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The availability of well-curated datasets has driven the success of MachineLearning (ML) models. Despite the increased access to earth observation datafor agriculture, there is a scarcity of curated, labelled datasets, whichlimits the potential of its use in training ML models for remote sensing (RS)in agriculture. To this end, we introduce a first-of-its-kind dataset, SICKLE,having time-series images at different spatial resolutions from 3 differentsatellites, annotated with multiple key cropping parameters for paddycultivation for the Cauvery Delta region in Tamil Nadu, India. The datasetcomprises of 2,398 season-wise samples from 388 unique plots distributed across4 districts of the Delta. The dataset covers multi-spectral, thermal andmicrowave data between the time period January 2018-March 2021. The paddysamples are annotated with 4 key cropping parameters, i.e. sowing date,transplanting date, harvesting date and crop yield. This is one of the firststudies to consider the growing season (using sowing and harvesting dates) aspart of a dataset. We also propose a yield prediction strategy that usestime-series data generated based on the observed growing season and thestandard seasonal information obtained from Tamil Nadu Agricultural Universityfor the region. The consequent performance improvement highlights the impact ofML techniques that leverage domain knowledge that are consistent with standardpractices followed by farmers in a specific region. We benchmark the dataset on3 separate tasks, namely crop type, phenology date (sowing, transplanting,harvesting) and yield prediction, and develop an end-to-end framework forpredicting key crop parameters in a real-world setting.

High-Resolution Satellite Imagery for Modeling the Impact of Aridification on Crop Production