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Estimating wheat spike-leaf composite indicator(SLI)dynamics by coupling spectral indices and machine learning

The Crop Journal 2024年第3期12卷 927-937页

作者： Haiyu Tao Ruiheng Zhou Yining Tang Wanyu Li Xia Yao Tao Cheng Yan Zhu Weixing Cao Yongchao Tian National Engineering and Technology Center for Information Agriculture Key Laboratory of Crop System Analysis and Decision MakingMinistry of Agriculture and Rural AffairsJiangsu Key Laboratory for Information AgricultureNanjing Agricultural UniversityNanjing 210095JiangsuChina

The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis.Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms.The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index.ETVI3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike emergence.Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,respectively.These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY.This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

关键词： Wheat spike photosynthesis Yield-related phenotypic trait spectral indices Machine learning Estimation

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Detection of Burned Areas through spectral indices Analysis of Sentinel-2A Satellite Images in the Abokouamékro Wildlife Reserve (Central, Côte D’Ivoire)

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Open Journal of Applied Sciences 2024年第1期14卷 205-222页

作者： Bob Kouakou Kouadio Sié Ouattara Alain Clément Jean-Marc Gala Bi Zaouri Jean-Luc Kouadio Kouassi Jean-Luc Edouard Kouakou N’guessan Laboratoire des Sciences et Technologies de la Communication et de l’Information (LSTCI) Institut National Polytechnique Houphou&#235t Boigny (INP-HB) Yamoussoukro C&#244te d’Ivoire Laboratoire des Sciences Agronomiques et Génie Rural Institut National Polytechnique Houphou&#235t Boigny (INPHB) Yamoussoukro C&#244te d’Ivoire Ecole de Géomatique et du Territoire (EGT) Abidjan C&#244te d’Ivoire LARIS SFR MATHSTIC Université d’Angers Angers France Centre Universitaire de Recherche et d’application en Télédétection (CURAT) UFR des Sciences de la Terre et des Ressources Minières Abidjan C&#244te d’Ivoire Laboratoire de Botanique UFR Biosciences Université Félix Houphou&#235t Boigny Abidjan C&#244te d’Ivoire

In Côte d’Ivoire, the recurring and unregulated use of bushfires, which cause ecological damage, presents a pressing concern for the custodians of protected areas. This study aims to enhance our comprehension of the dynamics of burnt areas within the Abokouamékro Wildlife Reserve (AWR) by employing the analysis of spectral indices derived from satellite imagery. The research methodology began with the calculation of mean indices and their corresponding spectral sub-indices, including NDVI, SAVI, NDWI, NDMI, BAI, NBR, TCW, TCG, and TCB, utilizing data from the Sentinel-2A satellite image dated January 17, 2022. Subsequently, a fuzzy classification model was applied to these various indices and sub-indices, guided by the degree of membership α, with the goal of effectively distinguishing between burned and unburned areas. Following the classification, the accuracies of the classified indices and sub-indices were validated using the coordinates of 100 data points collected within the AWR through GPS technology. The results revealed that the overall accuracy of all indices and sub-indices declines as the degree of membership α decreases from 1 to 0. Among the mean spectral indices, NDVI-mean, SAVI-mean, NDMI-mean exhibited the highest overall accuracies, achieving 97%, 95%, and 90%, respectively. These results closely mirrored those obtained by sub-indices using band 8 (NDVI-B8, SAVI-B8, and NDMI-B8), which yield respective overall accuracies of 93%, 92%, and 89%. At a degree of membership α = 1, the estimated burned areas for the most effective indices encompassed 2144.38 hectares for NDVI-mean, 1932.14 hectares for mean SAVI-mean, and 4947.13 hectares for mean NDMI-mean. A prospective approach involving the amalgamation of these three indices could have the potential to yield improved outcomes. This study could be a substantial contribution to the discrimination of bushfires in Côte d’Ivoire.

关键词： spectral indices Wildfire Burned Areas Abokouamékro Wildlife Reserve Côte D’Ivoire

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Assessing fire severity in Turkey's forest ecosystems using spectral indices from satellite images

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林业研究:英文版 2023年第6期34卷 1747-1761页

作者： Coşkun Okan Güney Ahmet Mert Serkan Gülsoy Department of Forest Fires Aegean Forestry Research Institute35430 IzmirTurkey Department of Wildlife Ecology and Management Faculty of ForestryIsparta University of Applied Science32260 IspartaTurkey Department of Soil Science and Ecology Faculty of ForestryIsparta University of Applied Science32260 IspartaTurkey

Fire severity classifications determine fire damage and regeneration potential in post-fire areas for effective implementation of restoration applications.Since fire damage varies according to vegetation and fire characteristics,regional assessment of fire severity is crucial.The objectives of this study were:(1)to test the performance of different satellite imagery and spectral indices,and two field—measured severity indices,CBI(Composite Burn Index)and GeoCBI(Geometrically structured Composite Burn Index)to assess fire severity;(2)to calculate classification thresholds for spectral indices that performed best in the study areas;and(3)to generate fire severity maps that could be used to determine the ecological impact of forest fires.Five large fires in Pinus brutia(Turkish pine)and Pinus nigra subsp.pallasiana var.pallasiana(Anatolian black pine)—dominated forests during 2020 and 2021 were selected as study sites.The results show that GeoCBI provided more reliable estimates of field—measured fire severity than CBI.While Sentinel-2 and Landsat-8/OLI images performed similarly well,MODIS performed poorly.Fire severity classification thresholds were determined for Sentinel-2 based RdNBR,dNBR,dSAVI,dNDVI,and dNDMI and Landsat-8/OLI based dNBR,dNDVI,and dSAVI.Among several spectral indices,the highest accuracy for fire severity classification was found for Sentinel-2 based RdNBR(72.1%)and Landsat-8/OLI based dNBR(69.2%).The results can be used to assess and map fire severity in forest ecosystems similar to those in this study.

关键词： Remote sensing Forest fire Fire severity spectral indices Composite burn index

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An examination of thematic research,development,and trends in remote sensing applied to conservation agriculture

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International Soil and Water Conservation Research 2024年第1期12卷 77-95页

作者： Zobaer Ahmed Aaron Shew Lawton Nalley Michael Popp V.Steven Green Kristofor Brye Center for Advanced Spatial Technologies University of ArkansasFayettevilleARUSA Environmental Dynamics Program University of ArkansasFayettevilleARUSA Department of Agricultural Economics and Agribusiness University of ArkansasFayettevilleARUSA College of Agriculture Arkansas State UniversityJonesboroARUSA University of Arkansas System Division of Agriculture Little RockARUSA Department of Crop Soiland Environmental SciencesUniversity of ArkansasFayettevilleARUSA

Conservation agriculture seeks to reduce environmental degradation through sustainable management of agricultural land.Since the 1990s,agricultural research has been conducted using remote sensing technologies;however,few previous reviews have been conducted focused on different conservation management practices.Most of the previous literature has focused on the application of remote sensing in agriculture without focusing exclusively on conservation practices,with some only providing a narrative review,others using biophysical remote sensing for quantitative estimates of the bio-geo-chemical-physical properties of soils and crops,and few others focused on single agricultural management practices.This paper used the preferred reporting items for systematic review(PRISMA)methodology to examine the last 30 years of thematic research,development,and trends associated with remote sensing technologies and methods applied to conservation agriculture research at various spatial and temporal scales.A set of predefined key concepts and keywords were applied in three databases:Scopus,Web of Science,and Google Scholar.A total of 188 articles were compiled for initial examination,where 68 articles were selected for final analysis and grouped into cover crops,crop residue,crop rotation,mulching,and tillage practices.Publications on conservation agriculture research using remote sensing have been increasing since 1991 and peaked at 10 publications in 2020.Among the 68 articles,94%used a pixel-based,while only 6%used an object-based classification method.Prior to 2005,tillage practices were abundantly studied,then crop residue was a focused theme between 2004 and 2012.From 2012 to 2020,the focus shifted again to cover crops.Ten spectral indices were used in 76%of the 68 studies.This examination offered a summary of the new potential and identifies crucial future research needs and directions that could improve the contribution of remote sensing to the provision of long-term operational services for various conservation agricult

关键词： Remote sensing Conservation agriculture Classification algorithm Spatial resolution Satellite spectral indices PRISMA

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Quantitative estimation of photosynthetic pigments using new spectral indices

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Journal of Forestry Research 2013年第3期24卷 477-483页

作者： XIONG Ying LI Ru YUE Yue-min Department of Resources and Environmental Sciences Changsha University of Science and Technology Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Institute of Subtropical Agriculture Chinese Academy of Sciences

Foliar chlorophylls are the most important pigments related to the physiological function of plants. Quantitative estimation of photosynthetic pigments can provide important information about relationships between plants and their environmental conditions. In this study, new spectral indices were designed to enhance spectral resistance to noise, using the area of the spectral curve and axis. The specific area was around the red edge （Rdaa）, instead of the sum of the first derivative of the spectrum, specifically the area of red edge （Rda）. Meanwhile, three reference indices were also introduced as non-sensitive bands of chlorophylls. The results show that by dividing spectral references, a kind of re-projection, the spectral indices can be calibrated to allow direct and reasonable comparisons of the results. The sensitivity of these reference indices to chlorophylls was also evaluated in this study. The regression results show that Rdaa and its derivates are highly related to chlorophylls and resistant to noise.

关键词： chlorophyll spectral indices red edge indicator paddy rice

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An automatic procedure for generating burn severity maps from the satellite images-derived spectral indices

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International Journal of Digital Earth 2021年第11期14卷 1659-1673页

作者： Saeid Gholinejad Elahe Khesali Department of Geomatics Engineering Faculty of Civil Engineering and TransportationUniversity of IsfahanIsfahanIran Remote Sensing&Photogrametry Department Faculty of Geodesy and Geomatics EngineeringK.N.Toosi University of TechnologyTehranIran

Fire,especially wildfire,which can be considered as one of the main threats to vegetation cover and animals’life,has attracted lots of attention from environmental researchers.To better manage the fire crisis and take the necessary measures to compensate for its damages,it is essential to have detailed information about the burn severity levels.Accordingly,satellite images and their spectral indices have been widely considered in the literature as powerful tools in producing burn severity information.Despite the efficiency of the previously proposed methods,the necessity of ground reference data for their thresholding step faces them with serious challenges.To address this problem,in this study,an automatic procedure based on the change-point analysis is presented for thresholding differenced normalized burn ratio(dNBR)and its another version,dNBR2.In this procedure,a mean-shift based change-point analysis is performed on the dNBR and dNBR2 images for classifying them into burn severity levels.Experiments,conducted on some parts of Alaska and California in the United States,illustrated the high efficiency of the proposed method.Moreover,as an applied experiment,the severity of the fires,occurred in 2020 in the Khaeiz protected area in Iran,was estimated and compared with local reports.

关键词： Burn severity mapping spectral indices changepoint analysis normalized burn ratio(NBR)

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Mapping rapeseed planting areas using an automatic phenology-and pixel-based algorithm(APPA) in Google Earth Engine

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The Crop Journal 2022年第5期10卷 1483-1495页

作者： Jichong Han Zhao Zhang Juan Cao Yuchuan Luo Academy of Disaster Reduction and Emergency Management Ministry of Emergency Management&Ministry of Education School of National Safety and Emergency ManagementBeijing Normal UniversityBeijing 100875China

The timely and rapid mapping of rapeseed planting areas is desirable for national food security. Most current rapeseed mapping methods depend strongly on images with good observations obtained during the flowering stages. Although vegetation indices have been proposed to identify the rapeseed flowering stage in some areas, automatically mapping rapeseed planting areas in large regions is still challenging.We developed an automatic phenology-and pixel-based algorithm(APPA) by integrating Landsat 8 and Sentinel-1 satellite data. We found that the Normalized Rapeseed Flowering Index shows unique spectral characteristics during the flowering and post-flowering periods, which distinguish rapeseed parcels from other land-use types(urban, water, forest, grass, maize, wheat, barley, and soybean). To verify the robustness of APPA, we applied APPA to seven areas in five rapeseed-producing countries with flowering images unavailable. The rapeseed maps by APPA showed consistently high accuracies with producer accuracies of 0.87–0.93 and F-scores of 0.92–0.95 based on 4503 verification samples. They showed high spatial consistency at the pixel level with the land cover Scientific Expertise Centres(SEC) map in France,Crop Map of England in United Kingdom, national-scale crop-and land-cover map of Germany, and Annual Crop Inventory in Canada at the pixel level. We propose APPA as a highly promising method for automatically and efficiently mapping rapeseed areas.

关键词： Automatic mapping spectral indices Polarization Phenology Rapeseed

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Estimation of rock Fe content based on hyperspectral indices

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Journal of Arid Land 2021年第12期13卷 1287-1298页

作者： WANG Jinlin WANG Wei CHENG Yinyi ZHANG Zhixin WANG Shanshan ZHOU Kefa LI Pingheng State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqi 830011China Xinjiang Key Laboratory of Mineral Resources and Digital Geology Urumqi 830011China Xinjiang Research Centre for Mineral Resources Chinese Academy of SciencesUrumqi 830011China University of Chinese Academy of Sciences Beijing 100049China Zhejiang A&F University Hangzhou 311300China

Information on the Fe content of bare rocks is needed for implementing geochemical processes and identifying mines.However,the influence of Fe content on the spectra of bare rocks has not been thoroughly analyzed in previous studies.The Saur Mountain region within the Hoboksar of the Russell Hill depression was selected as the study area.Specifically,we analyzed six hyperspectral indices related to rock Fe content based on laboratory measurements(Dataset I)and field measurements(Dataset II).In situ field measurements were acquired to verify the laboratory measurements.Fe content of the rock samples collected from different fresh and weathered rock surfaces were divided into six levels to reveal the spatial distributions of Fe content of these samples.In addition,we clearly displayed wavelengths with obvious characteristics by analyzing the spectra of these samples.The results of this work indicated that Fe content estimation models based on the fresh rock surface measurements in the laboratory can be applied to in situ field or satellite-based measurements of Fe content of the weathered rock surfaces.It is not the best way to use only the single wavelengths reflectance at all absorption wavelengths or the depth of these absorption features to estimate Fe content.Based on sample data analysis,the comparison with other indices revealed that the performance of the modified normalized difference index is the best indicator for estimating rock Fe content,with R2 values of 0.45 and 0.40 corresponding to datasets I and II,respectively.Hence,the modified normalized difference index(the wavelengths of 2220,2290,and 2370 nm)identified in this study could contribute considerably to improve the identification accuracy of rock Fe content in the bare rock areas.The method proposed in this study can obviously provide an efficient solution for large-scale rock Fe content measurements in the field.

关键词： bare rocks Fe content reflectance spectral indices modified normalized difference index Saur Mountain

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Evaluation of high yielding soybean germplasm under water limitation

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Journal of Integrative Plant Biology 2016年第5期58卷 475-491页

作者： Silvas J.Prince Mackensie Murphy Raymond N.Mutava Zhengzhi Zhang Na Nguyen Yoon Ha Kim Safiullah M.Pathan Grover J.Shannon Babu Valliyodan Henry T.Nguyen National Center for Soybean Biotechnology and Division of Plant Sciences University of Missouri

Limited information is available for soybean root traits and their plasticity under drought stress. To date, no studies have focused on examining diverse soybean germ- plasm for regulation of shoot and root response under water limited conditions across varying soil types. In this study, 17 genetically diverse soybean germplasm lines were selected to study root response to water limited conditions in clay （trial 1） and sandy soil （trial 2） in two target environments. Physiological data on shoot traits was measured at multiple crop stages ranging from early vegetative to pod filling. The phenotypic root traits, and biomass accumulation data are collected at pod filling stage. In trial 1, the number of lateral roots and forks were positively correlated with plot yield under water limitation and in trial 2, lateral root thickness was positively correlated with the hill plot yield. Plant Introduction （PI） 578477A and 088444 were found to have higher later root number and forks in clay soil with higher yield under water limitation, in sandy soil, P1458o2o was found to have a thicker lateral root system and higher yield under water limitation. The genotypes identified in this study could be used to enhance drought tolerance of elite soybean cultivars through improved root traits specific to target environments.

关键词： Fibrous root root angle root plasticity root systemarchitecture （RSA） root thickness spectral indices soybean spectralindices total root length water limitation

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Anomalous behaviour detection using one-class support vector machine and remote sensing images: a case study of algal bloom occurrence in inland waters

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International Journal of Digital Earth 2021年第7期14卷 921-942页

作者： Pedro Henrique Moraes Ananias Rogério Galante Negri Graduate Program in Natural Disasters UNESP/CEMADENSão Josédos CamposSão PauloBrazil Sciences Technology Institute São Paulo State University(UNESP)São Josédos CamposSão PauloBrazil

Algal blooms are a frequent subject in scientific discussions and are the focus of many recent studies,mainly due to their adverse effect on society.Given the lack of ground truth data and the need to develop tools for their detection and monitoring,this research proposes a novel method to automate detection.Concepts derived from multi-temporal image series processing,spectral indices and classification with Oneclass Support Vector Machine(OC-SVM)are used in this proposal.Imagery from multi-spectral sensors on Landsat-8 and MODIS were acquired through the Google Earth Engine API(GEE API).In order to evaluate our method,two bloom detection case studies(Lake Erie(USA)and Lake Taihu(China))were performed.Comparisons were made with methods based on spectral index thresholds.Also,to demonstrate the performance of the OC-SVM classifier compared to other machine learning methods,the proposal was adapted to be used with a Random Forest(RF)classifier,having its results added to the analysis.In situ measurements show that the proposed method delivers highly accurate results compared to spectral index thresholding approaches.However,a drawback of the proposal refers to its higher computational cost.The application of the new method to a real-world bloom case is demonstrated.

关键词： Remote sensing spectral indices unsupervised classification anomalies algal bloom detection

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