# Satellite remote sensing enables monitoring of soil organic carbon decline in croplands of Jilin China

## TL;DR

This study uses satellite data to track soil carbon loss in croplands in Jilin, China, and introduces a new method to monitor soil quality more effectively.

## Contribution

The study introduces a new broadband spectral index (RSI) for scalable and reproducible soil organic carbon monitoring.

## Key findings

- A 5.14% decline in soil organic carbon was observed in Jilin croplands over seven years.
- The Ratio Soil Index (RSI) showed strong correlations (0.72–0.77) with measured soil organic carbon across different satellite sensors.

## Abstract

Soil organic carbon (SOC) is a key parameter for soil quality. As one of the major grain-producing regions of China, Jilin Province plays a critical role in ensuring national food security, making cropland SOC monitoring essential. Based on satellite remote sensing observations, this study reveals an overall 5.14% decline in SOC across croplands in Jilin Province over the past seven years. Losses were most pronounced in the west, while the central and eastern areas remained relatively stable. Conventional SOC estimation methods largely rely on machine learning, which can lack physical interpretability and reproducibility. PLSR-based SOC models achieved validation R2 values of 0.40–0.61 with corresponding RMSEs of 0.30–0.38 across MODIS Terra, Landsat OLI, and Sentinel-2 MSI. The quantitative models exhibit satisfactory validation accuracy but limited spatial robustness across sensors in practical mapping. This study proposes a new broadband spectral index, the Ratio Soil Index (RSI), applied at 30-meter resolution. Using field synchronized SOC measurements and spectral analysis, we developed broadband indices from MODIS Terra, Landsat OLI, and Sentinel MSI. The RSI showed strong correlations with measured SOC, with coefficients of 0.72, 0.74, and 0.77 for the three sensors. Its spatial patterns were consistent with ground observations within the 95% confidence interval. The findings demonstrate that the RSI, with its concise formulation, reliable mapping performance, and ability to identify the variations of SOC, offers a scalable and reproducible metric for national SOC monitoring under changing agricultural management.