Comparison of Recent Remote Sensing Data Using an Artificial Neural Network to Predict Soil Moisture by Focusing on Radiometric Indices

Abstract

Remote sensing data is widely used as a common variable for digital soil mapping estimating models. The aim of this study, quite recently made available to researchers Operational Land Imager 2 (OLI–2) have structure Landsat 9 and Landsat 8 (OLI) and Sentinel 2A (MSI) to compare the performance of soil moisture estimation in multi-layer perceptron network (MLP) artificial intelligence algorithm of image data. The working area is 886.78 km2 and soil sampling was performed at 66 points for gravimetric soil moisture determination. In addition, after the satellite images were pre-processed, Soil Adjusted Vegetation Index (SAVI) and Normalized Difference Moisture Index (NDMI) were calculated. Landsat 9 (OLI-2) based SAVI and NDMI showed a moderately significant positive correlation relationship with gravimetric soil moisture (rSAVI-SM=0.62, rNMDI-SM=0.44). The relationship between Landsat 8 (OLI) (rSAVI-SM=0.57, rNDMI-SM=0.11) and Sentinel 2A (MSI) (rSAVI-SM=0.42, rNDMI-SM=0.27) based radiometric indices and soil moisture was lower than Landsat 9 (OLI-2). RMSE values of MLP models were found to be respectively 0.79, 1.16 and 1.17 for Landsat 9 (OLI-2), Landsat 8 (OLI) and Sentinel 2A (MSI). Our results showed that with an Operational Land Imager (OLI-2) and near and short-wave infrared wavelengths improvements to multispectral imaging have improved soil moisture estimation success.

Keywords

• multi-layer perceptron
• landsat 9
• soil moisture
• soil adjusted vegetation index
• normalized difference moisture index