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Wu L, Shao H, Li J, Chen C, Hu N, Yang B, Weng H, Xiang L, Ye D. Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0180. [PMID: 38779576 PMCID: PMC11109595 DOI: 10.34133/plantphenomics.0180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 03/29/2024] [Indexed: 05/25/2024]
Abstract
The last decades have witnessed a rapid development of noninvasive plant phenotyping, capable of detecting plant stress scale levels from the subcellular to the whole population scale. However, even with such a broad range, most phenotyping objects are often just concerned with leaves. This review offers a unique perspective of noninvasive plant stress phenotyping from a multi-organ view. First, plant sensing and responding to abiotic stress from the diverse vegetative organs (leaves, stems, and roots) and the interplays between these vital components are analyzed. Then, the corresponding noninvasive optical phenotyping techniques are also provided, which can prompt the practical implementation of appropriate noninvasive phenotyping techniques for each organ. Furthermore, we explore methods for analyzing compound stress situations, as field conditions frequently encompass multiple abiotic stressors. Thus, our work goes beyond the conventional approach of focusing solely on individual plant organs. The novel insights of the multi-organ, noninvasive phenotyping study provide a reference for testing hypotheses concerning the intricate dynamics of plant stress responses, as well as the potential interactive effects among various stressors.
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Affiliation(s)
- Libin Wu
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Han Shao
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Center for Artificial Intelligence in Agriculture, School of Future Technology,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Jiayi Li
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Chen Chen
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Nana Hu
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Center for Artificial Intelligence in Agriculture, School of Future Technology,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Biyun Yang
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Haiyong Weng
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Lirong Xiang
- Department of Biological and Agricultural Engineering,
North Carolina State University, Raleigh, NC 27606, USA
| | - Dapeng Ye
- College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Fujian Key Laboratory of Agricultural Information Sensing Technology, College of Mechanical and Electrical Engineering,
Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
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2
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Vargas-Rojas L, Ting TC, Rainey KM, Reynolds M, Wang DR. AgTC and AgETL: open-source tools to enhance data collection and management for plant science research. FRONTIERS IN PLANT SCIENCE 2024; 15:1265073. [PMID: 38450403 PMCID: PMC10915008 DOI: 10.3389/fpls.2024.1265073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 01/30/2024] [Indexed: 03/08/2024]
Abstract
Advancements in phenotyping technology have enabled plant science researchers to gather large volumes of information from their experiments, especially those that evaluate multiple genotypes. To fully leverage these complex and often heterogeneous data sets (i.e. those that differ in format and structure), scientists must invest considerable time in data processing, and data management has emerged as a considerable barrier for downstream application. Here, we propose a pipeline to enhance data collection, processing, and management from plant science studies comprising of two newly developed open-source programs. The first, called AgTC, is a series of programming functions that generates comma-separated values file templates to collect data in a standard format using either a lab-based computer or a mobile device. The second series of functions, AgETL, executes steps for an Extract-Transform-Load (ETL) data integration process where data are extracted from heterogeneously formatted files, transformed to meet standard criteria, and loaded into a database. There, data are stored and can be accessed for data analysis-related processes, including dynamic data visualization through web-based tools. Both AgTC and AgETL are flexible for application across plant science experiments without programming knowledge on the part of the domain scientist, and their functions are executed on Jupyter Notebook, a browser-based interactive development environment. Additionally, all parameters are easily customized from central configuration files written in the human-readable YAML format. Using three experiments from research laboratories in university and non-government organization (NGO) settings as test cases, we demonstrate the utility of AgTC and AgETL to streamline critical steps from data collection to analysis in the plant sciences.
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Affiliation(s)
- Luis Vargas-Rojas
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - To-Chia Ting
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Katherine M. Rainey
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Matthew Reynolds
- Wheat Physiology Group, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Diane R. Wang
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
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3
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Carlier A, Dandrifosse S, Dumont B, Mercatoris B. To What Extent Does Yellow Rust Infestation Affect Remotely Sensed Nitrogen Status? PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0083. [PMID: 37681000 PMCID: PMC10482323 DOI: 10.34133/plantphenomics.0083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 08/03/2023] [Indexed: 09/09/2023]
Abstract
The utilization of high-throughput in-field phenotyping systems presents new opportunities for evaluating crop stress. However, existing studies have primarily focused on individual stresses, overlooking the fact that crops in field conditions frequently encounter multiple stresses, which can display similar symptoms or interfere with the detection of other stress factors. Therefore, this study aimed to investigate the impact of wheat yellow rust on reflectance measurements and nitrogen status assessment. A multi-sensor mobile platform was utilized to capture RGB and multispectral images throughout a 2-year fertilization-fungicide trial. To identify disease-induced damage, the SegVeg approach, which combines a U-NET architecture and a pixel-wise classifier, was applied to RGB images, generating a mask capable of distinguishing between healthy and damaged areas of the leaves. The observed proportion of damage in the images demonstrated similar effectiveness to visual scoring methods in explaining grain yield. Furthermore, the study discovered that the disease not only affected reflectance through leaf damage but also influenced the reflectance of healthy areas by disrupting the overall nitrogen status of the plants. This emphasizes the importance of incorporating disease impact into reflectance-based decision support tools to account for its effects on spectral data. This effect was successfully mitigated by employing the NDRE vegetation index calculated exclusively from the healthy portions of the leaves or by incorporating the proportion of damage into the model. However, these findings also highlight the necessity for further research specifically addressing the challenges presented by multiple stresses in crop phenotyping.
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Affiliation(s)
- Alexis Carlier
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech,
University of Liège, 5030 Gembloux, Belgium
| | - Sebastien Dandrifosse
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech,
University of Liège, 5030 Gembloux, Belgium
| | - Benjamin Dumont
- Plant Sciences, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech,
University of Liège, 5030 Gembloux, Belgium
| | - Benoît Mercatoris
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech,
University of Liège, 5030 Gembloux, Belgium
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4
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Wong CYS. Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications. AOB PLANTS 2023; 15:plad039. [PMID: 37560760 PMCID: PMC10407989 DOI: 10.1093/aobpla/plad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 07/04/2023] [Indexed: 08/11/2023]
Abstract
Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.
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5
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Ganeva D, Roumenina E, Dimitrov P, Gikov A, Jelev G, Dyulgenova B, Valcheva D, Bozhanova V. Remotely Sensed Phenotypic Traits for Heritability Estimates and Grain Yield Prediction of Barley Using Multispectral Imaging from UAVs. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115008. [PMID: 37299735 DOI: 10.3390/s23115008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/12/2023] [Accepted: 05/20/2023] [Indexed: 06/12/2023]
Abstract
This study tested the potential of parametric and nonparametric regression modeling utilizing multispectral data from two different unoccupied aerial vehicles (UAVs) as a tool for the prediction of and indirect selection of grain yield (GY) in barley breeding experiments. The coefficient of determination (R2) of the nonparametric models for GY prediction ranged between 0.33 and 0.61 depending on the UAV and flight date, where the highest value was achieved with the DJI Phantom 4 Multispectral (P4M) image from 26 May (milk ripening). The parametric models performed worse than the nonparametric ones for GY prediction. Independent of the retrieval method and UAV, GY retrieval was more accurate in milk ripening than dough ripening. The leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fraction vegetation cover (fCover), and leaf chlorophyll content (LCC) were modeled at milk ripening using nonparametric models with the P4M images. A significant effect of the genotype was found for the estimated biophysical variables, which was referred to as remotely sensed phenotypic traits (RSPTs). Measured GY heritability was lower, with a few exceptions, compared to the RSPTs, indicating that GY was more environmentally influenced than the RSPTs. The moderate to strong genetic correlation of the RSPTs to GY in the present study indicated their potential utility as an indirect selection approach to identify high-yield genotypes of winter barley.
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Affiliation(s)
- Dessislava Ganeva
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Eugenia Roumenina
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Petar Dimitrov
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Alexander Gikov
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Georgi Jelev
- Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | | | - Darina Valcheva
- Institute of Agriculture, Agriculture Academy, 8400 Karnobat, Bulgaria
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Gamon JA, Wang R, Russo SE. Contrasting photoprotective responses of forest trees revealed using PRI light responses sampled with airborne imaging spectrometry. THE NEW PHYTOLOGIST 2023; 238:1318-1332. [PMID: 36658464 DOI: 10.1111/nph.18754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 01/16/2023] [Indexed: 06/17/2023]
Abstract
The Photochemical Reflectance Index (PRI) provides an optical indicator of photosynthetic light-use efficiency, photoprotection, and stress in plants. Although PRI can be applied in remote sensing, its interpretation depends on irradiance, which is hard to obtain from satellite or airborne imagery. To quantify forest photoprotective responses remotely, we developed a framework for modeling and interpreting PRI-light responses of individual trees and species using airborne imaging spectrometry coupled with georeferenced forest inventory data from a temperate broad-leaved forest. We derived an irradiance proxy, used hierarchical modeling to analyze PRI-light responses, and developed a framework of physiological interpretations of model parameters as facultative and constitutive components of photoprotection. Photochemical Reflectance Index declined with illumination, and PRI-light relationships varied with landscape position and among tree crowns and species. More sun-exposed foliage had lower intercepts and slopes of the relationship, indicating greater constitutive, but less facultative, photoprotection. We show that tree photoprotective strategies can be quantified at multiple scales using airborne hyperspectral data in structurally complex forests. Our findings and approach have important implications for the remote sensing of forest stress by offering a new way to assess functional diversity through dynamic differences in photoprotection and photosynthetic downregulation and providing previsual indicators of forest stress.
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Affiliation(s)
- John A Gamon
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, 68583-0961, USA
| | - Ran Wang
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, 68583-0961, USA
| | - Sabrina E Russo
- School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, NE, 68588-0118, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, 68588-0660, USA
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7
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Wong CYS, Gilbert ME, Pierce MA, Parker TA, Palkovic A, Gepts P, Magney TS, Buckley TN. Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0021. [PMID: 37040284 PMCID: PMC10076057 DOI: 10.34133/plantphenomics.0021] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/12/2022] [Indexed: 06/19/2023]
Abstract
Proximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response. Bean plants, an important legume for human consumption, are often grown in regions with limited rainfall and irrigation and are therefore bred to further enhance drought tolerance. We assessed physiological (stomatal conductance and predawn and midday leaf water potential) and ground- and tower-based hyperspectral remote sensing (400 to 2,400 nm and 400 to 900 nm, respectively) measurements to evaluate drought response in 12 common bean and 4 tepary bean genotypes across 3 field campaigns (1 predrought and 2 post-drought). Hyperspectral data in partial least squares regression models predicted these physiological traits (R 2 = 0.20 to 0.55; root mean square percent error 16% to 31%). Furthermore, ground-based partial least squares regression models successfully ranked genotypic drought responses similar to the physiologically based ranks. This study demonstrates applications of high-resolution hyperspectral remote sensing for predicting plant traits and phenotyping drought response across genotypes for vegetation monitoring and breeding population screening.
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8
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Hu J, Zhang B, Peng D, Yu R, Liu Y, Xiao C, Li C, Dong T, Fang M, Ye H, Huang W, Lin B, Wang M, Cheng E, Yang S. Estimation of wheat tiller density using remote sensing data and machine learning methods. FRONTIERS IN PLANT SCIENCE 2022; 13:1075856. [PMID: 36618628 PMCID: PMC9810811 DOI: 10.3389/fpls.2022.1075856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
The tiller density is a key agronomic trait of winter wheat that is essential to field management and yield estimation. The traditional method of obtaining the wheat tiller density is based on manual counting, which is inefficient and error prone. In this study, we established machine learning models to estimate the wheat tiller density in the field using hyperspectral and multispectral remote sensing data. The results showed that the vegetation indices related to vegetation cover and leaf area index are more suitable for tiller density estimation. The optimal mean relative error for hyperspectral data was 5.46%, indicating that the results were more accurate than those for multispectral data, which had a mean relative error of 7.71%. The gradient boosted regression tree (GBRT) and random forest (RF) methods gave the best estimation accuracy when the number of samples was less than around 140 and greater than around 140, respectively. The results of this study support the extension of the tested methods to the large-scale monitoring of tiller density based on remote sensing data.
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Affiliation(s)
- Jinkang Hu
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Bing Zhang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Dailiang Peng
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- International Research Center of Big Data for Sustainable Development Goals, Beijing, China
| | - Ruyi Yu
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Yao Liu
- Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing, China
| | - Chenchao Xiao
- Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing, China
| | - Cunjun Li
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Tao Dong
- Aerospace ShuWei High Tech. Co., Ltd., Beijing, China
| | - Moren Fang
- Beijing Azup Scientific Co., Ltd., Beijing, China
| | - Huichun Ye
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- International Research Center of Big Data for Sustainable Development Goals, Beijing, China
| | - Wenjiang Huang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- International Research Center of Big Data for Sustainable Development Goals, Beijing, China
| | - Binbin Lin
- Department of Geography, Texas A&M University, TX, United States
| | - Mengmeng Wang
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, China
| | - Enhui Cheng
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Songlin Yang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, China
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9
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Wocher M, Berger K, Verrelst J, Hank T. Retrieval of carbon content and biomass from hyperspectral imagery over cultivated areas. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING : OFFICIAL PUBLICATION OF THE INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING (ISPRS) 2022; 193:104-114. [PMID: 36643957 PMCID: PMC7614045 DOI: 10.1016/j.isprsjprs.2022.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Spaceborne imaging spectroscopy is a highly promising data source for all agricultural management and research disciplines that require spatio-temporal information on crop properties. Recently launched science-driven missions, such as the Environmental Mapping and Analysis Program (EnMAP), deliver unprecedented data from the Earth's surface. This new kind of data should be explored to develop robust retrieval schemes for deriving crucial variables from future routine missions. Therefore, we present a workflow for inferring crop carbon content (Carea ), and aboveground dry and wet biomass (AGBdry , AGBfresh ) from EnMAP data. To achieve this, a hybrid workflow was generated, combining radiative transfer modeling (RTM) with machine learning regression algorithms. The key concept involves: (1) coupling the RTMs PROSPECT-PRO and 4SAIL for simulation of a wide range of vegetation states, (2) using dimensionality reduction to deal with collinearity, (3) applying a semi-supervised active learning technique against a 4-years campaign dataset, followed by (4) training of a Gaussian process regression (GPR) machine learning algorithm and (5) validation with an independent in situ dataset acquired during the ESA Hypersense experiment campaign at a German test site. Internal validation of the GPR-Carea and GPR-AGB models achieved coefficients of determination (R 2) of 0.80 for Carea and 0.80, 0.71 for AGBdry and AGBfresh , respectively. The mapping capability of these models was successfully demonstrated using airborne AVIRIS-NG hyperspectral imagery, which was spectrally resampled to EnMAP spectral properties. Plausible estimates were achieved over both bare and green fields after adding bare soil spectra to the training data. Validation over green winter wheat fields generated reliable estimates as suggested by low associated model uncertainties (< 40%). These results suggest a high degree of model reliability for cultivated areas during active growth phases at canopy closure. Overall, our proposed carbon and biomass models based on EnMAP spectral sampling demonstrate a promising path toward the inference of these crucial variables over cultivated areas from future spaceborne operational hyperspectral missions.
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Affiliation(s)
- Matthias Wocher
- Department of Geography, Ludwig-Maximilians Universität München, Munich, Germany
| | - Katja Berger
- Image Processing Laboratory (IPL), University of Valencia, Valencia, Spain
- Mantle Labs GmbH, Vienna, Austria
| | - Jochem Verrelst
- Image Processing Laboratory (IPL), University of Valencia, Valencia, Spain
| | - Tobias Hank
- Department of Geography, Ludwig-Maximilians Universität München, Munich, Germany
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10
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Berger K, Machwitz M, Kycko M, Kefauver SC, Van Wittenberghe S, Gerhards M, Verrelst J, Atzberger C, van der Tol C, Damm A, Rascher U, Herrmann I, Paz VS, Fahrner S, Pieruschka R, Prikaziuk E, Buchaillot ML, Halabuk A, Celesti M, Koren G, Gormus ET, Rossini M, Foerster M, Siegmann B, Abdelbaki A, Tagliabue G, Hank T, Darvishzadeh R, Aasen H, Garcia M, Pôças I, Bandopadhyay S, Sulis M, Tomelleri E, Rozenstein O, Filchev L, Stancile G, Schlerf M. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review. REMOTE SENSING OF ENVIRONMENT 2022; 280:113198. [PMID: 36090616 PMCID: PMC7613382 DOI: 10.1016/j.rse.2022.113198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under shortterm, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.
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Affiliation(s)
- Katja Berger
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna 46980, Valencia, Spain
- Department of Geography, Ludwig-Maximilians-Universität München (LMU), Luisenstr. 37, 80333 Munich, Germany
| | - Miriam Machwitz
- Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, L-4422 Belvaux, Luxembourg
| | - Marlena Kycko
- Department of Geoinformatics Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warszawa, Poland
| | - Shawn C. Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Shari Van Wittenberghe
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna 46980, Valencia, Spain
| | - Max Gerhards
- Earth Observation and Climate Processes, Trier University, 54286 Trier, Germany
| | - Jochem Verrelst
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna 46980, Valencia, Spain
| | - Clement Atzberger
- Institute of Geomatics, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Str. 82, 1190 Vienna, Austria
| | - Christiaan van der Tol
- Faculty Geo-Information Science and Earth Observation, ITC, University of Twente, the Netherlands
| | - Alexander Damm
- Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Uwe Rascher
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Ittai Herrmann
- The Plant Sensing Laboratory, The Robert H. Smith Institute for Plant Sciences and Genetics in Agriculture, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 7610001, Israel
| | - Veronica Sobejano Paz
- Department of Environmental Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Sven Fahrner
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Roland Pieruschka
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Egor Prikaziuk
- Faculty Geo-Information Science and Earth Observation, ITC, University of Twente, the Netherlands
| | - Ma. Luisa Buchaillot
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Andrej Halabuk
- Institute of Landscape Ecology, Slovak Academy of Sciences, 814 99 Bratislava, Slovakia
| | - Marco Celesti
- HE Space for ESA - European Space Agency, European Space Research and Technology Centre (ESA-ESTEC), Keplerlaan 1, 2201, AZ Noordwijk, the Netherlands
| | - Gerbrand Koren
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, the Netherlands
| | - Esra Tunc Gormus
- Department of Geomatics Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey
| | - Micol Rossini
- Remote Sensing of Environmental Dynamics Laboratory (LTDA), University of Milano - Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Michael Foerster
- Geoinformation in Environmental Planning Lab, Technische Universität Berlin, 10623 Berlin, Germany
| | - Bastian Siegmann
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Asmaa Abdelbaki
- Earth Observation and Climate Processes, Trier University, 54286 Trier, Germany
| | - Giulia Tagliabue
- Remote Sensing of Environmental Dynamics Laboratory (LTDA), University of Milano - Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Tobias Hank
- Department of Geography, Ludwig-Maximilians-Universität München (LMU), Luisenstr. 37, 80333 Munich, Germany
| | - Roshanak Darvishzadeh
- Faculty Geo-Information Science and Earth Observation, ITC, University of Twente, the Netherlands
| | - Helge Aasen
- Earth Observation and Analysis of Agroecosystems Team, Division Agroecology and Environment, Agroscope, Zurich, Switzerland
- Institute of Agricultural Science, ETH Zürich, Zurich, Switzerland
| | - Monica Garcia
- Research Centre for the Management of Agricultural and Environmental Risks (CEIGRAM), ETSIAAB, Universidad Politécnica de Madrid, 28040, Spain
| | - Isabel Pôças
- ForestWISE - Collaborative Laboratory for Integrated Forest & Fire Management, Quinta de Prados, Campus da UTAD, 5001-801 Vila Real, Portugal
| | | | - Mauro Sulis
- Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, L-4422 Belvaux, Luxembourg
| | - Enrico Tomelleri
- Faculty of Science and Technology, Free University of Bozen/Bolzano, Italy
| | - Offer Rozenstein
- Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization—Volcani Institute, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7528809, Israel
| | - Lachezar Filchev
- Space Research and Technology Institute, Bulgarian Academy of Sciences (SRTI-BAS), Bulgaria
| | - Gheorghe Stancile
- National Meteorological Administration, Building A, Soseaua Bucuresti-Ploiesti 97, 013686 Bucuresti, Romania
| | - Martin Schlerf
- Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, L-4422 Belvaux, Luxembourg
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11
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Caballero G, Pezzola A, Winschel C, Casella A, Angonova PS, Rivera-Caicedo JP, Berger K, Verrelst J, Delegido J. Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery. REMOTE SENSING 2022; 14:4531. [PMID: 36186714 PMCID: PMC7613660 DOI: 10.3390/rs14184531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop's phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learning regression algorithm, i.e., Gaussian processes regression, and an active learning technique to estimate LAI, CCC and VWC of irrigated winter wheat. The established hybrid models of the three traits were validated against in-situ data of a wheat campaign in the Bonaerense valley, South of the Buenos Aires Province, Argentina, in the year 2020. We obtained good to highly accurate validation results with LAI: R2 = 0.92, RMSE = 0.43 m2 m-2, CCC: R2 = 0.80, RMSE = 0.27 g m-2 and VWC: R2 = 0.75, RMSE = 416 g m-2. The retrieval models were also applied to a series of S2 images, producing time series along the seasonal cycle, which reflected the effects of fertilizer and irrigation on crop growth. The associated uncertainties along with the obtained maps underlined the robustness of the hybrid retrieval workflow. We conclude that processing S2 imagery with optimised hybrid models allows accurate space-based crop traits mapping over large irrigated areas and thus can support agricultural management decisions.
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Affiliation(s)
- Gabriel Caballero
- Agri-Environmental Engineering, Technological University of Uruguay (UTEC), Av. Italia 6201, Montevideo 11500, Uruguay
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain
| | - Alejandro Pezzola
- Remote Sensing and SIG Laboratory, Hilario Ascasubi Agricultural Experimental Station, National Institute of Agricultural Technology (INTA), Hilario Ascasubi 8142, Argentina
| | - Cristina Winschel
- Remote Sensing and SIG Laboratory, Hilario Ascasubi Agricultural Experimental Station, National Institute of Agricultural Technology (INTA), Hilario Ascasubi 8142, Argentina
| | - Alejandra Casella
- Permanent Observatory of Agro-Ecosystems, Climate and Water Institute-National Agricultural Research Centre (ICyA-CNIA), National Institute of Agricultural Technology (INTA), Nicolás Repetto s/n, Hurlingham, Buenos Aires 1686, Argentina
| | - Paolo Sanchez Angonova
- Remote Sensing and SIG Laboratory, Hilario Ascasubi Agricultural Experimental Station, National Institute of Agricultural Technology (INTA), Hilario Ascasubi 8142, Argentina
| | | | - Katja Berger
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain
- Mantle Labs GmbH, Grünentorgasse 19/4, 1090 Vienna, Austria
| | - Jochem Verrelst
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain
| | - Jesus Delegido
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain
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12
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Phenotypic Traits Estimation and Preliminary Yield Assessment in Different Phenophases of Wheat Breeding Experiment Based on UAV Multispectral Images. REMOTE SENSING 2022. [DOI: 10.3390/rs14041019] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The utility of unmanned aerial vehicles (UAV) imagery in retrieving phenotypic data to support plant breeding research has been a topic of increasing interest in recent years. The advantages of image-based phenotyping are related to the high spatial and temporal resolution of the retrieved data and the non-destructive and rapid method of data acquisition. This study trains parametric and nonparametric regression models to retrieve leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fractional vegetation cover (fCover), leaf chlorophyll content (LCC), canopy chlorophyll content (CCC), and grain yield (GY) of winter durum wheat breeding experiment from four-bands UAV images. A ground dataset, collected during two field campaigns and complemented with data from a previous study, is used for model development. The dataset is split at random into two parts, one for training and one for testing the models. The tested parametric models use the vegetation index formula and parametric functions. The tested nonparametric models are partial least square regression (PLSR), random forest regression (RFR), support vector regression (SVR), kernel ridge regression (KRR), and Gaussian processes regression (GPR). The retrieved biophysical variables along with traditional phenotypic traits (plant height, yield, and tillering) are analysed for detection of genetic diversity, proximity, and similarity in the studied genotypes. Analysis of variance (ANOVA), Duncan’s multiple range test, correlation analysis, and principal component analysis (PCA) are performed with the phenotypic traits. The parametric and nonparametric models show close results for GY retrieval, with parametric models indicating slightly higher accuracy (R2 = 0.49; MRSE = 0.58 kg/plot; rRMSE = 6.1%). However, the nonparametric model, GPR, computes per pixel uncertainty estimation, making it more appealing for operational use. Furthermore, our results demonstrate that grain filling was better than flowering phenological stage to predict GY. The nonparametric models show better results for biophysical variables retrieval, with GPR presenting the highest prediction performance. Nonetheless, robust models are found only for LAI (R2 = 0.48; MRSE = 0.64; rRMSE = 13.5%) and LCC (R2 = 0.49; MRSE = 31.57 mg m−2; rRMSE = 6.4%) and therefore these are the only remotely sensed phenotypic traits included in the statistical analysis for preliminary assessment of wheat productivity. The results from ANOVA and PCA illustrate that the retrieved remotely sensed phenotypic traits are a valuable addition to the traditional phenotypic traits for plant breeding studies. We believe that these preliminary results could speed up crop improvement programs; however, stronger interdisciplinary research is still needed, as well as uncertainty estimation of the remotely sensed phenotypic traits.
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