  The availability of well-curated datasets has driven the success of Machine
Learning (ML) models. Despite greater access to earth observation data in
agriculture, there is a scarcity of curated and 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 called
SICKLE, which constitutes a time-series of multi-resolution imagery from 3
distinct satellites: Landsat-8, Sentinel-1 and Sentinel-2. Our dataset
constitutes multi-spectral, thermal and microwave sensors during January 2018 -
March 2021 period. We construct each temporal sequence by considering the
cropping practices followed by farmers primarily engaged in paddy cultivation
in the Cauvery Delta region of Tamil Nadu, India; and annotate the
corresponding imagery with key cropping parameters at multiple resolutions
(i.e. 3m, 10m and 30m). Our dataset comprises 2,370 season-wise samples from
388 unique plots, having an average size of 0.38 acres, for classifying 21 crop
types across 4 districts in the Delta, which amounts to approximately 209,000
satellite images. Out of the 2,370 samples, 351 paddy samples from 145 plots
are annotated with multiple crop parameters; such as the variety of paddy, its
growing season and productivity in terms of per-acre yields. Ours is also one
among the first studies that consider the growing season activities pertinent
to crop phenology (spans sowing, transplanting and harvesting dates) as
parameters of interest. We benchmark SICKLE on three tasks: crop type, crop
phenology (sowing, transplanting, harvesting), and yield prediction
