Article
Environmental Sciences
Yun Yang, Martha Anderson, Feng Gao, Jie Xue, Kyle Knipper, Christopher Hain
Summary: Evapotranspiration is important for crop health and water resource management. Fusing satellite data from multiple satellites can provide more continuous estimation of daily ET at field scale. In this study, the ET fusion modeling system was applied to retrieve ET using Landsat and MODIS data, and the results showed improvement in estimating daily field scale ET for all major crop types in the study area compared to the standard method.
Article
Geochemistry & Geophysics
Zekun Yang, Xi Chen, Yaokui Cui, Feng Lv, Zhaoyuan Yao, Sien Li, Lifeng Wu, Junliang Fan, Xiaozhuang Geng, Wenjie Fan
Summary: This study proposes an improved double instrumental variable method to fuse active and passive soil moisture (SM) products, which combines the best instrumental variables in time series and estimates fused weights to obtain SM products with higher change capture ability and accuracy. The results show that the fused SM products have improved correlations and accuracy by about 10% on average compared to the original products, and by more than 30% on average compared to the original method.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Chemistry, Analytical
Aaron Judah, Baoxin Hu
Summary: This research aimed to improve wetland classification by utilizing multi-source remotely sensed data and utilizing random forest classification and Dempster-Shafer theory. The proposed method achieved a significant improvement in classification accuracy and successfully identified important features in compound wetland categories.
Article
Computer Science, Information Systems
Zhifeng Zhang, Xiao Cui, Xiaohui Ji
Summary: The study introduces a model for multi-target classification of remotely sensed imagery using a hash method, combining low delay and low storage hash method with a dissociation perspective invariant model to improve classification accuracy.
Article
Geochemistry & Geophysics
Li Wang, Yanjiang Wang, Yaqian Zhao, Baodi Liu
Summary: The proposed Enhanced Residual Neural Network (ERNet) aims to improve classification performance on remote sensing images by addressing issues such as insufficient learning of discriminative information in early layers, overfitting due to limited labeling data, and limitations of transfer learning alone. By modifying the network architecture and incorporating dropout layers, ERNet achieves superior results compared to baseline methods in remote sensing image recognition tasks.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2021)
Article
Environmental Sciences
Daniel Fernandez, Eromanga Adermann, Marco Pizzolato, Roman Pechenkin, Christina G. G. Rodriguez, Alireza Taravat
Summary: In recent years, remote-sensing based methods have been increasingly used to assess soil erosion, thanks to the availability of freely accessible satellite data. Applying these techniques to the Arctic areas, however, presents challenges due to the region's unique features. This study compares three commonly used classification algorithms to model soil erosion, using ground truth samples from Iceland.
Article
Geochemistry & Geophysics
Guoqing Zhou, Weiguang Liu, Qiang Zhu, Yanling Lu, Yu Liu
Summary: In recent years, models based on fully convolutional neural networks have been proposed to improve accuracy but ignored computational efficiency. This research presents an innovative deep learning model, ECA-MobileNetV3(large)+SegNet, which simultaneously considers both aspects. By modifying the encoder and decoder structures, the proposed model achieves significant improvement in performance and reduces the number of parameters.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geography, Physical
Lei Deng, Jie Sun, Yong Chen, Han Lu, Fuzhou Duan, Lin Zhu, Tianxing Fan
Summary: In this study, a more suitable multispectral to hyperspectral network (M2H-Net) is proposed for remote sensing applications, which can efficiently reconstruct hyperspectral images within a wider spectral range. It demonstrates high accuracy and stability in image reconstruction.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Hok Sum Fok, Yutong Chen, Lei Wang, Robert Tenzer, Qing He
Summary: A new estimate of the monthly Mekong Basin runoff using remote sensing technology showed improved consistency with in situ monitoring data. The choice of evapotranspiration data products significantly influenced the accuracy of the estimated runoff, highlighting the importance of selecting appropriate data products for remote sensing runoff estimation.
Article
Chemistry, Multidisciplinary
Ling Dai, Guangyun Zhang, Jinqi Gong, Rongting Zhang
Summary: This paper proposes a data-driven method for hyperspectral remotely sensed data, which can autonomously extract key features and interactively learn feature indexes, providing a more flexible and creative framework compared to traditional methods.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Interdisciplinary Applications
Dilek Kucuk Matci, Ugur Avdan
Summary: This study proposes a method for automatically labeling images without a training phase. By using bands in the image and Corine data, a database is created by examining spectral characteristics of land classes from sample images. The unlabelled classes are evaluated using this database, and the relevant label is assigned. The developed approach is tested in several regions in Turkey and Greece and achieves high accuracy.
EARTH SCIENCE INFORMATICS
(2022)
Article
Environmental Sciences
Dekker Ehlers, Chao Wang, John Coulston, Yulong Zhang, Tamlin Pavelsky, Elizabeth Frankenberg, Curtis Woodcock, Conghe Song
Summary: The majority of the aboveground biomass on the Earth's land surface is stored in forests. However, accurate estimation of forest aboveground biomass (FAGB) remains challenging. This study proposed a new conceptual model using remotely sensed data to map FAGB. The model includes height metrics as the most important variables for estimating FAGB.
Article
Green & Sustainable Science & Technology
Sangchul Lee, Junyu Qi, Hyunglok Kim, Gregory W. McCarty, Glenn E. Moglen, Martha Anderson, Xuesong Zhang, Ling Du
Summary: This study compared two versions of the Soil and Water Assessment Tool (SWAT) by calibrating them against streamflow and remotely sensed evapotranspiration (RS-ET) products. At the watershed level, both SWAT and RSWAT showed similar performance metrics for daily streamflow and ET, while at the subwatershed level, RSWAT demonstrated higher KGE values for daily ET. These findings suggest that RS-ET has the potential to improve prediction accuracy from model structural improvements and highlight the usefulness of remotely sensed data in hydrologic modeling.
Article
Green & Sustainable Science & Technology
Liang Guo, Xiaohuan Xi, Weijun Yang, Lei Liang
Summary: The study revealed a continuous increase in built-up area and a decrease in vegetation area in Guangzhou City, China from 1986 to 2018. There was a strong positive correlation between GDP and built-up area, while a strong negative correlation was found between GDP and vegetation area, suggesting that the expansion of built-up area came at the expense of reduced vegetation area.
Article
Soil Science
Dave O'Leary, Colin Brown, Mark G. Healy, Shane Regan, Eve Daly
Summary: This paper presents a method that integrates multi-band remote sensing data to comprehensively interpret the intra-peatland variation of key restoration indicators. The study provides a framework for high spatial and temporal resolution monitoring of peatland restoration using future drone-based platforms.