Article
Environmental Sciences
Katharina Harfenmeister, Sibylle Itzerott, Cornelia Weltzien, Daniel Spengler
Summary: Monitoring the phenological development of winter wheat and winter barley using remote sensing features such as backscatter, polarimetric parameters, and NDVI shows sensitivity to specific stages. The approach demonstrates transferability across test sites and years, with differences mainly attributed to meteorological variations.
Article
Environmental Sciences
Raphael Quast, Wolfgang Wagner, Bernhard Bauer-Marschallinger, Mariette Vreugdenhil
Summary: This paper presents the retrieval of high-resolution soil moisture data from Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter measurements using a new bistatic radiative transfer modeling framework (RT1). The performance of the soil moisture retrievals is analyzed with respect to the ERA5-Land reanalysis dataset. The results demonstrate the potential of RT1 for the retrieval of high-resolution soil moisture data from SAR time series.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Geochemistry & Geophysics
Dong Fan, Tianjie Zhao, Xiaoguang Jiang, Huazhu Xue, Sitthisak Moukomla, Kittiwet Kuntiyawichai, Jiancheng Shi
Summary: In this study, a dual-temporal dual-channel (DTDC) algorithm was proposed to retrieve soil moisture using Sentinel-1 SAR data. By utilizing ancillary information from optical images and assuming constant surface roughness, the algorithm could solve for two consecutive soil moisture values and one roughness parameter simultaneously. The algorithm was tested on croplands in Northeast Thailand and demonstrated good performance in capturing temporal soil moisture changes and achieving similar patterns as a reference mission. This suggests that Sentinel-1 can be a suitable tool for agricultural water management.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Agronomy
Shoujia Ren, Bin Guo, Zhijun Wang, Juan Wang, Quanxiao Fang, Jianlin Wang
Summary: This study investigated the detection methods for soil moisture in winter wheat through field experiments, with findings showing that red-edge parameters are more sensitive to spectral changes and that NSRI performs well in detecting soil moisture.
AGRICULTURAL WATER MANAGEMENT
(2022)
Article
Agronomy
Dong Wu, Zhenhong Li, Yongchao Zhu, Xuan Li, Yingjie Wu, Shibo Fang
Summary: The study found that the USMEI effectively monitors agricultural drought in autumn and winter, while ESI and SMAPI perform better during the rest of the winter wheat growing season. The BSMEI is not suitable for monitoring droughts for winter wheat.
AGRICULTURAL WATER MANAGEMENT
(2021)
Article
Remote Sensing
Ning Li, Heping Li, Jianhui Zhao, Zhengwei Guo, Huijin Yang
Summary: This study proposes a winter wheat identification method combining Markov Random Field and Spectral Similarity Measure (MRF-SSM) using Sentinel-1A time-series images. The results show that the accuracy of mapping winter wheat using the MRF-SSM method is higher than using support vector machine and random forest methods, and it can accurately identify winter wheat near towns.
REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Natalia Efremova, Mohamed El Amine Seddik, Esra Erten
Summary: This study explores the possibility of using freely available Sentinel-1 and Sentinel-2 earth observation data for the simultaneous prediction of soil moisture content (SMC) using a cycle-consistent adversarial network (CycleGAN) for time-series gap filling. The proposed methodology learns the latent low-dimensional representation of satellite images and then builds a machine learning model on top of these representations to predict SMC. Experimental results show that the proposed method outperforms existing state-of-the-art methods for filling gaps in optical and synthetic-aperture radar (SAR) images.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Agronomy
Yunhao An, Zhe Gu, Xiyun Jiao, Qi Wei, Junzeng Xu, Kaihua Liu
Summary: Winter irrigation has an impact on soil N2O emissions, with emission peaks occurring two days after irrigation, and cumulative N2O emissions increasing with increased irrigation.
Article
Geography, Physical
Yi Xie, Shujing Shi, Lan Xun, Pengxin Wang
Summary: In this study, a winter wheat mapping index (WWMI) was constructed based on Sentinel-2 enhanced vegetation index (EVI) time series and wheat phenological features for automatic winter wheat mapping. The WWMI successfully differentiated winter wheat and nonwinter wheat areas, with the object-oriented approach performing better than the pixel-based approach.
GISCIENCE & REMOTE SENSING
(2023)
Article
Geography, Physical
Xianda Huang, Jianxi Huang, Xuecao Li, Qianrong Shen, Zhengchao Chen
Summary: Accurate mapping of winter wheat in its early stages is crucial for crop growth monitoring and yield forecasting. This study proposes a framework using spectral and temporal information of Sentinel-2 images for early season mapping of winter wheat. Results show that winter wheat can be mapped accurately in the early overwintering period, with overall accuracy comparable to post-season mapping.
GISCIENCE & REMOTE SENSING
(2022)
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
Tobias Ullmann, Thomas Jagdhuber, Dirk Hoffmeister, Simon Matthias May, Roland Baumhauer, Olaf Bubenzer
Summary: Recent research investigates the relationship between C-Band SAR backscatter and soil moisture (SM) in the hyper-arid environment of the Atacama Desert. The study finds a weak linear relationship between SM variations and SAR intensities for most stations in the Atacama, except for stations located in specific areas characterized by thick atmospheric dust deposits on top of subsurface cemented crusts. The study also reveals the presence of subsurface scattering effects in Sentinel-1C-Band data over large parts of the Atacama Desert.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Geography, Physical
Chunhua Liao, Jinfei Wang, Bo Shan, Jiali Shang, Taifeng Dong, Yongjun He
Summary: Near real-time (NRT) crop phenology detection and forecasting is crucial for precision agriculture. This study proposes a framework using Sentinel-2 time-series data to detect and forecast phenology for winter wheat and corn. The framework incorporates both canopy structure dynamics model (CSDM) and shape model-fitting approach. The results show that the framework can achieve accurate detection and forecasting with low RMSEs.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2023)
Article
Geosciences, Multidisciplinary
Sara Modanesi, Christian Massari, Alexander Gruber, Hans Lievens, Angelica Tarpanelli, Renato Morbidelli, Gabrielle J. M. De Lannoy
Summary: The global amount of water used for agricultural purposes is increasing, making the quantification of irrigation crucial. Studies are increasingly focusing on the synergistic use of models and satellite data to detect and quantify irrigation due to limited in situ observations. Combining large-scale land surface models (LSMs) with satellite information through data assimilation offers an optimal way to quantify irrigation water usage.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2021)
Article
Green & Sustainable Science & Technology
Xiaolei Wang, Mei Hou, Shouhai Shi, Zirong Hu, Chuanxin Yin, Lei Xu
Summary: This study proposes a rapid and robust phenology-based method to map winter wheat in a finer spatial resolution, using the Google Earth Engine. The method shows great applicability for mapping winter wheat and can provide early data preparation for winter wheat planting management.
Article
Environmental Sciences
Philip Marzahn, Swen Meyer
Article
Environmental Sciences
Thomas Weiss, Thomas Ramsauer, Alexander Loew, Philip Marzahn
Review
Environmental Sciences
Jian Peng, Clement Albergel, Anna Balenzano, Luca Brocca, Oliver Cartus, Michael H. Cosh, Wade T. Crow, Katarzyna Dabrowska-Zielinska, Simon Dadson, Malcolm W. J. Davidson, Patricia de Rosnay, Wouter Dorigo, Alexander Gruber, Stefan Hagemann, Martin Hirschi, Yann H. Kerr, Francesco Lovergine, Miguel D. Mahecha, Philip Marzahn, Francesco Mattia, Jan Pawel Musial, Swantje Preuschmann, Rolf H. Reichle, Giuseppe Satalino, Martyn Silgram, Peter M. Van Bodegom, Niko E. C. Verhoest, Wolfgang Wagner, Jeffrey P. Walker, Urs Wegmuller, Alexander Loew
Summary: Soil moisture observations from satellite data are important for various applications, and significant progress has been made in estimating soil moisture. However, there is a need for further development of high-resolution soil moisture products to meet the requirements of different disciplines.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Energy & Fuels
Amarachi Kalu, Janja Vrzel, Sebastian Kolb, Juergen Karl, Philip Marzahn, Fabian Pfaffenberger, Ralf Ludwig
Summary: Clean energy is crucial for sustainable environmental development, and understanding the environmental implications of alternative energy technologies is essential. The SustainableGAS project in Germany simulates various process chains for renewable energy substitution, following an interdisciplinary approach. This research focuses on environmental impacts, filling a knowledge gap in assessing the costs of alternative gas technologies.
Article
Remote Sensing
Yawei Wang, Pei Leng, Jian Peng, Philip Marzahn, Ralf Ludwig
Summary: This study comprehensively assessed two blended soil moisture products, Climate Change Initiative (CCI) and Soil Moisture Operational Product System (SMOPS), using reanalysis data and in-situ measurements. CCI showed overall better accuracy than SMOPS, but SMOPS could be a potential alternative in regions where CCI is not available due to better spatial coverage.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Environmental Sciences
Thomas Ramsauer, Thomas Weiss, Alexander Loew, Philip Marzahn
Summary: This study introduces an hourly index for high-resolution soil moisture estimation by extending the established Antecedent Precipitation Index with soil characteristics and temperature-dependent loss functions. The results show a promising improvement in soil moisture estimation accuracy, especially during soil moisture upsurge events. The study also demonstrates good agreement between the RADOLAN_API data set and the ESA CCI soil moisture product, with the resulting data set being made available as open access data.
Article
Environmental Sciences
Anna Balenzano, Francesco Mattia, Giuseppe Satalino, Francesco P. Lovergine, Davide Palmisano, Jian Peng, Philip Marzahn, Urs Wegmuller, Oliver Cartus, Katarzyna Dabrowska-Zielinska, Jan P. Musial, Malcolm W. J. Davidson, Valentijn R. N. Pauwels, Michael H. Cosh, Heather McNairn, Joel T. Johnson, Jeffrey P. Walker, Simon H. Yueh, Dara Entekhabi, Yann H. Kerr, Thomas J. Jackson
Summary: This study evaluates a pre-operational soil moisture product at 1 km resolution derived from Sentinel-1 radar satellite data. The retrieval algorithm relies on short term change detection using SAR imaging. Results show that for dense hydrological networks, the RMSE and correlation are around 0.06 m(3)/m(3) and 0.71, respectively, while in sparse networks, the RMSE increases by approximately 0.02 m(3)/m(3) (70% Confidence Level). Globally, the S-1 Theta product has an intrinsic RMSE of about 0.07 m(3)/m(3) and a correlation of 0.54.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Environmental Sciences
Birgitta Putzenlechner, Philip Marzahn, Philipp Koal, Arturo Sanchez-Azofeifa
Summary: This study investigated the use of remotely sensed FCOVER as a proxy for in situ FAPAR in a dense mixed-coniferous forest. The results showed that UAV-derived FCOVER was close to in situ FAPAR during the peak vegetation period, while the Sentinel-2 FCOVER product underestimated both. The study recommends integrating the spatial variability of UAV-derived FCOVER into quality assessments and using it to benchmark sampling sizes for in situ FAPAR measurements.