Article
Environmental Sciences
Amal Chakhar, David Hernandez-Lopez, Rocio Ballesteros, Miguel A. Moreno
Summary: This study assessed the potential of integrating Sentinel-1 and 2A data to perform crop classification and identified the most important input data for accurate results. The best performing scenario integrated VH and VV with NDVI using a cubic support vector machine (SVM) as the classifier.
Article
Environmental Sciences
Johannes Lohse, Anthony P. Doulgeris, Wolfgang Dierking
Summary: This study investigates the inclusion of Sentinel-1 texture features in a Bayesian classifier to improve the classification of sea ice types in SAR images. Results show that texture features play a crucial role in classification, especially in the generalized separation of ice and water, as well as the classification of young ice and multi-year ice, leading to significant improvements in classification accuracy.
Article
Agronomy
Mohammad Saadat, Seyd Teymoor Seydi, Mahdi Hasanlou, Saeid Homayouni
Summary: Rice plays a significant role in global food security and economic development. Traditional methods for estimating rice yield are often prone to errors and are time-consuming and costly. This study used satellite imagery and deep feature extraction methods to accurately map rice cultivation areas in Iran, leading to improvements in rice-type mapping efficiency.
Article
Agriculture, Multidisciplinary
Dairong Chen, Haoxuan Hu, Chunhua Liao, Junyan Ye, Wenhao Bao, Jinglin Mo, Yue Wu, Taifeng Dong, Hong Fan, Jie Pei
Summary: In this study, an ensemble-based data fusion framework was proposed and evaluated for constructing dense NDVI time series for crop monitoring. By predicting NDVI using Sentinel-1 SAR data and auxiliary environmental factors, the proposed method showed good accuracy and feasibility in filling data gaps in crop growth.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Environmental Sciences
Ran Pelta, Ofer Beeri, Rom Tarshish, Tal Shilo
Summary: The normalized difference vegetation index (NDVI) is an important parameter in precision agriculture. However, existing methods for estimating NDVI using Synthetic Aperture Radar (SAR) have limitations. This study proposes a new method, called Sentinel-1 to NDVIs for Agricultural Fields (SNAF), which utilizes a hyperlocal machine learning approach to convert Sentinel-1 data to NDVI values for agricultural fields. The method aims to overcome the limitations of existing methods and provide a continuous stream of NDVI values for agricultural decision making.
Article
Environmental Sciences
Arturo Villarroya-Carpio, Juan M. Lopez-Sanchez, Marcus E. Engdahl
Summary: This study explores the use of Sentinel-1 interferometric coherence data as a tool for crop monitoring. By analyzing time series of Sentinel-1 and 2 images acquired during 2017, it was found that coherence can serve as a good measure for monitoring the crop growing season, showing strong correlations with the NDVI.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Asier Uribeetxebarria, Ander Castellon, Ana Aizpurua
Summary: Accurately estimating wheat yield is essential for precision agriculture and crop management. This study combines Sentinel-1 and Sentinel-2 data with the CatBoost algorithm to predict wheat yield in 39 fields. The results show that the combination of S1 and S2 data with CatBoost algorithm can achieve high accuracy and reduce yield errors.
Article
Geosciences, Multidisciplinary
Angelica Tarpanelli, Alessandro C. Mondini, Stefania Camici
Summary: Inundation is a major natural hazard in Europe, but flood hazard and risk evaluation is complicated due to poor or unevenly distributed monitoring systems. The ESA Earth Observation Program's satellites, including Sentinels, have the potential to bridge this gap, but current mapping of flooded areas using Sentinel-1 and Sentinel-2 is often incomplete. This study evaluates the effectiveness of these satellites in systematically assessing floods in Europe, and finds that while Sentinel-1 can potentially observe 58% of flood events, Sentinel-2 is limited by cloud coverage and can only observe 28% of events.
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES
(2022)
Article
Biodiversity Conservation
Gengsheng Fang, Hao Xu, Sheng- Yang, Xiongwei Lou, Luming Fang
Summary: This study explored the potential of using different satellite sensors to predict forest canopy cover, diameter at breast height, and tree height. The results showed that the combination of S2 and L8 satellites or the addition of SAR data achieved the best accuracy in predicting these forest variables. Additionally, during the rainy season, shortwave infrared, near-infrared, and red-edge bands had the most significant implication for predicting these parameters.
ECOLOGICAL INDICATORS
(2023)
Article
Environmental Sciences
Felix Lobert, Ann-Kathrin Holtgrave, Marcel Schwieder, Marion Pause, Juliane Vogt, Alexander Gocht, Stefan Erasmi
Summary: The study evaluated the synergistic use of acquisitions from Sentinel-1, Sentinel-2, and Landsat 8 to detect the intensity of grassland management in terms of occurrence, frequency, and date of mowing events. The results showed that combining input features improved detection performance.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Geography, Physical
Juan Guerra-Hernandez, Lana L. Narine, Adrian Pascual, Eduardo Gonzalez-Ferreiro, Brigite Botequim, Lonesome Malambo, Amy Neuenschwander, Sorin C. Popescu, Sergio Godinho
Summary: This study used ICESat-2 satellite data combined with other data and models to estimate and map canopy height and aboveground biomass in Mediterranean forest areas. The results suggest that a multi-sensor approach may be used to extrapolate ICESat-2 derived estimates of aboveground biomass.
GISCIENCE & REMOTE SENSING
(2022)
Article
Environmental Sciences
Jian Li, Baozhang Chen
Summary: This study utilizes machine learning models to predict a global solar zenith angle suitable for the normalization of reflectance in 2018, and compares the performance of different models at a global scale and at three specific locations.
Article
Environmental Sciences
David Frantz, Franz Schug, Akpona Okujeni, Claudio Navacchi, Wolfgang Wagner, Sebastian van der Linden, Patrick Hostert
Summary: This study mapped building heights for entire Germany using Sentinel-1A/B and Sentinel-2A/B time series, achieving good results in training machine learning models. The synergistic combination of radar and optical models led to superior prediction results, and there were significant differences in average building heights across different regions in Germany.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Environmental Sciences
Andrea Monti-Guarnieri, Clement Albinet, Alessandro Cotrufo, Niccolo Franceschi, Marco Manzoni, Nuno Miranda, Riccardo Piantanida, Andrea Recchia
Summary: This paper presents a review and method for extracting actively sensed data and identifying Radio Frequency Interferences (RFI) in TOPSAR acquisition mode. By generating measurements of Earth Brightness Temperature (BT) and RFI equivalent temperature, and cross-comparing results from different sensors, precise SAR data denoising and RFI mitigation can be achieved.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Yusupujiang Aimaiti, Christina Sanon, Magaly Koch, Laurie G. Baise, Babak Moaveni
Summary: In this study, the performance of Sentinel-1 and Sentinel-2 data for building damage assessment in Kiev was assessed. The results showed that these data can be used for rapid mapping of damage and provide initial reference data immediately after a disaster.
Article
Environmental Sciences
Shuang Liang, Xiaofeng Li, Xingming Zheng, Tao Jiang, Xiaojie Li, Dejing Qiao
Article
Environmental Sciences
Xiancong Dong, Xiaojie Li, Xingming Zheng, Tao Jiang, Xiaofeng Li
Article
Agronomy
Xiuxue Chen, Xiaofeng Li, Lingjia Gu, Xingming Zheng, Guangrui Wang, Lei Li
Summary: This study examined the changes in snow-soil interface temperature (T-SS) and its main influencing factors in farmland of Northeast China over a 39-year period. It was found that both T-SS and the difference between T-SS and air temperature (T-DSSA) increased significantly, with mean snow depth (M-SD) identified as the most pivotal control factor.
Article
Chemistry, Analytical
Xiangkun Wan, Xiaofeng Li, Tao Jiang, Xingming Zheng, Xiaojie Li, Lei Li
Summary: This paper proposes a dual polling write method for secure digital cards triggered by a timer under a multitask framework, aimed at meeting the requirements of continuous data storage. Experimental results show that the new method significantly reduces time consumption for storage and programming steps, as well as the delay in the entire sampling cycle.
Article
Chemistry, Analytical
Yating Hu, Zhi Wang, Xiaofeng Li, Lei Li, Xigang Wang, Yanlin Wei
Summary: This study successfully established a method for classifying maize seeds using hyperspectral imaging and machine learning algorithms, which can effectively detect the degree of mildew and provide new ideas for quality assessment and selection of seeds.
Article
Environmental Sciences
Bingze Li, Ming Ma, Shengbo Chen, Xiaofeng Li, Si Chen, Xingming Zheng
Summary: Accurate monitoring of crop parameters is vital for predicting crop yield and inverting canopy parameters using remote sensing. This study proposes a new semi-empirical maize canopy model adapted for northeast China, which can predict the temporal dynamics of maize geometric and physical parameters. The results show that there is a strong correlation between leaf area index (LAI) and other parameters, and better performance is achieved using a regression method based on two-stage simulation. Furthermore, the model extension to large scales still maintains good accuracy.
Article
Environmental Sciences
Qianyi Gu, Yang Han, Yaping Xu, Huitian Ge, Xiaojie Li
Summary: Soil salinization is a major environmental problem affecting food security. This study demonstrates that deep learning methods can effectively extract and map the spatial distribution of saline land using remote sensing images. The addition of specific salinity indices improves the classification accuracy. Case studies conducted in Northeast China confirm the feasibility of the proposed method and highlight its significance for salinity modeling and agricultural land management practices.
Article
Environmental Sciences
Guang-Rui Wang, Xiao-Feng Li, Jian Wang, Yan-Lin Wei, Xing-Ming Zheng, Tao Jiang, Xiu-Xue Chen, Xiang-Kun Wan, Yan Wang
Summary: Satellite passive microwave remote sensing is used to estimate snow depth and snow water equivalent, but the presence of forests introduces uncertainties. A pixel-wise forest transmissivity estimation model is proposed in this study to improve the estimation accuracy of snow depth.
Article
Environmental Sciences
Xiaopeng Chen, Fang Gao, Yingye Li, Bin Wang, Xiaojie Li
Summary: The high spatial and temporal resolution of water body data is valuable for disaster monitoring and assessment. This paper proposes the water extraction methods of the multisource data fusion model (MDFM) and superpixel water extraction model (SWEM), which can overcome the limitations of optical remote sensing during floods and improve the accuracy and resolution of water extraction.
Article
Environmental Sciences
Xin Pan, Jun Xu, Jian Zhao, Xiaofeng Li
Summary: This article proposes a hierarchical object-focused and grid-based deep unsupervised segmentation method (HOFG) for high-resolution remote sensing images. By addressing the challenges associated with unsupervised deep neural networks, this method achieves better segmentation results through a lazy deep segmentation method and a hierarchical iterative segmentation strategy.
Article
Environmental Sciences
Shan Wang, Geng Cui, Xiaojie Li, Yan Liu, Xiaofeng Li, Shouzheng Tong, Mingye Zhang
Summary: This study investigates the groundwater storage anomaly (GWSA) in the black soil region of Northeast China using the GRACE satellite and GLDAS hydrological model. The results show that from 2002 to 2021, the overall GWSA decreased, with significant deficits in Heilongjiang, Jilin, and Liaoning Provinces. The study provides valuable insights into the spatial and temporal distribution of groundwater resources and their driving mechanisms in the black soil region, which can guide the conservation and sustainable utilization of groundwater resources.
