Soil Organic Carbon Estimation in Croplands by Integrating Soil Spectral Library and PRISMA Data

Soil organic carbon (SOC) is a crucial component of soil and is used as a proxy for soil health and fertility. SOC influences water and nutrient holding capacity, nutrient cycling and stability, and water infiltration and aeration properties of soil. Proximal Sensing and Remote sensing are two powerful tools well covered in literature used for quantitative analysis of SOC. It is difficult to examine the nutrients of the soil due to insufficient time for collecting soil samples from agricultural fields during the crop rotation process in countries like INDIA, where extensive agriculture is practiced. In this scenario, linking soil spectral libraries (SSL) developed from proximal sensing with RS image data would enable instantaneous estimation of SOC over a large command area.

A model (calibration using multivariate techniques) which contains the variability of the target site soils should be constructed in this process to extract useful information. However, many times this criterion is not easy to fulfil. To solve this problem, we propose building a soil spectral library using soil samples synthesised in lab conditions and further constructed SSL to estimate SOC and essential soil nutrients. In this regard, spectrum data of collected Alluvial soil samples were generated in lab conditions using ASD FieldSpec 4, while simultaneously analysing soil samples based on standard methods in soil science. A soil sample collected from the field was selected as the master sample, and sub-samples were prepared by combining soil with organic fertiliser and chemicals (spatial structure similar to soil compounds) of various compositions. The emissivity spectra of soil sub-samples were used to construct a spectral library and later used in the machine learning model to estimate the spatial variation of SOC utilizing space-based hyperspectral image (PRISMA). The results revealed that the proposed model for SOC estimation using spectral library is significant for the instant estimation of SOC. Future scope includes testing the approaches' capability for estimating essential soil nutrients in various soil origins.