Input/output product specifications
The global 30-m Seamless Data Cube (SDC) surface reflectance dataset (2000-2022) is generated by combining multi-sensor observations from the Landsat 5, 7, 8, 9 and MODIS Terra products.
1. Landsat collection-2 level-2 surface reflectance input product
We collected a comprehensive dataset comprising 6,564,546 Landsat Collection 2 Level 2 Surface Reflectance (L2SR) images from the USGS Earth Resources Observation and Science (EROS) Center, including data acquired by the Landsat 8-9 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 5 Thematic Mapper (TM). This dataset covers most of global land surface except Antarctica, spanning from 2000 to 2022.
The L2SR products are generated through a sequence of processing steps applied to Landsat raw data. These steps include reprojection, radiometric calibration, geometric correction, atmospheric correction, and cloud masking. Compared to the previous Landsat Collection 1 products, the Collection 2 products have markedly improved the Landsat absolute geolocation accuracy using Landsat 8 geolocational imaging performance harmonized with the ESA GRI data (Crawford, 2023). The Landsat 5,7 TM/ETM+ data were atmospherically corrected using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) (Masek et al., 2006), and the Landsat 8-9 OLI data were corrected using the Land Surface Reflectance Code (LaSRC) (Vermote et al., 2016). The Fmask algorithm (Zhu and Woodcock, 2012) was applied to detect cloud and cloud shadow in Landsat images. The L2SR products are spatially referenced using the Worldwide Reference System-2 (WRS-2) path rows and provided in the UTM projection. Figure 1 illustrates the spatial and temporal distribution of all the L2SR images used in this study. As listed in Table 1, we used the blue, green, red, near infrared (NIR), and two shortwave infrared (SWIR1 and SWIR2) bands for the generation of SDC. Although Landsat sensors have a relatively narrow field of view (15 degrees), the Bidirectional Reflectance Distribution Function (BRDF) normalization was found to be effective to make multi-temporal Landsat observations more consistent (Claverie et al., 2015; Roy et al., 2016b). We applied the c-factor technique and global constant BRDF coefficients provided by Roy et al. (2016b) to obtain the Landsat Nadir BRDF adjusted Reflectance (NBAR) dataset for SDC generation.
2. MODIS Nadir BRDF-Adjusted Reflectance (NBAR) products
We reprocessed the MODIS MOD09GA Version 6.1 surface reflectance product to obtain a daily seamless (without missing values) 500-m MODIS NBAR dataset, as detailed in Liang et al. (2024). The official MODIS MCD43A4 NBAR product was not employed due to persisting concerns associated with it, including the prevalence of missing data and residual influence of cloud and aerosols (Liang et al., 2024).
All MODIS Terra Surface Reflectance MOD09GA Version 6.1 images for the period 2000-2022 were acquired from NASA EarthData. The MOD09GA product is tiled in the MODIS sinusoidal system with a spatial resolution of about 500m. A set of processing algorithms was applied to these MOD09GA images to derive a daily, 500-m resolution, seamless MODIS NBAR data cube (Liang et al., 2024), including three main stages: (i) Land cover-based BRDF correction with the kernel-driven RossThick-LiSparse-Reciprocal (RTLSR) model using parameters derived from the MCD43A1 BRDF Model Parameters dataset and land cover maps from the MCD12Q1 land cover product; (ii) Outlier removal and gap filling using the ecosystem curve-fitting method; and (iii) Sliding-window temporal smoothing using the Savitzky-Golay filter. The generated MODIS NBAR seamless dataset provides seven spectral bands that are commonly used for terrestrial applications, six of which that have compatible bandwidths with Landsat sensors are employed for SDC generation, as listed in Table 1.