Agriculture

Plants, microbes, and other organisms have been drawing CO2 from the atmosphere for millions of years. Most of this carbon is continually broken down and recirculated through the carbon cycle, and some is stored deep underground as coal and oil, but a large amount of carbon is sequestered in the biomass of trees, peat bogs, and soil. Our current economy encourages practices that are freeing much of this sequestered carbon through deforestation and unsustainable agriculture. On top of these effects, cattle and rice farming generate methane, a greenhouse gas far more potent than CO2 itself. Overall, land use by humans is estimated to be responsible for about a quarter of global GHG emissions (and this may be an underestimate ). In addition to this direct release of carbon through human actions, the permafrost is now melting, peat bogs are drying, and forest fires are becoming more frequent as a consequence of climate change itself – all of which release yet more carbon.

The large scale of this problem allows for a similar scale of positive impact. According to one estimate, about a third of GHG emissions reductions could come from better land management and agriculture. ML can play an important role in some of these areas. Precision agriculture could reduce carbon release from the soil and improve crop yield, which in turn could reduce the need for deforestation.

Data
Satellite imagery are often useful for monitoring land use. Some widely accessed resources include,


 * Landsat
 * Sentinel
 * Earth Engine Data Catalog
 * Methane detection from satellite

Methods and Software
Some packages for working with remote sensing data are,


 * eo-learn: A python package maintained by the European Space Agency, giving easy access to imagery from Sentinel satellites, as well as utilities for data processing
 * Holos: A crop simulator for Canadian farms.

Recommended Readings

 * Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices
 * Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

Journals and conferences

 * Computers and Electronics in Agriculture
 * Precision Agriculture