Huarui Gong

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

Publisher: MDPI AG

Date: 22-11-2021

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 s ling (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.

Deferred Registration of Nanophosphor Photoluminescence As a Platform for Optical Bioimaging

Publisher: Pleiades Publishing Ltd

Date: 2019

DOI: 10.1134/S0030400X19010077

Institute For Spectroscopy Russian Academy Of Sciences

Location: Russian Federation

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Moscow Engineering Physics Institute

Location: Russian Federation

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National Research University Higher School Of Economics

Location: Russian Federation

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National Research University Higher School Of Economics

Location: No location found

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Moscow Engineering Physics Institute

Location: No location found

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中国科学院地理科学与资源研究所

Location: China

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