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Synergistic integration of optical and microwave satellite data for crop yield estimation

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Synergistic integration of optical and microwave satellite data for crop yield estimation

dc.contributor.author	Mateo-Sanchis, Anna
dc.contributor.author	Piles Guillem, Maria
dc.contributor.author	Muñoz Marí, Jordi
dc.contributor.author	Adsuara Fuster, José Enrique
dc.contributor.author	Pérez Suay, Adrián
dc.contributor.author	Camps-Valls, Gustau
dc.date.accessioned	2020-12-09T15:11:32Z
dc.date.available	2020-12-09T15:11:32Z
dc.date.issued	2019
dc.identifier.citation	Mateo-Sanchis, Anna Piles Guillem, Maria Muñoz Marí, Jordi Adsuara Fuster, Jose Enrique Pérez Suay, Adrián Camps Valls, Gustavo 2019 Synergistic integration of optical and microwave satellite data for crop yield estimation Remote Sensing of Environment 234 111460
dc.identifier.uri	https://hdl.handle.net/10550/76591
dc.description.abstract	Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regressionand its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment. Results show that (1) the proposed EVI-VOD lag metric correlates well with crop yield and outperforms common single-sensor metrics for crop yield estimation; (2) the statistical (machine learning) models working directly with the time series largely improve results compared to previously reported estimations; (3) the combined exploitation of information from the optical and microwave data leads to improved predictions over the use of single sensor approaches with coefficient of determination R 2 ≥ 0.76; (4) when models are used for within-season forecasting with limited time information, crop yield prediction is feasible up to four months before harvest (models reach a plateau in accuracy); and (5) the robustness of the approach is confirmed in a multi-year setting, reaching similar performances than when using single-year data. In conclusion, results confirm the value of using both EVI and VOD at the same time, and the advantage of using automatic machine learning models for crop yield/production estimation.
dc.language.iso	eng
dc.relation.ispartof	Remote Sensing of Environment, 2019, vol. 234, num. 111460
dc.subject	Teledetecció
dc.subject	Processos estocàstics
dc.subject	Imatges Processament
dc.title	Synergistic integration of optical and microwave satellite data for crop yield estimation
dc.type	journal article	es_ES
dc.date.updated	2020-12-09T15:11:32Z
dc.identifier.doi	10.1016/j.rse.2019.111460
dc.identifier.idgrec	136985
dc.rights.accessRights	open access	es_ES