Article
Environmental Sciences
Maurizio Santoro, Oliver Cartus, Johan E. S. Fransson
Summary: This article presents a study on the estimation and tracking of carbon density in Swedish forests using satellite L-band observations. The study found that while there were substantial uncertainties at the pixel level, the average values at landscape and county scale were consistent with existing data, and Swedish forests acted as a carbon sink between 2010 and 2015.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Mark L. Williams, Anthea L. Mitchell, Anthony K. Milne, Tim Danaher, Geoff Horn
Summary: This study utilizes L-band synthetic aperture radar (SAR) data to estimate vegetation indicators. It proposes a method to reduce the influence of external factors by correcting terrain slope and cross-track tendencies, and normalizing backscatter differences using linear least squares difference minimization. The method is applied in New South Wales, Australia, and demonstrates improved estimation of vegetation and provides spatially explicit forest structural information.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Sang-Eun Park, Yoon Taek Jung, Hyun-Cheol Kim
Summary: This study explores the possibility of using combined interpretation of optical and SAR data to identify and understand the spatiotemporal changes in the permafrost active layer. The results show a significant correlation between winter changes observed in SAR data and summer land cover changes observed in optical data. Additional data from independent sources also support this relationship.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Helen Blue Parache, Timothy Mayer, Kelsey E. Herndon, Africa Ixmucane Flores-Anderson, Yang Lei, Quyen Nguyen, Thannarot Kunlamai, Robert Griffin
Summary: A study using ALOS PALSAR satellite data for forest height estimation showed comparable results to previous research. Despite limitations in data quality and quantity, the use case in Savannakhet, Lao demonstrated the applicability of these techniques for estimating FSH in tropical forests.
Article
Environmental Sciences
Shinki Cho, Haoyi Xiu, Masashi Matsuoka
Summary: Most research on earthquake-caused building damage extraction using SAR images rely on inaccurate assessment methods. This study proposes a more detailed classification of Major damage buildings based on Japanese assessment data and field photographs. The backscattering characteristics of SAR images were analyzed for each damage class, and it was found that the correlation coefficient R decreased for large deformations such as collapsed buildings, while the coherence differential value gamma(dif) was sensitive to not only collapsed buildings but also damage with relatively small deformation. The study also suggests that ground displacement near the earthquake fault affected the coherence values.
Article
Environmental Sciences
Hironori Arai, Thuy Le Toan, Wataru Takeuchi, Kei Oyoshi, Tamon Fumoto, Kazuyuki Inubushi
Summary: In this study, a new multiscale data assimilation technique is developed using ALOS-2 PALSAR-2 L-band SAR data to estimate the spatiotemporal dynamics of field water levels. The results show promising applications for monitoring paddy field water levels and informing irrigation practices.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Konrad Wessels, Xiaoxuan Li, Alexandre Bouvet, Renaud Mathieu, Russell Main, Laven Naidoo, Barend Erasmus, Gregory P. Asner
Summary: Global savannas, as the third largest carbon sink, are facing rapid changes. This study tested the ability of L-band SAR to track changes in savanna vegetation structure and found that it has a higher sensitivity than previous studies suggested.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Environmental Sciences
Maurizio Santoro, Oliver Cartus, Johan E. S. Fransson
Summary: This study revisited the Water Cloud Model (WCM) for estimating forest biomass-related variables, aiming to reduce systematic retrieval errors associated with empirical assumptions in the model by exploring physically-based, Light Detection and Ranging (LiDAR)-aided, model parameterization at a larger scale. The integration of allometries in the WCM effectively reduced estimation errors, demonstrating the potential for providing large-scale estimates of biomass-related variables using L-band backscatter observations.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Forestry
Barbara Zimbres, Pedro Rodriguez-Veiga, Julia Z. Shimbo, Polyanna da Conceicao Bispo, Heiko Balzter, Mercedes Bustamante, Iris Roitman, Ricardo Haidar, Sabrina Miranda, Leticia Gomes, Fabricio Alvim Carvalho, Eddie Lenza, Leonardo Maracahipes-Santos, Ana Clara Abadia, Jamir Afonso do Prado Junior, Evandro Luiz Mendonca Machado, Anne Priscila Dias Gonzaga, Marcela de Castro Nunes Santos Terra, Jose Marcio de Mello, Jose Roberto Soares Scolforo, Jose Roberto Rodrigues Pinto, Ane Alencar
Summary: A study was conducted to build an aboveground woody biomass model for the Brazilian Cerrado biome using optical and SAR imagery, with Random Forest algorithm showing slightly better results compared to the Classification and Regression Tree model. However, the models underestimated very high aboveground woody biomass and slightly overestimated low biomass.
FOREST ECOLOGY AND MANAGEMENT
(2021)
Article
Environmental Sciences
Mohamad Hamze, Nicolas Baghdadi, Marcel M. El Hajj, Mehrez Zribi, Hassan Bazzi, Bruno Cheviron, Ghaleb Faour
Summary: This paper aims to develop an operational approach for surface soil moisture (SSM) estimation using both C-band and L-band data, utilizing artificial neural networks (NNs) technique to enhance estimation accuracy. By incorporating L-band-derived soil roughness (Hrms) with C-band SAR data in NNs model, the error in SSM estimation can be effectively reduced. Training and validation using synthetic and real databases show the effectiveness of this approach.
Article
Ecology
Unmesh Khati, Gulab Singh
Summary: This study explores the potential of combining backscatter with polarimetric SAR interferometry (PolInSAR) estimated forest stand height for improved above-ground biomass (AGB) estimation. The results demonstrate the potential of this synergistic combination for AGB mapping over a tropical forest range in India.
FRONTIERS IN FORESTS AND GLOBAL CHANGE
(2022)
Article
Geochemistry & Geophysics
Cheng Wang, Wu-Long Guo, Qing-He Zhang, Hai-Sheng Zhao, Le Cao
Summary: This article proposes a bi-iteration algorithm that integrates GPS, PALSAR, and ionosonde data to improve the precision of ionospheric tomography. Experimental verification demonstrates that the reconstruction accuracy of the algorithm is significantly higher compared to using GPS alone or combining GPS and ionosonde data, indicating the effectiveness of combining these three kinds of data in improving the precision of CIT.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Kersten Schmidt, Marco Schwerdt, Guillaume Hajduch, Pauline Vincent, Andrea Recchia, Muriel Pinheiro
Summary: SAR data products for Sentinel-1 have been freely available since 2014, and this paper introduces a method for correcting changes due to updates without reprocessing SAR data products. The method was applied to data acquisitions at the DLR calibration site and showed improved radiometric accuracy for almost five years.
Article
Geochemistry & Geophysics
Juha Lemmetyinen, Jorge Jorge Ruiz, Juval Cohen, Jouko Haapamaa, Anna Kontu, Jouni Pulliainen, J. Praks
Summary: This study investigated the attenuation of radar signals in boreal forests during winter using a multifrequency ground-based synthetic aperture radar (GB-SAR). The results showed that the ambient temperature had a significant impact on the attenuation, with the maximum attenuation observed at temperatures below 0 degrees Celsius. The presence of snow on the canopy also increased the attenuation, but had negligible effects on vegetation backscatter.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Paul C. Vermunt, Saeed Khabbazan, Susan C. Steele-Dunne, Jasmeet Judge, Alejandro Monsivais-Huertero, Leila Guerriero, Pang-Wei Liu
Summary: The study aimed to demonstrate the potential value of subdaily spaceborne radar for monitoring vegetation water dynamics. The results showed that backscatter was sensitive to both transient rainfall interception events, and slower daily cycles of internal canopy water and dew, demonstrating a potentially valuable application for the next generation of spaceborne radar missions.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Adugna G. Mullissa, Daniele Perissin, Valentyn A. Tolpekin, Alfred Stein
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2018)
Article
Remote Sensing
Nitin Bhatia, Alfred Stein, Ils Reusen, Valentyn A. Tolpekin
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2018)
Article
Environmental Sciences
Alfred Stein, Valentyn A. Tolpekin
NATURAL RESOURCE MODELING
(2018)
Article
Environmental Sciences
Nitin Bhatia, Marian-Daniel Iordache, Alfred Stein, Ils Reusen, Valentyn A. Tolpekin
REMOTE SENSING OF ENVIRONMENT
(2018)
Article
Environmental Sciences
Nitin Bhatia, Valentyn A. Tolpekin, Alfred Stein, Ils Reusen
Article
Environmental Sciences
Nafiseh Ghasemi, Valentyn Tolpekin, Alfred Stein
Article
Engineering, Electrical & Electronic
Nafiseh Ghasemi, Valentyn A. Tolpekin, Alfred Stein
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2018)
Article
Remote Sensing
Mengmeng Li, Wietske Bijker
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2019)
Article
Environmental Sciences
E. Goumehei, V. Tolpekin, A. Stein, W. Yan
WATER RESOURCES RESEARCH
(2019)
Article
Engineering, Electrical & Electronic
Kiledar S. Tomar, Shashi Kumar, Valentyn A. Tolpekin
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2019)
Article
Environmental Sciences
Khairiya Mudrik Masoud, Claudio Persello, Valentyn A. Tolpekin
Article
Remote Sensing
Mariana Belgiu, Wietske Bijker, Ovidiu Csillik, Alfred Stein
Summary: Crop type mapping is important for food security applications, and supervised classification methods are commonly used for generating data from satellite images. Various solutions like transfer learning, temporal-spectral signatures, re-utilization of inventories, and crowdsourcing are applied to generate samples for coarser classifications, but rarely for generating crop type samples. This study proposes a method that leverages phenology information to automatically generate crop samples, showing promising results for classes with reduced inter-class similarity. However, the method may not perform as well for crops with high inter-class similarity, particularly in regions with imbalanced crop samples. Despite its shortcomings, the proposed methodology offers a viable option for generating crop samples in regions with limited ground labels.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Geography, Physical
Getachew Workineh Gella, Wietske Bijker, Mariana Belgiu
Summary: This study successfully mapped crop types in fragmented agricultural landscapes using a combination of Synthetic Aperture Radar (SAR) and crop phenological information with different Dynamic Time Warping implementation strategies, demonstrating the potential for crop type mapping in smallholder farming areas.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2021)
Article
Remote Sensing
Wisdom Simataa Moola, Wietske Bijker, Mariana Belgiu, Mengmeng Li
Summary: This study developed a fuzzy classifier based on TWDTW distances to map vegetable types from Sentinel-1A SAR image time series. By calculating fuzzy memberships for each pixel, assessing classification uncertainty, and applying thresholds during defuzzification, the classification accuracy of the image was improved.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Environmental Sciences
Ratna Sari Dewi, Wietske Bijker
EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES
(2020)
