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.

本篇论文介绍了一个包含同期多光谱、热学和微波数据以及关键的稻作参数注释的数据集 ——SICKLE，并在三个不同的任务上对其进行了基准测试，即庄稼类型、生长季日期（播种、移栽、收获）和预测收成，并提出了使用根据观察到的生长季节和 tamalnadu 农业大学获取的标准季节信息生成的时间序列数据的收成预测策略。该数据集提供了一个可以训练机器学习模型来进行农业遥感的研究方向。