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

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...

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Autores principales:	Wanxue Zhu, Ehsan Eyshi Rezaei, Hamideh Nouri, Ting Yang, Binbin Li, Huarui Gong, Yun Lyu, Jinbang Peng, Zhigang Sun
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	unmanned aerial vehicle satellite remote sensing soil quality multispectral Sentinel-2B Science Q
Acceso en línea:	https://doaj.org/article/828b773707d1470898c7baa4388fb5b3
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spelling	oai:doaj.org-article:828b773707d1470898c7baa4388fb5b32021-11-25T18:55:42ZQuick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data10.3390/rs132247162072-4292https://doaj.org/article/828b773707d1470898c7baa4388fb5b32021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4716https://doaj.org/toc/2072-4292Satellite 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 (<i>n</i> = 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.Wanxue ZhuEhsan Eyshi RezaeiHamideh NouriTing YangBinbin LiHuarui GongYun LyuJinbang PengZhigang SunMDPI AGarticleunmanned aerial vehiclesatelliteremote sensingsoil qualitymultispectralSentinel-2BScienceQENRemote Sensing, Vol 13, Iss 4716, p 4716 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	unmanned aerial vehicle satellite remote sensing soil quality multispectral Sentinel-2B Science Q
spellingShingle	unmanned aerial vehicle satellite remote sensing soil quality multispectral Sentinel-2B Science Q Wanxue Zhu Ehsan Eyshi Rezaei Hamideh Nouri Ting Yang Binbin Li Huarui Gong Yun Lyu Jinbang Peng Zhigang Sun Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data
description	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 (<i>n</i> = 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.
format	article
author	Wanxue Zhu Ehsan Eyshi Rezaei Hamideh Nouri Ting Yang Binbin Li Huarui Gong Yun Lyu Jinbang Peng Zhigang Sun
author_facet	Wanxue Zhu Ehsan Eyshi Rezaei Hamideh Nouri Ting Yang Binbin Li Huarui Gong Yun Lyu Jinbang Peng Zhigang Sun
author_sort	Wanxue Zhu
title	Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data
title_short	Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data
title_full	Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data
title_fullStr	Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data
title_full_unstemmed	Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data
title_sort	quick detection of field-scale soil comprehensive attributes via the integration of uav and sentinel-2b remote sensing data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/828b773707d1470898c7baa4388fb5b3
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Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data

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