Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

Tao Zhou,Dagmar Haase,Chuanliang Sun,Jie Chen,Angela Lausch,Yajun Geng

doi:10.3390/rs11242934

Abstract

Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China. Fifteen predictor variables were used to construct models, including land use/land cover, multi-source remote sensing-derived variables, and topographic and climatic variables. We evaluated the prediction accuracy of the models based on a cross-validation procedure. Results showed that tree-based models (RF and BRT) outperformed SVM. Compared to the model that only used optical data, the addition of multi-temporal Sentinel-1A data using the BRT method improved the root mean square error (RMSE) and the mean absolute error (MAE) by 17.2% and 17.4%, respectively. Furthermore, the combination of all predictor variables using the BRT model had the best predictive performance, explaining 57% of the variation in STN, with the highest R2 (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. Remote sensing variables were the most important environmental variables for STN mapping, with 59% and 50% relative importance in the RF and BRT models, respectively. Our results show the potential of using multi-temporal Sentinel-1 data to predict STN, broadening the data source for future digital soil mapping. In addition, we propose that the SVM, RF, and BRT models should be calibrated and evaluated to obtain the best results for STN content mapping in similar landscapes.

Highlights

As a major component of the terrestrial nitrogen (N) pool, soil total nitrogen (STN) provides essential nutrients for plant growth and affects soil function and the concentration of greenhouse gases in the atmosphere
We explored the potential of multi-temporal Sentinel-1 data to predict Soil total nitrogen (STN) by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China
We predicted the spatial distribution of STN content in the middle reaches of the Heihe River Basin (HRB) in northwestern China by comparing the BRT, RF, and SVM models

Summary

Introduction

As a major component of the terrestrial nitrogen (N) pool, soil total nitrogen (STN) provides essential nutrients for plant growth and affects soil function and the concentration of greenhouse gases in the atmosphere. Low STN values limit plant growth while excessive STN may result in loss of nitrogen from the soil, causing soil fertility degradation and water pollution [1]. Soil degradation associated with soil nitrogen loss reduces soil security and greatly contributes to climate change. As a key indicator of soil fertility and quality, STN content is closely related to agricultural productivity and food security. Up to date STN maps are important to identify spatial variation and control factors of STN, which can help maintain food security and soil security and provide a reference for managing climate change. To address environmental challenges, such as climate change and land degradation, an accurate and efficient method is needed to predict the spatial distribution of STN and improve its prediction accuracy

Methods

Results

Conclusion