Cover crops, grown between cash crop harvesting and planting, can bring significant benefits to soil conservation (Plastina et al., 2020), nutrient management (Abdalla et al., 2019), weed control (Alonso-Ayuso et al., 2018, climate change adaptation and mitigation for agroecosystems (Delgado et al., 2021). The row crop system in the U.S. Midwest, contributing to one-third of the world's corn and soybean production (Rizzo et al., 2018), however, faces grand environmental challenges on excessive use of fertilizers (Jin et al., 2019), soil carbon loss (Thaler et al., 2021), and water quality degradation (Zhao et al., 2020). Planting cover crops has been considered an essential solution to address these environmental challenges (Seifert et al., 2018). However, the cover crop adoption percentage in the U.S. Midwest was very low (3.6% of cropland acreage in 2017) (NASS, 2019), primarily due to
Remote sensing datasets with both high spatial and high temporal resolution are critical for monitoring and modeling the dynamics of land surfaces. However, no current satellite sensor could simultaneously achieve both high spatial resolution and high revisiting frequency. Therefore, the integration of different sources of satellite data to produce a fusion product has become a popular solution to address this challenge. Many methods have been proposed to generate synthetic images with rich spatial details and high temporal frequency by combining two types of satellite datasets—usually frequent coarse-resolution images (e.g., MODIS) and sparse fine-resolution images (e.g., Landsat). In this paper, we introduce STAIR 2.0, a new fusion method that extends the previous STAIR fusion framework, to fuse three types of satellite datasets, including MODIS, Landsat, and Sentinel-2. In STAIR 2.0, input images are first processed to impute missing-value pixels that are due to clouds or sensor mechanical issues using a gap-filling algorithm. The multiple refined time series are then integrated stepwisely, from coarse- to fine- and high-resolution, ultimately providing a synthetic daily, high-resolution surface reflectance observations. We applied STAIR 2.0 to generate a 10-m, daily, cloud-/gap-free time series that covers the 2017 growing season of Saunders County, Nebraska. Moreover, the framework is generic and can be extended to integrate more types of satellite data sources, further improving the quality of the fusion product.
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