Assessments on biomass production by remote sensing

Assessments on biomass production by remote sensing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessments on biomass production by remote sensing Ana Azevedo, Antônio Teixeira, Inajá Sousa, Janice Leivas, Celina Takemura This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6453512/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Biomass production (BIO) and its anomalies were modeled using MODIS satellite images and gridded weather data to test an environmental monitoring system in the biomes Atlantic Forest (AF) and Caatinga (CT) within SEALBA, an agricultural growing region bordered by the states of Sergipe (SE), Alagoas (AL), and Bahia (BA), Northeast Brazil. Spatial and temporal variations on BIO between these biomes were strongly identified, with the annual long-term averages (2007–2023) for AF and CT of 78 ± 22 and 58 ± 17 kg ha − 1 d − 1 , respectively. BIO anomalies were detected through its standardized indexes - STD (BIO STD ), comparing the results for each of the years from 2020 to 2023 with the long-term rates from 2007 to each of these years. The highest negative BIO STD values were in 2023, but concentrated in CT, indicating periods with the lowest vegetation growth, regarding the long-term conditions from 2007 to 2023. The largest positive BIO STD values were for the AF biome in 2022, evidencing the highest vegetative vigor in comparison with the long-term period 2007–2022. The proposed BIO monitoring system is important for environmental policies as they picture suitable periods and places for agricultural and forestry explorations, allowing sustainable managements under climate and land-use changes conditions, with possibilities for replication of the methods in other environmental conditions. Geotechnologies Environmental management Land-use changes Atlantic Forest Caatinga Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6453512","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443503226,"identity":"04c4fe4e-fa03-49a8-ad3c-095348e0ccb1","order_by":0,"name":"Ana Azevedo","email":"","orcid":"","institution":"Universidade Federal de Sergipe","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Azevedo","suffix":""},{"id":443503227,"identity":"293e150d-04f6-4c1f-8e98-263f500df198","order_by":1,"name":"Antônio Teixeira","email":"data:image/png;base64,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","orcid":"","institution":"Universidade Federal de Sergipe","correspondingAuthor":true,"prefix":"","firstName":"Antônio","middleName":"","lastName":"Teixeira","suffix":""},{"id":443503228,"identity":"44fbf86a-f6b0-462d-bc92-88cf53d3daf9","order_by":2,"name":"Inajá Sousa","email":"","orcid":"","institution":"Universidade Federal de Sergipe","correspondingAuthor":false,"prefix":"","firstName":"Inajá","middleName":"","lastName":"Sousa","suffix":""},{"id":443503229,"identity":"2bb8b091-b895-40ca-a980-3612bd113551","order_by":3,"name":"Janice Leivas","email":"","orcid":"","institution":"Brazilian Agricultural Research Corporation","correspondingAuthor":false,"prefix":"","firstName":"Janice","middleName":"","lastName":"Leivas","suffix":""},{"id":443503230,"identity":"0c8b644d-6472-448e-95d8-6f0ebe809e05","order_by":4,"name":"Celina Takemura","email":"","orcid":"","institution":"Brazilian Agricultural Research Corporation","correspondingAuthor":false,"prefix":"","firstName":"Celina","middleName":"","lastName":"Takemura","suffix":""}],"badges":[],"createdAt":"2025-04-15 09:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6453512/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6453512/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80781514,"identity":"3b92aa4d-2f52-4f1f-8792-06d9cc7aa7d3","added_by":"auto","created_at":"2025-04-17 04:43:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3180516,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the SEALBA agricultural growing region in Northeast Brazil involving the states of Sergipe – SE, Alagoas – AL and Bahia – BA (a); weather stations, highlighting altitudes (b); and the Atlantic Forest – AF and Caatinga – CT biomes (c)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6453512/v1/06429a48542d434cb8d867e3.png"},{"id":80781509,"identity":"7e520032-df62-4b1a-9724-64402f3549d7","added_by":"auto","created_at":"2025-04-17 04:43:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":255070,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for modelling biomass production (BIO) and its anomalies through the standardized index - STD (BIO\u003csub\u003eSPD\u003c/sub\u003e) by applying the SAFER and RUE models with gridded data on global solar radiation (R\u003csub\u003eG\u003c/sub\u003e) and mean air temperature (T\u003csub\u003ea\u003c/sub\u003e).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6453512/v1/2ecb96f4a6a8abd09415b174.png"},{"id":80781510,"identity":"17c51b1a-e8a6-4134-bb84-5d5b5adcd992","added_by":"auto","created_at":"2025-04-17 04:43:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1659922,"visible":true,"origin":"","legend":"\u003cp\u003eLong-term average pixel values and standard deviations (SD): (a) precipitation (P), (b) reference evapotranspiration (ET\u003csub\u003e0\u003c/sub\u003e), (c) incident global solar radiation (R\u003csub\u003eG\u003c/sub\u003e) and (e) average air temperature (T\u003csub\u003ea\u003c/sub\u003e), at the 16-day MOD13Q1 product, for the biomes Atlantic Forest (AF) and Caatinga (CT) within SEALBA, in terms of Day of the Year (DOY), regarding the period from 2007 to 2023. Overbars means average pixel values.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6453512/v1/4c4aff5dc59daee407130352.png"},{"id":80781512,"identity":"bb780e8b-636f-422b-93b9-dbd4b6265d3f","added_by":"auto","created_at":"2025-04-17 04:43:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1936552,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution, averages and standard deviations of biomass production (BIO) at the annual scale (a) and for the 16-day MODIS images (b), considering the long-term conditions for the period from 2007 to 2023, in the biomes Atlantic Forest (AF) and Caatinga (C), within the SEALBA agricultural growing region. Overbars means average pixel values.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6453512/v1/c96b595a839997e5e4098f2d.png"},{"id":80781518,"identity":"c167fe55-d247-4230-9640-9f49a18b7e9f","added_by":"auto","created_at":"2025-04-17 04:43:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4298856,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distributions, averages and standard deviations (SD) for the annual values of biomass production (BIO), considering the average conditions for 2020, 2021, 2022 and 2023, in the biomes Atlantic Forest (AF) and Caatinga (CT), within the SEALBA agricultural growing region. Overbars means average pixel values.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6453512/v1/eae9ddbb5f031822b0811f64.png"},{"id":80781519,"identity":"cca553eb-88f6-4f5b-8a0e-a46e5b460c6f","added_by":"auto","created_at":"2025-04-17 04:43:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1721219,"visible":true,"origin":"","legend":"\u003cp\u003ePixel averages and standard deviations (SD) of biomass production (BIO), for the MODIS 16-day periods, during the years 2020 (a), 2021 (b), 2022 (c) and 2023 (d), classifying the biomes Atlantic Forest (AF) and Caatinga (CT) within SEALBA, in terms of Day of the Year (DOY). \u0026nbsp;Overbars means average pixel values.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6453512/v1/86001f7d6de329e1b53c0f84.png"},{"id":80781533,"identity":"8c39b542-d57d-45ea-8c1b-3a259e8d9e50","added_by":"auto","created_at":"2025-04-17 04:43:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3540201,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distributions, averages and standard deviations (SD) for the standardized index (STD) for biomass production (BIO\u003csub\u003eSTD\u003c/sub\u003e) at the annual scale, for the years 2020, 2021, 2022 and 2023, considering the long-term values from 2007 to each of these years, in the biomes of Atlantic Forest (AF) and Caatinga (CT), within the SEALBA agricultural growing region. Overbars means average pixel values.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6453512/v1/54aea02d0de0f85fc2852276.png"},{"id":80781711,"identity":"cad576ea-315b-4678-8926-8b8f4ccc907c","added_by":"auto","created_at":"2025-04-17 04:51:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1325196,"visible":true,"origin":"","legend":"\u003cp\u003ePixel averages of the standardized index (STD) for biomass production (BIO\u003csub\u003eSTD\u003c/sub\u003e) and standard deviations (SD), for the MODIS 16-day periods during the years 2020 (a), 2021 (b), 2022 (c) and 2023 (d), regarding the long-term conditions of 2007-2020, 2007-2021, 2007-2022 and 2007-2023 in the Atlantic Forest (AF) and Caatinga (CT) biomes within SEALBA, in terms of Day of the Year (DOY). Overbars means average pixel values.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6453512/v1/74464c9c36788fe94f8a63db.png"},{"id":81568184,"identity":"6acced72-36b6-4793-94c6-f0298b33a2a2","added_by":"auto","created_at":"2025-04-28 15:46:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3970771,"visible":true,"origin":"","legend":"","description":"","filename":"paperApril2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6453512/v1_covered_2d23dffe-b8c1-45f4-a2d3-1dc414119c1c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessments on biomass production by remote sensing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Geotechnologies, Environmental management, Land-use changes, Atlantic Forest, Caatinga","lastPublishedDoi":"10.21203/rs.3.rs-6453512/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6453512/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiomass production (BIO) and its anomalies were modeled using MODIS satellite images and gridded weather data to test an environmental monitoring system in the biomes Atlantic Forest (AF) and Caatinga (CT) within SEALBA, an agricultural growing region bordered by the states of Sergipe (SE), Alagoas (AL), and Bahia (BA), Northeast Brazil. Spatial and temporal variations on BIO between these biomes were strongly identified, with the annual long-term averages (2007\u0026ndash;2023) for AF and CT of 78\u0026thinsp;\u0026plusmn;\u0026thinsp;22 and 58\u0026thinsp;\u0026plusmn;\u0026thinsp;17 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. BIO anomalies were detected through its standardized indexes - STD (BIO\u003csub\u003eSTD\u003c/sub\u003e), comparing the results for each of the years from 2020 to 2023 with the long-term rates from 2007 to each of these years. The highest negative BIO\u003csub\u003eSTD\u003c/sub\u003e values were in 2023, but concentrated in CT, indicating periods with the lowest vegetation growth, regarding the long-term conditions from 2007 to 2023. The largest positive BIO\u003csub\u003eSTD\u003c/sub\u003e values were for the AF biome in 2022, evidencing the highest vegetative vigor in comparison with the long-term period 2007\u0026ndash;2022. The proposed BIO monitoring system is important for environmental policies as they picture suitable periods and places for agricultural and forestry explorations, allowing sustainable managements under climate and land-use changes conditions, with possibilities for replication of the methods in other environmental conditions.\u003c/p\u003e","manuscriptTitle":"Assessments on biomass production by remote sensing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 04:43:09","doi":"10.21203/rs.3.rs-6453512/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d85dc3b-032e-4aec-9dd0-315dabc1b73b","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-28T15:38:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-17 04:43:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6453512","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6453512","identity":"rs-6453512","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}