Abstract
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.
Highlights
The techniques and technologies available through the science of remote sensing have become indispensable for monitoring changes in the terrestrial surface [1,2,3,4,5,6,7]
The intercalibration of the spectral bands for the ETM+ and MultiSpectral Instrument (MSI) based on the Operational Land Imager (OLI) sensor obtained the following r2: ETM+ blue band (r2 = 0.97); MSI blue band (r2 = 0.96); ETM+ green band (r2 = 0.98); MSI green band (r2 = 0.96); ETM+ red band (r2 = 0.99); MSI red band (r2 = 0.97); ETM+ infrared band (r2 = 0.93); and MSI infrared band (r2 = 0.94)
A set of metrics was applied taking into account the findings highlighted in Chai and Draxler [74], who concluded that a composite of statistical indices is usually required to evaluate the performance of the models
Summary
The techniques and technologies available through the science of remote sensing have become indispensable for monitoring changes in the terrestrial surface [1,2,3,4,5,6,7]. High temporal resolution images tend to cover larger portions of the Earth’s surface, resulting in a poor spatial resolution. This fact is considered a technical limitation and known as a trade-off between the temporal and spatial resolution [14,15], which is due to the relationship between the scanning swath and image pixel size [11]. Based on these assumptions, there is currently no single orbital constellation that captures images with a high/medium temporal resolution and high spatial resolution, at least for free [16]
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