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Satellite remote sensing has become a popular tool to analyze agricultural drought through terrestrial vegetation health conditions using the normalized difference vegetation index (NDVI). Drought monitoring techniques using remote sensing-based drought indices assume that vegetation conditions vary year-to-year due to prevailing weather conditions (e.g., precipitation and temperature), and current conditions are evaluated based on the deviation from the long-term statistics such as mean, minimum, or maximum. However, the rotation between agricultural crops (e.g., corn and soybeans) implies that this assumption may not hold, as each crop type may have distinct phenological variability across the growing season. In this study, the effect of crop rotation between corn and soybeans on the accuracy of the NDVI-based agricultural drought monitoring was investigated in Iowa, USA. The vegetation condition index (VCI), which is derived from NDVI, was selected to demonstrate the impact of crop rotation. The standard precipitation index (SPI) and official crop yield statistics were used as independent validation of the drought information acquired by these indices. The results suggested that the NDVI alone was not able to distinguish drought-related vegetation stress from vegetation changes caused by crop rotation between corn and soybeans. It was found that the integration of land cover with NDVI greatly improved the agricultural drought information obtained by the VCI over the crop-rotated agricultural fields in Iowa.
