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Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning

Fundamental Research 2023年第6期3卷 951-959页

作者： Renhai Zhong Yue Zhu Xuhui Wang Haifeng Li Bin Wang Fengqi You Luis F.Rodríguez Jingfeng Huang K.C.Ting Yibin Ying Tao Lin College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhouZhejiang 310058China International Campus Zhejiang UniversityHainingZhejiang 314400China Sino-French Institute for Earth System Science College of Urban and Environmental SciencesPeking UniversityBeijing 100871China School of Geosciences and Info-Physics Central South UniversitySouth Lushan RoadChangsha 410000China NSW Department of Primary Industries Wagga Wagga Agricultural InstitutePine Gully Road Wagga WaggaNSW 2650Australia Robert Frederick Smith School of Chemical and Biomolecular Engineering Cornell UniversityIthacaNY 14853USA Department of Agricultural and Biological Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Institute of Applied Remote Sensing and Information Technology Zhejiang UniversityHangzhouZhejiang 310058China Key Laboratory of On Site Processing Equipment for Agricultural Products Ministry of Agriculture and Rural AffairsHangzhouZhejiang 310058China Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province HangzhouZhejiang 310058China

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food *** the data-driven deep learning approach has shown great capacity in predicting yield patterns,its capacity to detect and attribute the impacts of climatic extremes on yields remains *** this study,we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018,with a special focus on the extreme yield loss in *** found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018(R^(2)=0.81)and well reproduced the extreme yield anomalies in 2012(R^(2)=0.79).Further attribution analysis indicated that extreme heat stress was the major cause for yield loss,contributing to 72.5%of the yield loss,followed by anomalies of vapor pressure deficit(17.6%)and precipitation(10.8%).Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in *** results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.

关键词： Crop yield estimation Deep Learning Long short-term memory Multi-task learning Extreme yield loss Attribution analysis

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Iterative Random Training Sampling Convolutional Neural Network for Hyperspectral image Classification

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IEEE Transactions on Geoscience and remote sensing 2023年 61卷

作者： Chang, Chein-I Liang, Chia-Chen Hu, Peter Fuming University of Maryland Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan Providence University Department of Computer Science and Information Management Taichung02912 Taiwan University of Maryland School of Medicine R. A. Cowley Shock Trauma Center Shock Trauma Anesthesia Organized Research Center Department of Anesthesia BaltimoreMD21201 United States

Convolutional neural network (CNN) has received considerable interest in hyperspectral image classification (HSIC) lately due to its excellent spectral-spatial feature extraction capability. To improve CNN, many approaches have been directed at exploring the infrastructure of its network by introducing different paradigms. This article takes a rather different approach by developing an iterative CNN that extends a CNN by including a feedback system to repeatedly process the same CNN in an iterative manner. Its idea is to take advantage of a recently developed iterative training sampling spectral-spatial classification (IRTS-SSC) that allows CNN to update its spatial information of classification maps through a feedback spatial fil- tering system via IRTS. The resulting CNN is called iterative random training sampling CNN (IRTS-CNN) with several unique features. First, IRTS-CNN combines CNN and IRTS-SSC into one paradigm, an architecture that has never been investigated in the past. Second, it implements a series of spatial filters to capture spatial information of classified data samples and further feeds this information back via an iterative process to expand the current input data cube for the next iteration. Third, it utilizes the expanded data cube to randomly reselect training samples and then to reimplement CNN iteratively. Last but not least, IRTS-CNN provides a general framework that can implement any arbitrary CNN as an initial classifier to improve its performance through an iterative process. Extensive experiments are conducted to demonstrate that IRTS-CNN indeed significantly improves CNN, specifically when only a small size of limited training samples is used. © 1980-2012 IEEE.

关键词： Iterative methods

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Hybrid Conditional Random Fields Based on Complex-valued 3D CNN for PolSAR image Classification

Hybrid Conditional Random Fields Based on Complex-valued 3D ...

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IEEE National Conference on Radar

作者： Peng Zhang Beibei Li Xiaofeng Tan Yinyin Jiang Ming Li Yan Wu National laboratory of Radar Signal Processing Xidian University Xi'an China Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University Xi'an China

ISBN: (数字)9781728189420

ISBN: (纸本)9781728189437

In this paper, we propose a hybrid conditional random fields based on complex-valued 3-dimensional convolutional neural network (CV3DCNN), named as HCRF-CV3DCNN, for polarimetric synthetic aperture radar (PolSAR) image classification. HCRF-CV3DCNN extracts deep features of PolSAR image by CV3DCNN, and thus exploits amplitude and phase information of PolSAR data to measure the class probability in the framework of random fields. Additionally, based on the CV3DCNN-based class probabilities, the relative entropy of class distributions is derived and utilized to regulate the label interactions to enhance the accuracy of edge location in the classification. At last, to capture PolSAR image information in a more complete manner, the deep features and PolSAR scattering statistics are integrated into HCRF-CV3DCNN based on Bayesian fusion. In this way, HCRF-CV3DCNN effectively combines the representation-learning ability of deep learning model with the modeling power of random fields including spatial correlation and data statistics. The experimental results demonstrate the superiority of HCRF-CV3DCNN over the recent deep learning models for PolSAR image classification.

关键词： Scattering Feature extraction image classification Data models Entropy Deep learning Kernel

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AI for Extreme Event Modeling and Understanding: Methodologies and Challenges

arXiv

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arXiv 2024年

作者： Camps-Valls, Gustau Fernández-Torres, Miguel-Ángel Cohrs, Kai-Hendrik Höhl, Adrian Castelletti, Andrea Pacal, Aytac Robin, Claire Martinuzzi, Francesco Papoutsis, Ioannis Prapas, Ioannis Pérez-Aracil, Jorge Weigel, Katja Gonzalez-Calabuig, Maria Reichstein, Markus Rabel, Martin Giuliani, Matteo Mahecha, Miguel Popescu, Oana-Iuliana Pellicer-Valero, Oscar J. Ouala, Said Salcedo-Sanz, Sancho Sippel, Sebastian Kondylatos, Spyros Happé, Tamara Williams, Tristan Image Processing Laboratory Universitat de València Spain Technical University of Munich Munich Germany Department of Electronics Information and Bioengineering Politecnico di Milano Italy Bremen Germany Max Planck Institute of Biogeochemistry Jena Germany Leipzig University Leipzig Germany National Technical University of Athens Greece National Observatory of Athens Athens Greece Department of Signal Processing and Communications Universidad de Alcalá Madrid Spain Jena Germany IMT Atlantique Brest France Institute for Environmental Studies VU Amsterdam Netherlands Leipzig Germany Institute for Earth System Science & Remote Sensing Leipzig University Leipzig Germany Leipzig University Leipzig Germany Institut für Physik der Atmosphäre Oberpfaffenhofen Germany

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. However, the latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous and limited annotated data. This paper reviews how AI is being used to analyze extreme events (like floods, droughts, wildfires and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models. We discuss the hurdles of dealing with limited data, integrating information in real-time, deploying models, and making them understandable, all crucial for gaining the trust of stakeholders and meeting regulatory needs. We provide an overview of how AI can help identify and explain extreme events more effectively, improving disaster response and communication. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy for analyzing and predicting extreme events. Such collaborative efforts aim to enhance disaster readiness and disaster risk reduction. © 2024, CC BY.

关键词： Floods

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Assessment of groundwater potential zones using remote sensing and GIS-based fuzzy analytical hierarchy process (F-AHP) in Mpwapwa District, Dodoma, Tanzania

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Geosystems and Geoenvironment 2024年第1期3卷

作者： Ally, Ally Mgelwa Yan, Jianguo Bennett, George Lyimo, Neema Nicodemus Mayunga, Selassie David State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430079 China Department of Geology College of Earth Sciences and Engineering University of Dodoma Dodoma Iyumbu 41218 Tanzania Department of Mining and Mineral Processing Engineering College of Earth Sciences and Engineering University of Dodoma Dodoma Iyumbu 41218 Tanzania Department of Informatics and Information Technology College of Natural and Applied Sciences Sokoine University of Agriculture Morogoro 3038 Tanzania Botswana International University of Science and Technology Plot 10071 Boseja Palapye Botswana

Groundwater is a very important resource for socio-economic development. The uncertainty of where potential groundwater resources is located often causes some groundwater development projects to fail. It is common for water resources development projects hitting dry wells after heavy investments of resources. In Mpwapwa District, borehole drilling locations are uncertain, determined by trial-and-error techniques based on geophysical survey methods that involve the study of the behaviour of rock and soil types in specific geological locations. To reduce such uncertainty, this study used remote sensing and GIS-based Fuzzy Analytical Hierarchical Process (F-AHP) to simulate groundwater potential zones (GWPZ) in Mpwapwa District, Dodoma region, Tanzania. The F-AHP model was used to reclassify, weight, and rank various thematic maps, including lithology, soil types, drainage density, lineament, magnetic intensity, slope and elevation. The overall GWPZ map was created by combining the seven (7) ranking thematic map layers in a GIS environment. The resulting GWPZ map that was then validated using two methods: overlaying method and area under the curve (AUC) method. The resulting GWPZ map shows that 19%, 31%, 28% and 22% of the area are classified as very good, good, moderate, poor and very poor zones, respectively. The accuracy of the generated map is 72% using the overlaying method and 93% using the AUC method. © 2023 The Authors

关键词： Fuzzy analytic hierarchy process Geographic information system Groundwater potential zones Mpwapwa District remote sensing Semi-arid region

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Detection of White Matter Hyperintensities in Magnetic Resonance Imaging by Hyperspectral Subpixel Detection

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IEEE Access 2024年 12卷 111992-112007页

作者： Yung-Chieh Chang Chein-I Chang Yen-Chieh Ouyang Jyh-Wen Chai Wen-Hsien Chen Kuan-Jung Pan Hsin-Che Wang Clayton Chi-Chang Chen Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging and Radiological Sciences Central Taiwan University of Science and Technology Taichung Taiwan Department of Computer Science and Electrical Engineering Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore County Baltimore MD USA Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan College of Medicine China Medical University Taichung Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung Taiwan Department of Medical Research Center for Quantitative Imaging in Medicine (CQUIM) Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging China Medical University Hospital Taichung Taiwan

White matter hyperintensities (WMHs) are lesion in brain magnetic resonance images generally associated with Alzheimer’s disease (AD) and cognitive decline. Finding WMHs of AD poses a great challenge for diagnosis. This paper interprets a brain MR image as a hyperspectral image so that a well stablished hyperspectral subpixel detection algorithm, constrained energy minimization (CEM), is applicable to solving the WMHs detection problem at mixed pixel and subpixel level. To resolve nonlinear mixing in detecting WMHs nearby boundaries, a nonlinear CEM, called kernel CEM (KCEM) is also developed. Since CEM is a hyperspectral technique without taking spatial correlation into account, CEM was also extended to iterative CEM (ICEM) by including spatial filters to capture spatial information for WMHs detection. This paper combines ICEM and KCEM to derive a new WHMs detection algorithm, iterative KCEM (IKCEM) to improve ICEM and KCEM on WMHs detection. To evaluate the WMHs detection performance, two criteria, Dice similarity index (DSI) and 3D ROC analysis are used as evaluation tools. In order to show the superiority of IKCEM, two commonly used software packages, statistical parametric mapping (SPM)-based algorithms, SPM-lesion growth algorithm (SPM-LGA) and SPM-lesion prediction algorithm (SPM-LPA) are implemented for validation and comparison.

关键词： Hyperspectral imaging Magnetic resonance imaging Iterative methods Medical diagnostic imaging White matter Manuals Energy management Neuroimaging Brain modeling Alzheimer's disease Energy measurement

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A Novel Parameter Estimation Method for Generalized Gamma Distribution Towards SAR Data processing 6

A Novel Parameter Estimation Method for Generalized Gamma Di...

引用

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Zhang, Peng Li, Beibei Boudaren, Mohamed El Yazid Yan, Junkun Li, Ming Wu, Yan National Laboratory of Radar Signal Processing Xidian University Xi'an China School of Electronic Engineering Ecole Militaire Polytechnique Bordj El Bahri Algiers Algeria Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University Xi'an China

ISBN: (纸本)9781728129129

Statistical modeling of synthetic aperture radar (SAR) data is a crucial step in SAR image processing. In this context, the generalized Gamma (GGamma) distribution, which generalizes many common distributions, has been recognized as a valid model. Parameter estimation remains, however, a challenging step that conditions the quality of model fitting to data and thus, of the required processing. In this paper, we propose a novel parameter estimation method for GGamma distribution in the log-domain, named as the maximum likelihood and logarithmic cumulants (ML-LC) method. The ML-LC method constructs a novel scale-independent shape parameter estimation in the log-domain based on the Mellin transform and maximum likelihood estimation, and proposes a feasible solution technique for the shape parameter based on the multistart local search (MLS), gradient descent (GD) and bisection methods. To assess the performance of our estimation method, we perform the goodness-of-fit test on SAR data. In addition, we apply the ML-LC method in some SAR data processing tasks covering image segmentation and classification. The results obtained confirm the interest of the proposed ML-LC method. © 2019 IEEE.

关键词： Synthetic aperture radar

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SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics

arXiv

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arXiv 2023年

作者： Karasante, Ilektra Alonso, Lazaro Prapas, Ioannis Ahuja, Akanksha Carvalhais, Nuno Papoutsis, Ioannis National Observatory of Athens Institute for Astronomy Astrophysics Space Applications and Remote Sensing Penteli15236 Greece Max Planck Institute for Biogeochemistry Department of biogeochemical Integration Jena07745 Germany Universitat de València Image Processing Laboratory València46022 Spain University of Cambridge Department of Engineering CambridgeCB2 1PZ United Kingdom Universidade Nova de Lisboa Departamento de Ciências e Engenharia do Ambiente Faculdade de Ciências e Tecnologia Caparica2829-516 Portugal ELLIS Unit Jena Jena Germany National Technical University of Athens School of Rural Surveying and Geoinformatics Engineering Zografou15773 Greece

The global occurrence, scale, and frequency of wildfires pose significant threats to ecosystem services and human livelihoods. To effectively quantify and attribute the antecedent conditions for wildfires, a thorough understanding of Earth system dynamics is imperative. In response, we introduce the SeasFire datacube, a meticulously curated spatiotemporal dataset tailored for global sub-seasonal to seasonal wildfire modeling via Earth observation. The SeasFire datacube comprises of 59 variables encompassing climate, vegetation, oceanic indices, and human factors, has an 8-day temporal resolution and a spatial resolution of 0.25 ◦, and spans from 2001 to 2021. We showcase the versatility of SeasFire for exploring the variability and seasonality of wildfire drivers, modeling causal links between ocean-climate teleconnections and wildfires, and predicting sub-seasonal wildfire patterns across multiple timescales with a Deep Learning model. We publicly release the SeasFire datacube and appeal to Earth system scientists and Machine Learning practitioners to use it for an improved understanding and anticipation of wildfires. © 2023, CC BY.

关键词： Earth system models

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Estimating crop primary productivity with sentinel-2 and landsat 8 using machine learning methods trained with radiative transfer simulations

arXiv

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arXiv 2020年

作者： Wolanin, Aleksandra Camps-Valls, Gustau Gómez-Chova, Luis Mateo-García, Gonzalo van der Tol, Christiaan Zhang, Yongguang Guanter, Luis Section 1.4 Remote Sensing GFZ German Research Centre for Geosciences Helmholtz-Centre Potsdam Germany Image Processing Laboratory Universitat de València València Spain Faculty of ITC University of Twente Enschede Netherlands Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System Sciences Nanjing University Nanjing210023 China

Satellite remote sensing has been widely used in the last decades for agricultural applications, both for assessing vegetation condition and for subsequent yield prediction. Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for an advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 and Landsat 8 optical remote sensing data and machine learning methods in order to estimate crop GPP. With this approach, we by-pass the need for an intermediate step to retrieve the set of vegetation biophysical parameters needed to accurately model photosynthesis, while still accounting for the complex processes of the original physically-based model. Several implementations of the machine learning models are tested and validated using simulated and flux tower-based GPP data. Our final neural network model is able to estimate GPP at the tested flux tower sites with r2 of 0.92 and RMSE of 1.38 gC d−1 m−2, which outperforms empirical models based on vegetation indices. The first test of applicability of this model to Landsat 8 data showed good results (r2 of 0.82 and RMSE of 1.97 gC d−1 m−2), which suggests that our approach can be further applied to other sensors. Modeling and testing is restricted to C3 crops in this study, but can be extended to C4 crops by producing a new training dataset with SCOPE that accounts for the different photosynthetic pathways. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from

关键词： Photosynthesis

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Pixel-wise PolSAR image classification via a novel complex-valued deep fully convolutional network

arXiv

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arXiv 2019年

作者： Cao, Yice Wu, Yan Zhang, Peng Liang, Wenkai Li, Ming Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University Xi'an710071 China National Key Laboratory of Radar Signal Processing Xidian University Xi'an710071 China Collaborative Innovation Center of Information Sensing and Understanding Xidian University Xian710071 China

Although complex-valued (CV) neural networks have shown better classification results compared to their real-valued (RV) counterparts for polarimetric synthetic aperture radar (PolSAR) classification, the extension of pixel-level RV networks to the complex domain has not yet thoroughly examined. This paper presents a novel complex-valued deep fully convolutional neural network (CV-FCN) designed for PolSAR image classification. Specifically, CV-FCN uses PolSAR CV data that includes the phase information and utilizes the deep FCN architecture that performs pixel-level labeling. It integrates the feature extraction module and the classification module in a united framework. Technically, for the particularity of PolSAR data, a dedicated complex-valued weight initialization scheme is defined to initialize CV-FCN. It considers the distribution of polarization data to conduct CV-FCN training from scratch in an efficient and fast manner. CV-FCN employs a complex downsampling-then-upsampling scheme to extract dense features. To enrich discriminative information, multi-level CV features that retain more polarization information are extracted via the complex downsampling scheme. Then, a complex upsampling scheme is proposed to predict dense CV labeling. It employs complex max-unpooling layers to greatly capture more spatial information for better robustness to speckle noise. In addition, to achieve faster convergence and obtain more precise classification results, a novel average cross-entropy loss function is derived for CV-FCN optimization. Experiments on real PolSAR datasets demonstrate that CV-FCN achieves better classification performance than other state-of-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： image classification

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