Spatial distribution of mangrove soil organic carbon in Indus Delta, Pakistan: A multi-sensor remote sensing and machine learning approach

Abstract

Mangrove forests play a crucial role in carbon sequestration, especially in the coastal zones of arid regions where their ability to store carbon in soil is generally much higher than in terrestrial drylands. The Indus Delta in Pakistan is the world's largest arid mangrove forest system but lacks spatially explicit data on Soil Organic Carbon (SOC) despite its importance for conservation and carbon budgeting. This study aims to establish a baseline SOC map for 2020 at 10 m spatial resolution using Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (MultiSpectral Instrument) satellite imagery, integrated with in-situ soil sampling. SOC predictions were made using a Classification and Regression Tree (CART) machine learning model within the Google Earth Engine platform, leveraging 40 predictor variables, including spectral bands and derived indices. A total of 53 topsoil (10 cm depth) samples were collected in February 2020 across the Indus Delta, and SOC was analyzed using the Walkley-Black method. The results showed an average SOC value of 67.59 ± 37.41 MgC ha⁻¹ (to 10 cm depth), and local values ranging from 15.07 to 138.04 MgC ha⁻¹. with a total of 0.91 PgC. The CART model demonstrated high accuracy, with an R² of 0.95 and an RMSE of 9.18 MgC ha⁻¹. However, the region faces challenges such as seawater intrusion and hypersalinity, which threaten its ability to sequester carbon. With the first high-resolution SOC map for the Indus Delta, this study provides valuable insights for ecosystem management, conservation planning, and carbon budgeting. These findings of this study have the potential to significantly influence initiatives like REDD+ and Blue Carbon projects, which aim to enhance carbon sequestration while addressing the ecological challenges facing Pakistan's mangroves.