Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data

Wanxue Zhu,Binbin Li,Hamideh Nouri,Jinbang Peng,Zhigang Sun,Huarui Gong,Ting Yang,Yun Lyu,Ehsan Eyshi Rezaei

doi:10.3390/rs13224716

Abstract

Satellite and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV multispectral and Sentinel-2B data to estimate soil salinity and chemical properties over a large agricultural farm (400 ha) covered by different crops and harvest areas at the coastal saline-alkali land of the Yellow River Delta of China in 2019. Spatial information of soil salinity, organic matter, available/total nitrogen content, and pH at 0–10 cm and 10–20 cm layers were obtained via ground sampling (n = 195) and two-dimensional spatial interpolation, aiming to overlap the soil information with remote sensing information. The exploratory factor analysis was conducted to generate latent variables, which represented the salinity and chemical characteristics of the soil. A machine learning algorithm (random forest) was applied to estimate soil attributes. Our results indicated that the integration of UAV texture and Sentinel-2B spectral data as random forest model inputs improved the accuracy of latent soil variable estimation. The remote sensing-based information from cropland (crop-based) had a higher accuracy compared to estimations performed on bare soil (soil-based). Therefore, the crop-based approach, along with the integration of UAV texture and Sentinel-2B data, is recommended for the quick assessment of soil comprehensive attributes.

Highlights

Optimizing agricultural management practices, such as efficient and sustainable irrigation and fertilization, are effective ways to improve soil quality across vast coastal saline-alkali regions [1,2,3]
In the field of modern precision agriculture, quick assessment of comprehensive soil attributes is important for agronomic management over a large farm, the development of latent soil variables that consider both the fertility and salinity levels of soil is key to achieving this goal [13]
This study aimed to find an effective and quick approach to map and monitor the soil fertility and salinity using unmanned aerial vehicle (UAV) multispectral and Sentinel-2B satellite remote sensing data

Summary

Introduction

Optimizing agricultural management practices, such as efficient and sustainable irrigation and fertilization, are effective ways to improve soil quality across vast coastal saline-alkali regions [1,2,3]. The organic fertilizers/amendments have notable capabilities in improving soil structure and water holding capacity since the organic carbon from amendments works as a bonder to bind silt and clay particles together for the Remote Sens. Excessive nitrogen application accelerates environmental pollution and soil salinization through the release of a higher quantity of base cations to soil solution [1]. Under such circumstances, it is fundamental to diagnose the spatial variance of soil properties across saline-alkali soils. In the field of modern precision agriculture, quick assessment of comprehensive soil attributes is important for agronomic management over a large farm, the development of latent soil variables that consider both the fertility and salinity levels of soil is key to achieving this goal [13]

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Results

Discussion

Conclusion