorous Sensor Model (RSM) algorithm and Ground control point (GCP). After orthorectification, the geolocation accuracy of the NovaSAR-1 imagery is reduced to less than 10 m.
GEOCARTO INTERNATIONAL
(2022)
Article
Environmental Sciences
Shadi Sadat Baghermanesh, Shabnam Jabari, Heather McGrath
Summary: Synthetic Aperture Radar (SAR) imagery is a crucial tool for flood mapping, especially in challenging urban environments. This study proposes a machine learning model that combines SAR simulated reflectivity maps, PolInSAR features, and five auxiliary features to improve flood mapping accuracy. The results show a significant improvement of 9.6% in overall classification accuracy using this approach.
Article
Environmental Sciences
Alena Dostalova, Mait Lang, Janis Ivanovs, Lars T. Waser, Wolfgang Wagner
Summary: The constellation of two Sentinel-1 satellites offers unprecedented SAR data coverage with high spatial and temporal resolution, showing potential for forest mapping and classification at a continental scale in Europe.
Article
Environmental Sciences
Nabil Bachagha, Wenbin Xu, Xingjun Luo, Nicola Masini, Mondher Brahmi, Xinyuan Wang, Fatma Souei, Rosa Lasaponora
Summary: The availability of high-resolution satellite synthetic aperture radar (SAR) data has attracted the attention of scientists and archeologists. This research explores the potential of using a novel method (nonlocal-SAR) to detect buried archeological remains in steep terrain. The study confirms the capability of SAR data to reveal unknown archeological sites.
Article
Environmental Sciences
Kerstin Brembach, Andrey Pleskachevsky, Hugues Lantuit
Summary: The Arctic is undergoing significant climate change, resulting in increased air temperatures and higher wave heights, which in turn have negative impacts on local economies and ecosystems. This study utilizes remote sensing data to overcome the limitations of existing wave height data in the Arctic and provides valuable insights into the spatial and temporal patterns of wave heights, as well as their links to local coastal processes.
Article
Environmental Sciences
Willeke A'Campo, Annett Bartsch, Achim Roth, Anna Wendleder, Victoria S. Martin, Luca Durstewitz, Rachele Lodi, Julia Wagner, Gustaf Hugelius
Summary: This study explores the potential of seasonal backscatter mechanisms in Arctic tundra environments for land cover classification using HH/HV TerraSAR-X (TSX) imagery. The results show that Kennaugh matrix element data can distinguish land cover classes with 92.4% accuracy, outperforming the Sigma Nought intensity data which had a 57.7% accuracy.
Article
Environmental Sciences
Mhamad El Hage, Ludovic Villard, Yue Huang, Laurent Ferro-Famil, Thierry Koleck, Thuy Le Toan, Laurent Polidori
Summary: This paper assesses the quality of a digital terrain model produced using tomographic processing of P-band SAR imagery. The results show the high potential of P-band SAR tomography in depicting topography under forests, despite some intrinsic limitations. These findings are valuable for the upcoming BIOMASS mission developed by ESA.
Article
Environmental Sciences
Ignacio Borlaf-Mena, Juan Garcia-Duro, Maurizio Santoro, Ludovic Villard, Ovidiu Badea, Mihai Andrei Tanase
Summary: Systematic Sentinel-1 acquisitions enable the detailed description of forest temporal dynamics, providing a powerful tool for phenological studies and forest type classification. This study incorporates the variation of scattering directionality into the classification models and successfully classifies temperate forests over mountainous terrain with high accuracy.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Environmental Sciences
Marta Pasternak, Kamila Pawluszek-Filipiak
Summary: This study compares various SAR-derived indices with in situ information to track the phenological development of different crops. The performance of different sensors and filtering methods is evaluated. The results show that different plant species have different responses to SAR indices and sensors, and time series filtering can increase the agreement between phenology development and SAR indices.
Article
Geochemistry & Geophysics
Akshay Patil, Gulab Singh, Christoph Rudiger, Shradha Mohanty, Sanjeev Kumar, Snehmani
Summary: This study proposes an algorithm for snow depth and snow water equivalent retrieval based on a polarimetric synthetic aperture radar (SAR) decomposition model and field measured data. The algorithm was validated through field campaigns in the Indian Himalaya, showing promising accuracy for both snow depth and snow water equivalent.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Engineering, Electrical & Electronic
Alberto Alonso-Gonzalez, Nuria Gimeno Martinez, Irena Hajnsek, Patricia Cifuentes Revenga, Maria Jose Gonzalez Bonilla, Christo Grigorov, Achim Roth, Ursula Marschalk, Nuria Casal Vazquez, Juan Manuel Cuerda, Marcos Garcia Rodriguez
Summary: This article analyzes the potential synergies between the two German satellites of the TSX/TDX mission and the Spanish PAZ satellite, showing that combining the acquisitions can improve image performance and revisit time. Field tests in different regions demonstrate that the satellite images from both missions are not compromised when combined.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Ling Chang, Anurag Kulshrestha, Bin Zhang, Xu Zhang
Summary: This study aims to extract meaningful attributes of radar scatterers from SAR images, specifically PAZ images, to enhance the understanding of SAR data and the interpretation of deformation processes. The study proposes a new scheme to extract geometric, physical, and land-use attributes of coherent radar scatterers using time series InSAR techniques. The scheme includes converting radar scatterers in HH and VV to a common reference system and utilizing a Random Forest classification method to categorize scatterers based on scattering mechanisms. The scheme is demonstrated with 30 Spanish PAZ SAR images, and the extracted attributes are analyzed for data and deformation interpretation.
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
Multidisciplinary Sciences
Viacheslav Komisarenko, Kaupo Voormansik, Radwa Elshawi, Sherif Sakr
Summary: This paper presents a method for detecting grassland mowing events using time series of Sentinel-1 and Sentinel-2 optical images through a deep learning model. The proposed model achieves high accuracy by using a rejection mechanism based on a threshold of prediction confidence.
SCIENTIFIC REPORTS
(2022)
Article
Environmental Studies
Janis Liepins, Andis Lazdins, Santa Kaleja, Kaspars Liepins
Summary: Different tree species have different contributions to the total biomass stock, so it is important to develop species-specific stand-level equations for estimating forest biomass and carbon stocks. Using data from the National Forest Inventory in Latvia, we investigated the relationship between growing stock volume and stand biomass density and found that models considering dominant species composition performed better than models with growing stock as the only variable.