Article
Environmental Sciences
Marcelle Lock, Neil Saintilan, Iris van Duren, Andrew Skidmore
Summary: The Australian New South Wales Estuary health assessment and biodiversity monitoring program has set state-wide targets for estuary health. In this study, the use of remote sensing derived data for monitoring water quality indicators in selected lakes along the coast was investigated. The results showed that the remote sensing products were partly successful in predicting chlorophyll a concentration and water clarity, but varied across years and lakes. It is likely that the physical differences between the systems influence the algorithm's output, suggesting the need for a tailored monitoring approach.
Article
Environmental Sciences
Katja Kowalski, Akpona Okujeni, Patrick Hostert
Summary: In this study, a generalized drought monitoring framework for Central European grasslands was developed by combining Sentinel-2 data, field survey information, and spectral unmixing. The study accurately estimated the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and soil using a spectral library and multi-temporal Sentinel-2 data. The grassland-specific Normalized Difference Fraction Index (NDFI) was calculated based on the time series data, revealing widespread drought impacts on Central European grasslands during the persistent drought period from 2018 to 2020. The study highlights the value of integrating Sentinel-2 data, field survey information, and spectral unmixing for drought monitoring across grassland gradients in Central Europe.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Environmental Sciences
Andres Echeverria, Alejandro Urmeneta, Maria Gonzalez-Audicana, Esther M. Gonzalez
Summary: This study assessed the utility of Sentinel-2 images in monitoring rainfed alfalfa vegetation cover in semiarid areas like Bardenas Reales in Spain. Results showed a high correlation between NDVI and FVC at the parcel level, while the correlation at the pixel level remained moderate. The findings suggest that Sentinel-2 imagery is a suitable tool for monitoring rainfed alfalfa pastures in semiarid areas, potentially increasing pasture management success.
Article
Environmental Sciences
Chunfeng Ma, Kasper Johansen, Matthew F. McCabe
Summary: This paper combines high resolution Sentinel-1 SAR and Sentinel-2 optical imagery to monitor irrigation events and crop dynamics in a dryland agricultural landscape. By analyzing the responses of simulated backscatters to soil moisture, NDVI and NDWI, and conducting correlation analysis using Sentinel-1 SAR and Sentinel-2 optical data, an appropriate SAR-based vegetation descriptor was identified.
Article
Environmental Sciences
Muhammad Moshiur Rahman, Andrew Robson, James Brinkhoff
Summary: Accurate monitoring of avocado crop phenology is crucial for optimizing farm management, improving productivity, and evaluating resilience to extreme weather and climate change. This study used remote sensing data to monitor avocado orchards in Australia from 2017 to 2021 and found significant differences between different phenological stages, as well as seasonal trends.
Article
Environmental Sciences
Kristen L. Wilson, Melisa C. Wong, Emmanuel Devred
Summary: Satellite remote sensing is a valuable tool for mapping and monitoring the distribution of marine macrophytes. This study compared the performance of WorldView-3 and Sentinel-2 satellites in mapping bottom habitat, and explored the impact of tidal height and image collection dates on classification results.
Article
Agronomy
Dimitrios Tassopoulos, Dionissios Kalivas, Rigas Giovos, Nestor Lougkos, Anastasia Priovolou
Summary: This study successfully monitored vine growth in a PDO zone using Sentinel-2 satellite data, identifying differences in VIs across subzones with different climatic conditions and exploring the effectiveness of Sentinel-2 data in management applications.
Article
Environmental Sciences
Anatol Garioud, Silvia Valero, Sebastien Giordano, Clement Mallet
Summary: SenRVM is a new multi-sensor approach to regress SAR time series towards NDVI, utilizing a deep Recurrent Neural Network architecture to provide accurate results in optical temporal resolution.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Biodiversity Conservation
Yingcong Wang, Zhaoning Gong, Han Zhou
Summary: This study used Landsat TM/ETM+/OLI and Sentinel-2 remote sensing imagery datasets to explore the phenological characteristics of submerged aquatic vegetation (SAV) and construct a phenological map of SAV groups in Baiyangdian Lake. The long-term spatial distribution characteristics and trends of SAV from 1986 to 2021 were analyzed. The results showed that HANTS analysis eliminated abnormal observations and noise, and the NDVI time series curves accurately reflected the phenological characteristics of SAV.
ECOLOGICAL INDICATORS
(2023)
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
Environmental Sciences
Mingjie Liao, Rui Zhang, Jichao Lv, Bin Yu, Jiatai Pang, Ran Li, Wei Xiang, Wei Tao
Summary: By using an improved TS-InSAR method, the study conducted comprehensive research on ground subsidence in Yan'an New District. The area was relatively stable overall, but three significant subsidence funnels were identified. These funnels expanded and accelerated in subsidence rates over the years.
Article
Biotechnology & Applied Microbiology
Wuheng Yang, Jinming Sha, Zhongcong Bao, Jinwei Dong, Xiaomei Li, Eshetu Shifaw, Jing Tan, Terefe Hanchiso Sodango
Summary: Tidal flats are crucial habitats for a variety of wildlife, and monitoring their boundaries is important for biodiversity and coastal sustainability. However, the use of optical data for monitoring tidal flats is limited due to cloud cover and foggy weather, suggesting that integrating radar data can improve monitoring efficiency.
ENVIRONMENTAL TECHNOLOGY & INNOVATION
(2021)
Article
Engineering, Geological
Dingwen Zhang, Wentao Yang, Chong Xu, Tao Ye, Qiwei Liu
Summary: A new method is proposed in this study to extract deforming landslides from background noise using time-series Sentinel-2 images. The method was tested along a section of the Jinsha River in southwest China and was found to be effective in eliminating background noise and isolating deforming landslides.
Article
Computer Science, Information Systems
Javier Marcello, Francisco Eugenio, Consuelo Gonzalo-Martin, Dionisio Rodriguez-Esparragon, Ferran Marques
Summary: This study utilizes remote sensing technology for the sustainable management of coastal and mountain ecosystems, utilizing a multiplatform approach to process various high spatial resolution images and identifying the effectiveness of remote sensing in accurately mapping the parks.
Article
Environmental Sciences
Katja Kowalski, Akpona Okujeni, Maximilian Brell, Patrick Hostert
Summary: Severe droughts had unprecedented impacts on grasslands in Central Europe in 2018 and 2019. The Sentinel-2 time series has untapped potential for improving grassland monitoring during droughts. Different soil types of grasslands showed varying degrees of drought impacts.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Ren-Min Yang, Wen-Wen Guo
LAND DEGRADATION & DEVELOPMENT
(2018)
Article
Soil Science
R. -M. Yang, B. Minasny, Y. -X. Ma, D. Field, A. McBratney, C. -F. Wu
SOIL USE AND MANAGEMENT
(2018)
Article
Geosciences, Multidisciplinary
Ren-Min Yang, Wen-Wen Guo, Jing-Biao Zheng
Article
Engineering, Electrical & Electronic
Ren-Min Yang, Wen-Wen Guo
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2019)
Article
Environmental Sciences
Ren-Min Yang
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES
(2019)
Article
Environmental Sciences
Ren-Min Yang
SCIENCE OF THE TOTAL ENVIRONMENT
(2019)
Article
Geography, Physical
Fei Yang, Gan-Lin Zhang, Daniela Sauer, Fan Yang, Ren-Min Yang, Feng Liu, Xiao-Dong Song, Yu-Guo Zhao, De-Cheng Li, Jin-Ling Yang
Article
Soil Science
Ren-Min Yang, Fan Yang
SOIL SCIENCE SOCIETY OF AMERICA JOURNAL
(2020)
Article
Environmental Sciences
Ren-Min Yang, Liu-Mei Chen
Summary: Empirical study on the impact of invasive Spartina alterniflora on soil bulk density revealed the mechanisms involving soil organic carbon, salinity, and sand content. Invasion of S. alterniflora leads to a decrease in soil bulk density, highlighting the variability within the interacting system of soil and plants.
LAND DEGRADATION & DEVELOPMENT
(2021)
Article
Environmental Sciences
Li-An Liu, Ren-Min Yang, Xin Zhang, Chang-Ming Zhu, Zhong-Qi Zhang
Summary: The study utilized different methods to obtain soil data for modeling, revealing a significant correlation between the invasion process and soil quality index, with invasion having a direct positive effect on soil quality, while vegetation had a negative impact on soil quality in the top layer.
Article
Environmental Sciences
Ren-Min Yang, Liang-Jie Wang, Liu-Mei Chen, Zhong-Qi Zhang
Summary: This study successfully developed a predictive model for explaining and predicting soil bulk density variation using a causal-based partial least squares structural equation modeling approach. The model was validated in a coastal wetland in eastern China and showed significant direct effects of nutrient cycling, plant invasion, and depth dependence on bulk density. The findings highlighted the practical usefulness of the model in improving predictive accuracy and understanding soil variation at a system level.
LAND DEGRADATION & DEVELOPMENT
(2021)
Article
Soil Science
Ren-Min Yang, Chang-Ming Zhu, Xin Zhang, Lai-Ming Huang
Summary: This study demonstrates the use of the space-for-time substitution method to predict soil organic C change over time in data-poor areas. The results show that the accuracy of the predictions obtained with this method is comparable to that of predictions obtained with temporal models.
SOIL SCIENCE SOCIETY OF AMERICA JOURNAL
(2022)