| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21c.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34R/44LJCNE | |||
| Repository | sid.inpe.br/mtc-m21c/2021/05.10.16.05 (restricted access) | |||
| Last Update | 2021:05.10.16.05.39 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21c/2021/05.10.16.05.39 | |||
| Metadata Last Update | 2022:04.03.22.28.40 (UTC) administrator | |||
| DOI | 10.1016/j.compag.2021.106063 | |||
| ISSN | 0168-1699 | |||
| Citation Key | LucianoPiDuRoLeMa:2021:EmMoFo | |||
| Title | Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm  | |||
| Year | 2021 | |||
| Month | May | |||
| Access Date | 2024, May 19 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 4765 KiB | |||
| 2. Context | ||||
| Author | 1 Luciano, Ana Cláudia dos Santos 2 Picoli, Michelle Cristina Araújo 3 Duft, Daniel Garbellini 4 Rocha, Jansle Vieira 5 Leal, Manoel Regis Lima Verde 6 le Maire, Guerric | |||
| Group | 1 2 DIOTG-CGCT-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 Universidade de São Paulo (USP) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Universidade de São Paulo (USP) 4 Universidade Estadual de Campinas (UNICAMP) 5 Universidade Estadual de Campinas (UNICAMP) 6 Eco&Sols, Univ Montpellier, CIRAD, INRA, IRD | |||
| Author e-Mail Address | 1 analuciano@usp.br 2 michelle.picoli@inpe.br 3 daniel.duft@usp.br 4 jansle@unicamp.br 5 regis.leal@lnbr.cnpem.br 6 guerric.le_maire@cirad.fr | |||
| Journal | Computers and Electronics in Agriculture | |||
| Volume | 184 | |||
| Pages | e106063 | |||
| Secondary Mark | A1_ENGENHARIAS_III A2_INTERDISCIPLINAR A2_GEOCIÊNCIAS A2_CIÊNCIAS_AGRÁRIAS_I A2_CIÊNCIA_DA_COMPUTAÇÃO B1_MEDICINA_II B1_MEDICINA_I B1_ENGENHARIAS_IV B1_ENGENHARIAS_II B1_CIÊNCIA_DE_ALIMENTOS B2_QUÍMICA B2_CIÊNCIAS_BIOLÓGICAS_II B3_ASTRONOMIA_/_FÍSICA C_CIÊNCIAS_AMBIENTAIS | |||
| History (UTC) | 2021-05-10 16:05:39 :: simone -> administrator :: 2021-05-10 16:05:40 :: administrator -> simone :: 2021 2021-05-10 16:06:45 :: simone -> administrator :: 2021 2022-04-03 22:28:40 :: administrator -> simone :: 2021 | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Content Type | External Contribution | |||
| Version Type | publisher | |||
| Keywords | Crop yield Remote sensing Vegetation indices Machine learning | |||
| Abstract | Sugarcane plays an important role in food and energy production in Brazil and worldwide. The large availability of satellite sensors and advanced techniques for processing data have improved the forecasting sugarcane yield on a local and global scale, but more work is needed on exploiting the synergy between remote sensing, meteorological and agronomic data. In this study, we combined such data sources to forecast sugarcane yield using a random forest (RF) algorithm on an extensive area of 50,000 ha, over four years. Images from Landsat satellites were processed to time series of surface reflectance and spectral indices. The approach focused on the development of predictive models which only used data acquired and accessible several months before the harvest. First, three RF models were calibrated with different predictors to forecast the sugarcane yield at harvest: using Landsat satellite images and meteorological data (RF1); agronomic and meteorological data (RF2); a combination of Landsat satellite images, agronomic and meteorological data (RF3). As a comparison, we also tested the influence of including knowledge on the future harvest date in the models RF2 and RF3 (RF4 and RF5). The average values of R2 for RF1, RF2, and RF3 were 0.66, 0.50 and 0.74, respectively. The model with the highest values of R2 (RF3) had a Root Mean Square Error (RMSE) of 9.9 ton ha−1 on yield forecast, approximately 15% of the yield average. Including the harvest date improved the RF2 and RF3 models to reach R2 = 0.69 and RMSE = 10.8 ton ha−1 for RF4, and R2 = 0.76 and RMSE of 9.4 ton ha−1 for RF5. A blind forecasting test for the 2016 yields showed similar prediction than the forecast made by in situ field expertise. This result has the potential to assist management of sugarcane production. | |||
| Area | SRE | |||
| Arrangement | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Empirical model for... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| Language | en | |||
| Target File | 1-s2.0-S0168169921000818-main.pdf | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Archiving Policy | denypublisher denyfinaldraft24 | |||
| Read Permission | deny from all and allow from 150.163 | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Next Higher Units | 8JMKD3MGPCW/46KUATE | |||
| Host Collection | urlib.net/www/2017/11.22.19.04 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||