  The availability of well-curated datasets has driven the success of Machine
Learning (ML) models. Despite the increased access to earth observation data
for agriculture, there is a scarcity of curated, labelled datasets, which
limits 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 different
satellites, annotated with multiple key cropping parameters for paddy
cultivation for the Cauvery Delta region in Tamil Nadu, India. The dataset
comprises of 2,398 season-wise samples from 388 unique plots distributed across
4 districts of the Delta. The dataset covers multi-spectral, thermal and
microwave data between the time period January 2018-March 2021. The paddy
samples are annotated with 4 key cropping parameters, i.e. sowing date,
transplanting date, harvesting date and crop yield. This is one of the first
studies to consider the growing season (using sowing and harvesting dates) as
part of a dataset. We also propose a yield prediction strategy that uses
time-series data generated based on the observed growing season and the
standard seasonal information obtained from Tamil Nadu Agricultural University
for the region. The consequent performance improvement highlights the impact of
ML techniques that leverage domain knowledge that are consistent with standard
practices followed by farmers in a specific region. We benchmark the dataset on
3 separate tasks, namely crop type, phenology date (sowing, transplanting,
harvesting) and yield prediction, and develop an end-to-end framework for
predicting key crop parameters in a real-world setting.
