Article
Environmental Sciences
Yu Zhao, Shaoyu Han, Yang Meng, Haikuan Feng, Zhenhai Li, Jingli Chen, Xiaoyu Song, Yan Zhu, Guijun Yang
Summary: The novel hybrid method combining Crop Biomass Algorithm of Wheat (CBA-Wheat) and Simple Algorithm For Yield (SAFY) with transfer learning improves the accuracy and efficiency of winter wheat yield estimation.
Article
Environmental Sciences
Xingshuo Peng, Wenting Han, Jianyi Ao, Yi Wang
Summary: This study developed a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. The method showed high accuracy in estimating maize yield under different water treatments, indicating the potential of incorporating UAV observations with crop data to monitor crop yield.
Article
Environmental Sciences
Manel Khlif, Maria Jose Escorihuela, Aicha Chahbi Bellakanji, Giovanni Paolini, Zohra Lili Chabaane
Summary: This study aimed to analyze different drought indices for identifying drought periods and predicting cereal yield in two semi-arid regions, Lleida in Catalonia and Kairouan in Tunisia. Four indices were calculated from remote sensing data: Soil Moisture Anomaly Index (SMAI), Vegetation Anomaly Index (VAI), Evapotranspiration Anomaly Index (EAI), and Inverse Temperature Anomaly Index (ITAI). Correlation studies between indices and wheat/barley yields were conducted, and the EAI and SMAI were found to be key indicators for yield estimation and early estimation, respectively.
Article
Environmental Sciences
V. S. Manivasagam, Yuval Sadeh, Gregoriy Kaplan, David J. Bonfil, Offer Rozenstein
Summary: Spatial information embedded in a crop model, such as Leaf Area Index (LAI), can improve yield prediction. This study evaluated the assimilation of high-resolution satellite imagery-derived LAI into a crop model, SAFY, to assess within-field crop yield, showing improved accuracy with PlanetScope and Sentinel-2 fused images. The potential to capture within-field yield variations using high-resolution imagery was demonstrated.
Article
Agronomy
John W. Piltz, Craig A. Rodham, John F. Wilkins, Belinda F. Hackney
Summary: Experiments conducted in southern New South Wales, Australia, evaluated agronomic and quality parameters of various cereal crops grown alone or in combination with Vicia benghalensis L. Yield varied between years due to rainfall, with Vicia inclusion increasing crude protein content but potentially causing lodging.
Article
Agronomy
P. Debaeke, F. Attia, L. Champolivier, J-F Dejoux, A. Micheneau, A. Al Bitar, R. Trepos
Summary: This study combined remote sensing data and statistical modeling to forecast sunflower yield in southwestern France. The results showed that models including green area duration and maximum green area index were more accurate in predicting individual-level yield, while models based solely on maximum green area index were less accurate. The predictions were most accurate in 2014, followed by 2015 and 2016. At the grain catchment area level, models including green area duration were the most accurate.
EUROPEAN JOURNAL OF AGRONOMY
(2023)
Article
Geochemistry & Geophysics
Tianjun Shi, Jinnan Gong, Shikai Jiang, Xiyang Zhi, Guangzhen Bao, Yu Sun, Wei Zhang
Summary: Aircraft detection in remote-sensing images is important in both military and civilian applications. However, current datasets lack diversity, making it challenging to train robust detectors. Therefore, we manually label and construct a complex optical remote-sensing aircraft target detection dataset from various sources. The dataset contains diverse scenes and sufficient instances, supporting the training and evaluation of data-driven algorithms. We also train and evaluate multiple detectors based on this dataset to provide a benchmark for aircraft detection techniques.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Dong Han, Pengxin Wang, Kevin Tansey, Shuyu Zhang, Huiren Tian, Yue Zhang, Hongmei Li
Summary: The proposed SAFY-V model, integrating VTCI, can better estimate winter wheat yields, especially in arid areas, thereby improving estimation accuracy.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Animesh Chandra Das, Ryozo Noguchi, Tofael Ahamed
Summary: This research utilized remote sensing technique with the standardized precipitation index (SPI) to determine drought stress in tea estates, revealing a strong relationship between soil moisture and plant canopy moisture. The study found that drought frequency significantly impacted tea yield in the estates, suggesting that satellite remote sensing with SPI could be a valuable tool for measuring drought stress in tea estates.
Review
Green & Sustainable Science & Technology
Rongkun Zhao, Yuechen Li, Mingguo Ma
Summary: Paddy rice is a staple food for three billion people worldwide, and there are various mapping methods available for estimating paddy rice planting area and yield. The best methods include multisource data integration, machine learning, and radar mapping.
Article
Environmental Sciences
Bin Yang, Wanxue Zhu, Ehsan Eyshi Rezaei, Jing Li, Zhigang Sun, Junqiang Zhang
Summary: Unmanned aerial vehicles (UAV)-based multispectral remote sensing, combined with multi-temporal data, significantly improves the monitoring and prediction of crop yield accuracy in agro-ecosystems. Specific developmental stages of crops, such as tasseling, silking, milking, and dough stages, are critical for achieving the highest yield prediction accuracy. Additionally, certain spectral indices, such as NDRE and GNDVI, are crucial features for maize yield prediction.
Article
Geochemistry & Geophysics
Zhenyu Cui, Jiaxu Leng, Ying Liu, Tianlin Zhang, Pei Quan, Wei Zhao
Summary: This article proposes a novel anchor-free rotated ship detection framework, SKNet, which effectively addresses the challenges of detecting rotated ships in optical remote sensing images. Through extensive experiments on three datasets, SKNet achieves state-of-the-art detection performance while being time-efficient, demonstrating the best speed-accuracy tradeoff.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Qiming Qin, Zihua Wu, Tianyuan Zhang, Vasit Sagan, Zhaoxu Zhang, Yao Zhang, Chengye Zhang, Huazhong Ren, Yuanheng Sun, Wei Xu, Cong Zhao
Summary: This paper discusses the use of optical and thermal remote sensing in agricultural drought monitoring, with a focus on four categories of methods and the potential of solar-induced chlorophyll fluorescence in early drought detection. Future directions in agricultural drought monitoring include improving early detection, enhancing spatio-temporal resolution, integrating multi-source data, and utilizing deep learning and cloud computing for smart prediction and assessment.
Article
Biodiversity Conservation
Marzieh Mokarram, Tam Minh Pham
Summary: This study uses a novel meteorological drought-based approach to predict the yield of pomegranate and palm trees in southern Iran, and identifies the most effective drought indices through remote-sensing indices and principal component analysis. The results predict that approximately 50-60% of the region will have low yields for these crops in 2040. This approach provides a framework for predicting the decreasing crop yield due to drought effects and supporting decision-making in sustainable horticultural management.
ECOLOGICAL INDICATORS
(2022)
Article
Multidisciplinary Sciences
Yazhou Yao, Tao Chen, Hanbo Bi, Xinhao Cai, Gensheng Pei, Guoye Yang, Zhiyuan Yan, Xian Sun, Xing Xu, Hai Zhang
Summary: This paper presents the background and results of the Automated Object Recognition in Optical Remote Sensing Imagery track in the 2022 International Algorithm Case Competition, and provides a summary of the challenges, champion solutions, and future directions.
NATIONAL SCIENCE REVIEW
(2023)
Article
Chemistry, Analytical
Emna Ayari, Zeineb Kassouk, Zohra Lili-Chabaane, Nicolas Baghdadi, Mehrez Zribi
Summary: The objective of this paper is to estimate soil moisture in pepper crops with drip irrigation in a semi-arid area in Tunisia using synthetic aperture radar (SAR) data. The sensitivity of different polarizations and bands of SAR data to soil moisture and vegetation properties is examined, and simulated SAR signals are used to analyze the potential of the proposed model. The results indicate the potential of the model to simulate radar signals over heterogeneous soil moisture fields.
Article
Environmental Sciences
Hassan Bazzi, Nicolas Baghdadi, Francois Charron, Mehrez Zribi
Summary: This paper presents a comparative analysis between C- and L-band Synthetic Aperture Radar (SAR) data for the detection of irrigation events. The results show that the L-band is more suitable for irrigation detection than the C-band, regardless of the vegetation cover development and characteristics.
Article
Environmental Sciences
Simon Nativel, Emna Ayari, Nemesio Rodriguez-Fernandez, Nicolas Baghdadi, Remi Madelon, Clement Albergel, Mehrez Zribi
Summary: This study proposes two hybrid methodologies to improve soil moisture estimations, using various metrics and data for testing. The results indicate the effectiveness of the hybrid algorithms.
Article
Environmental Sciences
Ehsan Elwan, Michel Le Page, Lionel Jarlan, Nicolas Baghdadi, Luca Brocca, Sara Modanesi, Jacopo Dari, Pere Quintana Segui, Mehrez Zribi
Summary: This study proposes an operational approach to map irrigated areas based on the synergy of Sentinel-1 and Sentinel-2 data. The proposed methodology is validated in two study sites in Spain and Italy, representing semiarid and humid climatic contexts, respectively. The results show the importance of multi-site training and the consideration of optical/radar synergy and multi-scale spatial information for accurate classification.
Article
Environmental Sciences
Matteo Rolle, Stefania Tamea, Pierluigi Claps, Emna Ayari, Nicolas Baghdadi, Mehrez Zribi
Summary: This study presents an estimation of maize actual sowing periods for the year 2019 by combining optical and radar information from Sentinel-1 and Sentinel-2. The use of NDVI and radar time series enabled a high-resolution assessment of sowing periods and the description of maize emergence through the soil.
Article
Agronomy
Hassan Bazzi, Nicolas Baghdadi, Sami Najem, Hadi Jaafar, Michel Le Page, Mehrez Zribi, Ioannis Faraslis, Marios Spiliotopoulos
Summary: This study assesses the potential of Sentinel-1 Synthetic Aperture Radar data to detect irrigation events at the plot scale. Results show that overall accuracy of irrigation detection using S1 data is good, but varies with climatic conditions and crop types. The density of available S1 images also affects the accuracy of irrigation detection.
Article
Environmental Sciences
Hironori Arai, Mehrez Zribi, Kei Oyoshi, Karin Dassas, Mireille Huc, Shinichi Sobue, Thuy Le Toan
Summary: The aim of this study was to develop a methodology for evaluating the spatiotemporal dynamics of inundation status in tropical wetlands using GNSS-R data. By proposing a new quality control technique and applying it to the Mekong Delta, the study compared the behaviors of GNSS-R reflectivity and ALOS-2 PALSAR-2 scatter signals.
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
Mohamad Hamze, Bruno Cheviron, Nicolas Baghdadi, Dominique Courault, Mehrez Zribi
Summary: This study developed an approach using Sentinel-1 SAR data and the Optirrig crop growth and irrigation model to detect irrigation dates and amounts for maize crops in Southern France. The method analyzed changes in surface soil moisture derived from SAR data to detect irrigation events at the plot scale. The results showed relatively high accuracy in detecting irrigation dates, but varied performance in detecting irrigation amounts depending on the climatic conditions.
Article
Environmental Sciences
Mouad Ettalbi, Nicolas Baghdadi, Pierre-Andre Garambois, Hassan Bazzi, Emmanuel Ferreira, Mehrez Zribi
Summary: Soil moisture maps are indispensable for hydrological, agricultural, and risk assessment purposes, and can now be developed at high spatial resolution using Sentinel-1 SAR data. This paper presents an improved and fully autonomous method for high-resolution soil moisture mapping in bare agricultural areas, without relying on a weather forecasting framework. The proposed solution utilizes neural network techniques and radar data integration to estimate soil moisture accurately, and the results show that it outperforms the traditional method using a priori weather information.
Article
Environmental Sciences
Oscar Rojas-Munoz, Jean-Christophe Calvet, Bertrand Bonan, Nicolas Baghdadi, Catherine Meurey, Adrien Napoly, Jean-Pierre Wigneron, Mehrez Zribi
Summary: Surface soil moisture (SSM) observed by satellites is an essential component of the Earth system. In this study, aggregated SSM observations from Sentinel-1 and Sentinel-2 are assimilated into the ISBA land surface model using the LDAS-Monde tool. The assimilation of SSM alone has a small impact on simulated soil moisture, but a marked impact is observed when leaf area index (LAI) is assimilated, especially when combined with SSM.
Article
Geochemistry & Geophysics
Mehrez Zribi, Karin Dassas, Vincent Dehaye, Pascal Fanise, Emna Ayari, Michel Le Page
Summary: The objective of this study is to analyze the variations in global navigation satellite system reflectometry (GNSS-R) data based on land cover. Airborne measurements using the global navigation satellite system reflectometry instrument (GLORI), a polarimetric instrument, were conducted at an agricultural site in Urgell, Spain in July 2021. In situ measurements of soil and vegetation properties were obtained simultaneously. The study discusses the behavior of copolarization (RR) and cross polarization (RL) reflectivity as a function of land use and estimates the distribution of coherent and incoherent components in the reflected power for different land cover types.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Geosciences, Multidisciplinary
Remi Madelon, Nemesio J. Rodriguez-Fernandez, Hassan Bazzi, Nicolas Baghdadi, Clement Albergel, Wouter Dorigo, Mehrez Zribi
Summary: High-resolution (around 10-100m) surface soil moisture observations are important for various applications. This study adapted the (SMP)-M-2 algorithm to work at 1 km resolution and extended its application to herbaceous vegetation types. The algorithm combines Sentinel-1 and Sentinel-2 data and shows good agreement and accuracy compared to other datasets.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2023)
Article
Engineering, Electrical & Electronic
Saeideh Maleki, Nicolas Baghdadi, Cassio Fraga Dantas, Sami Najem, Hassan Bazzi, Nuria Pantaleoni Reluy, Dino Ienco, Mehrez Zribi
Summary: This study aims to improve the accuracy of rapeseed field detection using Sentinel-1 time series data and addressing ground sample collection challenges. Various solutions, including model transfer and limited training samples, are proposed and evaluated. Different algorithms and their performance in different scenarios are examined, with a focus on the impact of image count and phenological shift.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Hassan Bazzi, Nicolas Baghdadi, Mehrez Zribi
Summary: In this study, two classification models for irrigation mapping were compared. The results showed that the soil-moisture based model using K-means clustering (Dari model) is simple but less accurate, while the (SIM)-I-2 model based on Sentinel-1 and Sentinel-2 time series data has higher accuracy but higher complexity for application.
