In the tropical-humid region, wet farming crops (e.g., paddy) are a common agricultural commodity with a high-water requirement. Usually planted in the Asia monsoon region with a high precipitation rate, these crops are divided into the wet cropping season and the dry cropping season. During the dry cropping season, they are particularly vulnerable to agricultural drought caused by the decrease in precipitation. This study used Indonesia as a case study and is aimed at assessing the agricultural drought risk on a wet farming crop during the dry cropping season by examining the correlation between the drought hazard and its risk. For hazard assessment, Standardized Precipitation Index (SPI) was used to assess the agricultural drought, by using the Global Satellite Mapping of Precipitation (GSMaP) which has 0.1° × 0.1° spatial resolution. The result of correlation analysis between the SPI and drought-affected areas on a city scale showed that SPI-3 in August is the most suitable timescale to assess the agricultural drought in Indonesia. The agricultural drought risk assessment was conducted on the grid scale, where the crop yield estimation model was developed with the help of Normalized Difference Vegetation Index (NDVI). Based on the correlation analysis between SPI-3 and the detrended crop yield as drought risk indicators, the higher yield loss was found in the area above the threshold value (r-value ≤ 0.6) indicating that those areas were more vulnerable to drought, while the area below the threshold value has lower crop yield loss even in the area that was hit by the most severe drought, because the existing irrigation system was able to resist the drought’s impact on crop yield loss.

There is valuable information that can be obtained beyond using a fixed crop calendar with coarse spatial resolution. Knowing the dynamics of the timing and location in which a particular crop is planted and harvested, with an annual temporal resolution and a fine spatial resolution, is crucial not only for monitoring crop conditions and production but also for understanding crop management under changing climates. In this study, the Normalized Difference Vegetation Index (NDVI) was utilized to develop a historical crop calendar for paddy in Indonesia with a 1 km resolution from 2001 to 2021. The result of this study is the first dynamic crop calendar that includes information about the planting, peak, and harvesting dates, as crop growth indicators, derived from the analysis of NDVI value fluctuations. Additionally, this dataset also includes the total number of cropping seasons each year. In Indonesia, there are intensive agricultural activities, including two dry cropping seasons that occur after the wet cropping season. However, this dataset is limited only to crops grown during the dry seasons, which typically begin in February and June. This dataset offers significant information at a finer spatiotemporal resolution to enable studies on agricultural fields undergoing climate change, although it is more country–specific than the other established dataset. The annual crop calendar dataset from 2001 to 2021 underscores the significance of examining the variability in cropping seasons over the years. This exploration aims to deepen our comprehension of the interplay between cropping seasons, climatic indicators, and even the social factors influencing farmers’ decisions. Furthermore, presented at a 1 km resolution, this dynamic crop calendar underscores the need for a more precise representation of diverse cropping intensities and seasons, particularly within small and fragmented agricultural areas.