A MODIS-based scalable remote sensing method to estimate sowing and harvest dates of soybean crops in Mato Grosso, Brazil

Minghui Zhang, Gabriel Abrahao, Avery Cohn, Jake Campolo, Sally Thompson

Research output: Contribution to journalArticlepeer-review


Large-scale agriculture in the state of Mato Grosso, Brazil is a major contributor to global food supplies, but its continued productivity is vulnerable to contracting wet seasons and increased exposure to extreme temperatures. Sowing dates serve as an effective adaptation strategy to these climate perturbations. By controlling the weather experienced by crops and influencing the number of successive crops that can be grown in a year, sowing dates can impact both individual crop yields and cropping intensities. Unfortunately, the spatiotemporally resolved crop phenology data necessary to understand sowing dates and their relationship to crop yield are only available over limited years and regions. To fill this data gap, we produce a 500 m rainfed soy (Glycine max) sowing and harvest date dataset for Mato Grosso from 2004 to 2014 using a novel time series analysis method for Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery, adapted for implementation in Google Earth Engine (GEE). Our estimates reveal that soy sowing and harvest dates varied widely (about 2 months) from field to field, confirming the need for spatially resolved crop timing information. An interannual trend toward earlier sowing dates occurred independently of variations in wet season onset, and may be attributed to an improvement in logistic or economic constraints that previously hampered early sowing. As anticipated, double cropped fields in which two crops are grown in succession are planted earlier than single cropped fields. This difference shrank, however, as sowing of single cropped fields occurred closer to the wet season onset in more recent years. The analysis offers insights about sowing behavior in response to historical climate variations which could be extended to understand sowing response under climate change in Mato Grosso.

Original languageEnglish
Article numbere07436
Issue number7
Publication statusPublished - Jul 2021


Dive into the research topics of 'A MODIS-based scalable remote sensing method to estimate sowing and harvest dates of soybean crops in Mato Grosso, Brazil'. Together they form a unique fingerprint.

Cite this