A Method to Downscale MODIS Surface Reflectance Using Convolutional Neural Networks

Abstract

Surface reflectance is an important indicator for the physical states of the Earth’s surface. The Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product at 500 m resolution (MOD09A1) includes seven spectral bands and has been widely used to derive many high-level parameter products, such as leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR). However, the MODIS surface reflectance product at 250 m resolution (MOD09Q1) is only available for the red and near-infrared (NIR) bands, which greatly limits its applications. In this study, a downscaling reflectance convolutional neural network (DRCNN) is proposed to downscale the surface reflectance of the MOD09A1 product and derive 250 m surface reflectance in the blue, green, shortwave infrared (SWIR1, 1628–1652 nm) and shortwave infrared (SWIR2, 2105–2155 nm) bands for generating high-level parameter products at 250 m resolution. The surface reflectance of the MOD09A1 and MOD09Q1 products are preprocessed to obtain cloud-free continuous surface reflectance. Additionally, the surface reflectance in the blue, green, SWIR1 and SWIR2 bands from the preprocessed MOD09A1 product were upsampled to obtain surface reflectance in the corresponding bands at 1 km resolution. Then, a database was generated from the upsampled surface reflectance and the preprocessed MOD09A1 product over the On Line Validation Exercise (OLIVE) sites to train the DRCNN. The surface reflectance in the blue, green, SWIR1 and SWIR2 bands from the preprocessed MOD09A1 product and the surface reflectance in the red and NIR bands from the preprocessed MOD09Q1 product were entered into the trained DRCNN to obtain the surface reflectance in the blue, green, SWIR1 and SWIR2 bands at 250 m resolution. The downscaled surface reflectance from the DRCNN were compared with the surface reflectance from the MOD09A1 product and Landsat 7. The results show that the DRCNN can effectively downscale the surface reflectance of the MOD09A1 product to generate the surface reflectance at 250 m resolution.