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Open AccessArticle

Fusion of MODIS and Landsat-Like Images for Daily High Spatial Resolution NDVI

1
Department of Agricultural Engineering, Federal University of Viçosa (UFV), Viçosa 36570-900, Brazil
2
Department of Soil and Plant Nutrition, Federal University of Viçosa (UFV), Viçosa 36570-900, Brazil
3
Department of Soils and Agricultural Engineering, State University of Ponta Grossa (UEPG), Ponta Grossa 84030-900, Brazil
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(8), 1297; https://doi.org/10.3390/rs12081297
Received: 17 February 2020 / Revised: 30 March 2020 / Accepted: 14 April 2020 / Published: 20 April 2020
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
One of the obstacles in monitoring agricultural crops is the difficulty in understanding and mapping rapid changes of these crops. With the purpose of addressing this issue, this study aimed to model and fuse the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) using Landsat-like images to achieve daily high spatial resolution NDVI. The study was performed for the period of 2017 on a commercial farm of irrigated maize-soybean rotation in the western region of the state of Bahia, Brazil. To achieve the objective, the following procedures were performed: (i) Landsat-like images were upscaled to match the Landsat-8 spatial resolution (30 m); (ii) the reflectance of Landsat-like images was intercalibrated using the Landsat-8 as a reference; (iii) Landsat-like reflectance images were upscaled to match the MODIS sensor spatial resolution (250 m); (iv) regression models were trained daily to model MODIS NDVI using the upscaled Landsat-like reflectance images (250 m) of the closest day as the input; and (v) the intercalibrated version of the Landsat-like images (30 m) used in the previous step was used as the input for the trained model, resulting in a downscaled MODIS NDVI (30 m). To determine the best fitting model, we used the following statistical metrics: coefficient of determination (r2), root mean square error (RMSE), Nash–Sutcliffe efficiency index (NSE), mean bias error (MBE), and mean absolute error (MAE). Among the assessed regression models, the Cubist algorithm was sensitive to changes in agriculture and performed best in modeling of the Landsat-like MODIS NDVI. The results obtained in the present research are promising and can enable the monitoring of dynamic phenomena with images available free of charge, changing the way in which decisions are made using satellite images. View Full-Text
Keywords: temporal dynamics; images downscaling; machine learning; decision making in agriculture temporal dynamics; images downscaling; machine learning; decision making in agriculture
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Filgueiras, R.; Mantovani, E.C.; Fernandes-Filho, E.I.; Cunha, F.F.; Althoff, D.; Dias, S.H.B. Fusion of MODIS and Landsat-Like Images for Daily High Spatial Resolution NDVI. Remote Sens. 2020, 12, 1297.

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