Building predictive models to assess degradation of soil organic matter over time using remote sensing data

Abstract EN

Agricultural fields near rabia district, northwest of Nineveh Governorate / Iraq, were selected for study, as the study area is located between longitudes (36°31'51.34" and 36°43'40.343") north and two latitudes (42°16'14.475" and 42°34'50.99") east, with an area of approximately 52.5 hectares.

the predictive model is built from the integration of multiple linear and nonlinear regression relationships between remote sensing data and laboratory-measured organic matter concentration values.

the predictive model was applied to satellite data for three years (2002, 2012, and 2022), producing three maps to describe the soil content of organic matter (a map for each year).

the results of the study showed the possibility of applying predictive models to satellite data for a particular area and for previous years to give results with high spatial accuracy (R² = 0.9581).

Spatial maps were possible for each of the three years studied (2002, 2012, and 2022), and fertility maps were drawn by projecting spectral evidence values into the predictive model equation in the ENVI program.

the resulting images were then processed using ArcGIS 10.8 to color them and perform a Reclassify operation and take them out with the values of percentages of organic matter concentrations.

the results showed a clear deterioration in the soil's organic matter content over time, especially between 2012 and 2022.