Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks. Issue 3 (3rd July 2022)

• Record Type: Journal Article
• Authors: Bouasria, Abdelkrim; Ibno Namr, Khalid; Rahimi, Abdelmejid; Ettachfini, El Mostafa; Rerhou, Badr
• Is Part Of: Geo-spatial information science. Volume 25:Issue 3(2022)
• Page Start: 353
• Page End: 364
• Publication Date: 2022-07-03
• Subjects: Digital soil mapping -- soil organic matter -- remote sensing -- multiple linear regression -- artificial neural networks -- irrigated area -- Doukkala -- Morocco; Geographic information systems -- Periodicals; Cartography -- Data processing -- Periodicals; Surveying -- Data processing -- Periodicals; Remote sensing -- Periodicals; 526.0285
• DOI: 10.1080/10095020.2022.2026743
• Languages: English
• ISSNs: 1009-5020

Abstract

ABSTRACT: In agricultural systems, the regular monitoring of Soil Organic Matter (SOM) dynamics is essential. This task is costly and time-consuming when using the conventional method, especially in a very fragmented area and with intensive agricultural activity, such as the area of Sidi Bennour. The study area is located in the Doukkala irrigated perimeter in Morocco. Satellite data can provide an alternative and fill this gap at a low cost. Models to predict SOM from a satellite image, whether linear or nonlinear, have shown considerable interest. This study aims to compare SOM prediction using Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). A total of 368 points were collected at a depth of 0–30 cm and analyzed in the laboratory. An image at 15 m resolution (MSPAN) was produced from a 30 m resolution (MS) Landsat-8 image using image pansharpening processing and panchromatic band (15 m). The results obtained show that the MLR models predicted the SOM with (training/validation) R 2 values of 0.62/0.63 and 0.64/0.65 and RMSE values of 0.23/0.22 and 0.22/0.21 for the MS and MSPAN images, respectively. In contrast, the ANN models predicted SOM with R 2 values of 0.65/0.66 and 0.69/0.71 and RMSE values of 0.22/0.10 and 0.21/0.18 for the MS and MSPAN images, respectively. Image pansharpening improved the prediction accuracy by 2.60% and 4.30% and reduced the estimation error by 0.80% and 1.30% for the MLR and ANN models, respectively.