We propose to use aviris airborne remote sensing data and soil carbon measured on field samples to examine the relationships between soil carbon and land management intensity, and to develop spatially…

This federal contract award is a grant for $154,475.03 from the National Aeronautics and Space Administration (NASA) to Central State University's Office of Sponsored Programs. The grant is for research to use airborne hyperspectral remote sensing data and soil carbon measurements to examine the relationship between soil carbon and land management practices, and to develop models to map soil carbon across diverse agricultural systems in Gujarat, India. This research aims to support climate change mitigation and food security through improved understanding of soil carbon dynamics. The grant does not have a set-aside designation and was awarded on October 1, 2017, with a completion date of September 30, 2021. Central State University is an 1890 Land Grant Historically Black College and University located in Wilberforce, Ohio, and has previously received federal grants from agencies like the National Science Foundation and Department of Agriculture for agriculture, natural resources, and STEM education initiatives.

We propose to use aviris airborne remote sensing data and soil carbon measured on field samples to examine the relationships between soil carbon and land management intensity, and to develop spatially explicit empirical models to map soil carbon across diverse agricultural management systems. Soil carbon sequestration plays an important role in mitigating the twin crises of climate change and food insecurity, and hence an improved knowledge of soil carbon dynamics and its spatio-temporal variability is essential; a rapid, reliable and a repeatable method of estimating soil carbon is needed. While spectral data can be directly related to soil carbon, surface residue cover and tillage intensity, specific characteristics of agricultural management systems are a major determinant of the variability of soil carbon in space and time. Remote sensing, and hyperspectral remote sensing in particular, offers great potential for identifying and mapping the diversity of land use and management regimes across the landscape. We propose to map soil carbon across a diverse agricultural landscape in gujarat india by firstly mapping agricultural management systems, including crop types, tillage systems and residue cover using aviris data, and secondly by developing spatial models of soil carbon distribution, based in the aviris data and ground truth soil samples. We will validate land use and tillage intensity at the sample sites and measure soil carbon at these same sites using VIS- NIR analysis and traditional methods for a subset of sites to assist development of a spectral library.