Modelos para la estimación del rendimiento de la caña de azúcar en Costa Rica con datos de campo e índices de vegetación

Bryan Alemán-Montes; Pere Serra; Alaitz Zabala

doi:10.4995/raet.2023.18705

Authors

Bryan Alemán-Montes Universidad de Costa Rica https://orcid.org/0000-0003-4349-2255
Pere Serra Universidad Autónoma de Barcelona https://orcid.org/0000-0003-1023-5586
Alaitz Zabala Universidad Autónoma de Barcelona https://orcid.org/0000-0002-3931-4221

DOI:

https://doi.org/10.4995/raet.2023.18705

Keywords:

Sugarcane, vegetation indexes, linear regression, Sentinel-2, Landsat-8

Abstract

Remote sensing offers important inputs for sugarcane yield estimation, since its temporal and spatial approaches allow monitoring the phenological cycle of the crop. The objective of this research was to apply an operational method for the estimation of sugarcane yield and sugar content through the combination of field variables with vegetation indices, calculated with the satellite sensors on board Sentinel-2 and Landsat-8 in a cooperative from Costa Rica. In addition, historical harvest data and start months of phenological cycle were used to estimate sugarcane yield and sugar content per ton using multiple linear regressions. The integration of historical data and Simple Ratio (SR) vegetation index, calculated in different steps of the phenological cycle (in the months of September, December and January), allowed us to obtain an estimation model of sugarcane yield (tons of sugarcane per hectare) with a regression coefficient (R2) of 0.64 and a RMSE of 8.0 tons/ha. While for sugar content (kilograms of refined sugar per ton) we obtained a R2 of 0.59 integrating historical variables and the vegetation indexes SR and Green Normalized Difference Vegetation Index (GNDVI); in this case the RMSE was 4.9 kg/tons. Ultimately, this operational method of yield estimation provides tools for decision making before, during and after the harvest stage.

Downloads

Download data is not yet available.

Author Biographies

Bryan Alemán-Montes, Universidad de Costa Rica

Centro de Investigaciones Agronómicas. Universidad de Costa Rica

Grupo de Investigación Grumets, Departamento de Geografía. Universidad Autónoma de Barcelona

Pere Serra, Universidad Autónoma de Barcelona

Grupo de Investigación Grumets, Departamento de Geografía

Alaitz Zabala, Universidad Autónoma de Barcelona

Grupo de Investigación Grumets, Departamento de Geografía

References

Abdel-Rahman, E.M., Ahmed, F.B. 2008. The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: A review of the literature. International Journal of Remote Sensing, 29(13), 3753-3767. https://doi.org/10.1080/01431160701874603

Abebe, G., Tadesse, T., Gessesse, B. 2022. Combined Use of Landsat 8 and Sentinel 2A Imagery for Improved Sugarcane Yield Estimation in Wonji-Shoa, Ethiopia. Journal of the Indian Society of Remote Sensing, 50, 143-157. https://doi.org/10.1007/s12524-021-01466-8

Alemán-Montes, B., Henríquez-Henríquez, C., Ramírez-Rodríguez, T., Largaespada-Zelaya, K. 2021. Estimación de rendimiento en el cultivo de caña de azúcar (Saccharum officinarum) a partir de fotogrametría con vehículos aéreos no tripulados (VANT). Agronomía Costarricense, 45(1), 67-80. https://doi.org/10.15517/rac.v45i1.45695

Alemán, B., Serra, P., Zabala, A. 2022. Estimación del rendimiento de la caña de azúcar en Costa Rica con datos de campo e índices de vegetación. En L.Á. Ruiz Fernández, J. Estornell Cremades, M. González de Audícana Amenábar, J. Álvarez Mozos (Ed.), XIX Congreso de la Asociación Española de Teledetección, 27-30.

Allison, J.C.S., Pammenter, N.W., Haslam, R.J. 2007. Why does sugarcane (Saccharum sp. hybrid) grow slowly? South African Journal of Botany, 73(4), 546-551. https://doi.org/10.1016/j.sajb.2007.04.065

Bégué, A., Lebourgeois, V., Bappel, E., Todoroff, P., Pellegrino, A., Baillarin, F., Siegmund, B. 2010. Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI. International Journal of Remote Sensing, 31(20), 5391-5407. https://doi.org/10.1080/01431160903349057

Canata, T.F., Wei, M.C.F., Maldaner, L.F., Molin, J.P. 2021. Sugarcane yield mapping using highresolution imagery data and machine learning technique. Remote Sensing, 13(2), 1-14. https://doi.org/10.3390/rs13020232

Chaves, M., Bermúdez, L. 2015. Agroindustria azucarera costarricense: un modelo organizacional y productivo efectivo con 75 años de vigencia Introducción. En Departamento de investigación y extensión de la caña de azúcar (DIECA).

Chaves, M., Picoli, M., Sanches, I. 2020. Recent applications of Landsat 8/OLI and Sentinel-2/ MSI for land use and land cover mapping: A systematic review. Remote Sensing, 12(18). https://doi.org/10.3390/rs12183062

Cock, J.H. 2003. Sugarcane growth and development. International Sugar Journal, 105(1259), 540-552.

Dubey, S.K., Gavli, A.S., Yadav, S.K., Sehgal, S., Ray, S.S. 2018. Remote Sensing-Based Yield Forecasting for Sugarcane (Saccharum officinarum L.) Crop in India. Journal of the Indian Society of Remote Sensing, 46(11), 1823-1833. https://doi.org/10.1007/s12524-018-0839-2

ESA, (European Space Agency). 2021. S2 MPC Sen2Cor Software.

Escadafal, R., Huete, A. 1991. Étude Des Propriétés Spectrales Des Sols Arides Appliquée à Lamélioration Des Indices de Vegetation Obtenus Par Télédection. CR Académie des Sciences de Paris, 312(2), 1385-1391. http://www.scopus.com/inward/record.url?eid=2-s2.0-0026305591&partnerID=40&md5=1a9a77276f4613b8eec010a111f41ff0

Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N. 1996. Use of a green channel in remote sensing of global vegetation from EOS- MODIS. Remote Sensing of Environment, 58(3), 289-298. https://doi.org/10.1016/S0034-4257(96)00072-7

Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2-3), 416-426. https://doi.org/10.1016/S0034-4257(02)00018-4

Huete, A. 1988. A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 25(1), 295-309. https://doi.org/10.1016/0034-4257(88)90106-X

Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(12), 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2

IMN, (Instituto meteorológico Nacional de Costa Rica). 2008. Atlas climático de Costa Rica. https://www.imn.ac.cr/atlas-climatologico

INEC (Instituto Nacional de Estadística y Censos). 2020. Encuesta Nacional Agropecuaria 2019: Resultados Generales de la Actividad Agrícola y Forestal. https://inec.cr/estadisticas-fuentes/encuestas/encuesta-nacional-agropecuaria?page=7

Inman-Bamber, N.G. 1994. Temperature and seasonal effects on canopy development and light interception of sugarcane. Field Crops Research, 36(1), 41-51. https://doi.org/10.1016/0378-4290(94)90051-5

James, G., Witten, D., Trevor, H., Tibshirani, R. 2013. An Introduction to Statistical Learning - with Applications in R, Gareth James, Springer. https://doi.org/10.1007/978-1-4614-7138-7

Jiménez-Jiménez, S.I., Marcial-Pablo, M. de J., Ojeda-Bustamante, W., Sifuentes-Ibarra, E., Inzunza-Ibarra, M.A., Sánchez-Cohen, I. 2022. VICAL: Global Calculator to Estimate Vegetation Indices for Agricultural Areas with Landsat and Sentinel-2 Data. Agronomy, 12(7). https://doi.org/10.3390/agronomy12071518

Jordan, C.F. 1969. Derivation of Leaf-Area Index from Quality of Light on the Forest Floor. Ecology, 50(4), 663-666. https://doi.org/10.2307/1936256

Krupavathi, K., Raghubabu, M., Mani, A., Parasad, P.R.K., Edukondalu, L. 2022. Field-Scale Estimation and Comparison of the Sugarcane Yield from Remote Sensing Data: A Machine Learning Approach. Journal of the Indian Society of Remote Sensing, 50(2), 299-312. https://doi.org/10.1007/s12524-021-01448-w

Li, J., Lu, X., Cheng, K., Liu, W. 2020. Regression and Time Series Model Selection. Regression and Time Series Model Selection, 1968. https://doi.org/10.1142/3573

dos Santos Luciano, A.C., Picoli, M.C.A., Duft, D.G., Rocha, J.V., Leal, M.R.L.V., le Maire, G. 2021. Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm. Computers and Electronics in Agriculture, 184, 106063. https://doi.org/10.1016/j.compag.2021.106063

Mata, R., Rosales, A., Sandoval, Da., Vindas, E., Alemán, B. 2020. Subórdenes de suelos de Costa Rica [mapa digital]. Escala 1:200000. http://www.cia.ucr.ac.cr/es/mapa-de-suelos-de-costa-rica

Max, A., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Ziem, A., Scrucca, L., Hunt, T., Kuhn, M.M. 2020. Package 'caret ' R.

Morel, J., Todoroff, P., Bégué, A., Bury, A., Martiné, J.F., Petit, M. 2014. Toward a satellite-based system of sugarcane yield estimation and forecasting in smallholder farming conditions: A case study on reunion island. Remote Sensing, 6(7), 6620-6635. https://doi.org/10.3390/rs6076620

Narmilan, A., Gonzalez, F., Salgadoe, A.S.A., Kumarasiri, U.W.L.M., Weerasinghe, H.A.S., Kulasekara, B.R. 2022. Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery. Remote Sensing, 14(5), 1140. https://doi.org/10.3390/rs14051140

Panigrahy, S., Sharma, S.A. 1997. Mapping of crop rotation using multidate Indian Remote Sensing Satellite digital data. ISPRS Journal of Photogrammetry and Remote Sensing, 52(2), 85-91. https://doi.org/10.1016/S0924-2716(97)83003-1

Pearson, R.L., Miller, L.D. 1972. Remote Mapping of Standing Crop Biomass for Estimation of the Productivity of Shortgrass Prairie, Pawnee National Grasslands, Colorado. Proceedings of the 8th International Symposium on Remote Sensing of the Environment.

Piekutowska, M., Niedbała, G., Piskier, T., Lenartowicz, T., Pilarski, K., Wojciechowski, T., Pilarska, A.A., Czechowska-Kosacka, A. 2021. The application of multiple linear regression and artificial neural network models for yield prediction of very early potato cultivars before harvest. Agronomy, 11(5), 885. https://doi.org/10.3390/agronomy11050885

Rahman, M.M., Robson, A. 2020. Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level. Remote Sensing, 12(8), 1313. https://doi.org/10.3390/rs12081313

Rahman, M.M., Robson, A.J. 2016. A Novel Approach for Sugarcane Yield Prediction Using Landsat Time Series Imagery: A Case Study on Bundaberg Region. Advances in Remote Sensing, 5, 93-102. https://doi.org/10.4236/ars.2016.52008

Rao, P.V.K., Rao, V.V., Venkataratnam, L. 2002. Remote sensing: A technology for assessment of sugarcane crop acreage and yield. Sugar Tech, 4(3-4), 97-101. https://doi.org/10.1007/BF02942689

Richardson, A.J., Wiegand, C.L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12), 1541-1552.

Rouse, J.W., Hass, R.H., Schell, J.A., Deering, D.W. 1973. Monitoring vegetation systems in the great plains with ERTS. Third Earth Resources Technology Satellite (ERTS) symposium, 351, 309-317. https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19740022614.pdf

Rudorff, B.F.T., Batista, G.T. 1990. Yield estimation of sugarcane based on agrometeorological-spectral models. Remote Sensing of Environment, 33(3), 183-192. https://doi.org/10.1016/0034-4257(90)90029-L

Saez, J.V. 2017. Dinámica de acumulación de sacarosa en tallos de caña de azúcar (Saccharum spp.) modulada por cambios en la relación fuente-destino. Universidad Nacional de Cordoba.

Shendryk, Y., Davy, R., Thorburn, P. 2021. Integrating satellite imagery and environmental data to predict field-level cane and sugar yields in Australia using machine learning. Field Crops Research, 260(October 2020), 107984. https://doi.org/10.1016/j.fcr.2020.107984

Simões, M. dos S., Rocha, J.V., Lamparelli, R.A.C. 2005. Variáveis espectrais e indicadores de desenvolvimento e produtividade da canadeaçúcar. Scientia Agricola, 62(3), 199–207. https://doi.org/10.1590/S0103-90162005000300001

Sishodia, R.P., Ray, R.L., Singh, S.K. 2020. Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 1–31. https://doi.org/10.3390/rs12193136

Som-Ard, J., Atzberger, C., Izquierdo-Verdiguier, E., Vuolo, F., Immitzer, M. 2021. Remote sensing applications in sugarcane cultivation: A review. Remote Sensing, 13(20), 1-46. https://doi.org/10.3390/rs13204040

USGS, (United States Geological Survey). 2022. Landsat 8-9 Collection 2 (C2) Level 2 Science Product ( L2SP ) Guide. En USGS (Vol. 2, Número March).

Zhao, Y., Justina, D. Della, Kazama, Y., Rocha, J.V., Graziano, P.S., Lamparelli, R.A.C. 2016. Dynamics modeling for sugar cane sucrose estimation using time series satellite imagery. En C.M.U. Neale y A. Maltese (Eds.), Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII (Vol. 9998, p. 99980J). https://doi.org/10.1117/12.2242490

Zumo, I.M., Hashim, M. 2020. Mapping Seasonal Variations of Grazing Land Above-ground Biomass with Sentinel 2A Satellite Data. IOP Conference Series: Earth and Environmental Science, 540(1). https://doi.org/10.1088/1755-1315/540/1/012061