Assessment of remotely-sensed canopy measurements for mapping soil nutrient deficiencies in Mexico croplands

Authors: Cameron Levine*, University of Southern California
Topics: Agricultural Geography, Remote Sensing, Soils
Keywords: remote sensing, nutrient deficiencies, agro-ecosystems, soil fertility
Session Type: Virtual Poster
Day: 4/8/2021
Start / End Time: 11:10 AM / 12:25 PM
Room: Virtual 51
Presentation File: No File Uploaded

Soil fertility plays a critical role in agro-ecosystems by providing the foundation for water and nutrient uptake. Nutrient deficient soils may result in lower yielding crops and can reduce the effectiveness of fertilizers. Adapting management practices to account for soil deficiencies remains difficult in smallholder systems where collection of soil and plant tissues is cost and time prohibitive. Here we explore satellite-based methods for detecting soil and crop nutrient deficiencies in North-Central Mexico, using the ESA’s Sentinel-2 Multi Spectral Instrument Level-1.

Typical vegetation indices used for crop yield prediction, such as the Normalized Difference Vegetation Index (NDVI) or Green Chlorophyll Index (GCI) are not highly sensitive to leaf chlorosis caused by nutrient deficiencies. We therefore assess a set of vegetation indices derived from Sentinel-2’s Red Edge bands and designed for detecting leaf chlorosis for their ability to explain variation in soil fertility and crop yields. Vegetation index values during the growing season peak were sampled at ground-collected soil samples and stratified by the top and bottom 10% of various soil features. Fields in the upper quantile of organic matter, zinc, and phosphorous showed a consistently higher value of the deficiency indices than those in the bottom 10%. We also find the Simplified Canopy Chlorophyll Content Index shows moderate correlation (r = 0.28) with the residuals from a yield prediction model based on GCI and weather. This relationship indicates the potential of the red edge vegetation indices to add information related to plant nutrient deficiency to satellite-based yield models.

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