An overview of the soil organic matter content present within the Emakhosaneni area, KwaZulu-Natal, South Africa using remote sensing technologies.

Abstract

Soil Organic Matter (SOM) is one of the fundamental constituents of soil and plays significant roles in the overall fertility, productivity, and quality of soil. It consists of decaying plant and animal material at various stages of decomposition, substances released by plant roots, and soil organisms. Additionally, it is responsible for supporting many physical, chemical, and biological functions within the soil. And, these functions influence the provision of ecosystem services to humans, plants, and animals. However, this SOM is under threat as 25% of the earth’s surface has become degraded, with 12 million hectares of topsoil being lost every year and, hence SOM. South Africa is one of those countries that are impacted by arable soil loss. Therefore, accurate measurement of SOM at different spatial scales is vital in providing information for planning a recovery strategy. However, traditional methods of soil analysis can be time-consuming, costly, and labour extensive. On the other hand, remote sensing is an efficient method that is time effective, low-cost, non-destructive, and has rapid data acquisition. Thus, offering an alternative to traditional methods of soil analysis. Hence, this research aimed to examine the SOM content within the Emakhosaneni area, KwaZulu-Natal, South Africa, by measuring the reflectance of soil using laboratory and remote sensing analysis. The first study examined the percentage of SOM content present within the study area, and its four major land uses using a laboratory technique. The results indicated that the area has a relatively low average percentage of SOM content (2.79%) present within its soils. Also, out of the four major land use types the agricultural land use had the highest average percentage of SOM content, followed by rangeland, built-up, and eroded land uses. These results further indicated the influence of land use activities on the SOM content within the study area. Overall, this study revealed the SOM content within the area is very low. It also highlighted the severity and consequences of the depleting SOM content within the area’s soils. The second study examined the relationship between the SOM content and spectral reflectance of soils within the study area using laboratory spectroscopy. Results suggested that the spectra obtained was influenced by soil colour and thus, established a relationship between SOM content and reflectance of soil samples as SOM content is linked to soil colour. Further assessment of this relationship by Partial Least Squares Regression analysis revealed a fair performance. With the models created using the pre-processed spectra and SOM content performing better than those with no pre-processed spectra. Overall, this study highlighted the importance and capability of Visible and Near Infrared (400 nm to 2400 nm) spectroscopy in examining SOM content compared to conventional laboratory approaches and its influence on the spectral reflectance of the soils. The third study assessed the relationship between SOM content and Sentinel-2 satellite remote sensing data of soils in the area. Both geostatistical methods and hybrid geostatistical methods were used to predict SOM content. Results showed that the hybrid geostatistical methods performed better than the geostatistical methods in predicting SOM content due to the contribution of auxiliary information. Therefore, this study emphasizes the potential of auxiliary remote sensing data such as Sentinel-2 imagery in predicting SOM content in KwaZulu-Natal, South Africa, while also making critical inferences regarding the spatial and temporal variability of SOM within an area. The overall findings presented in this research are encouraging and show that different remote sensing techniques can be successfully used in the estimation, assessment, and prediction of SOM content, especially within the Emakhosaneni area, KwaZulu-Natal, South Africa, with accuracy levels that are acceptable.