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

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 8; p. 1297
• Main Authors: Filgueiras, Roberto, Mantovani, Everardo Chartuni, Fernandes-Filho, Elpídio Inácio, Cunha, Fernando França da, Althoff, Daniel, Dias, Santos Henrique Brant
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2020
• Subjects: Agricultural practices; Agriculture; Algorithms; Commercial farms; Corn; Crop rotation; Crops; Decision making; decision making in agriculture; Farms; images downscaling; Landsat; Landsat satellites; machine learning; Mapping; Methods; MODIS; Monitoring; Normalized difference vegetative index; Reflectance; Regression analysis; Regression models; Remote sensing; Researchers; Root-mean-square errors; Satellite imagery; Satellites; Sensors; Soybeans; Spatial discrimination; Spatial resolution; Spectroradiometers; Statistical analysis; temporal dynamics; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12081297

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.