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Exploring the Influence of Spatial Resolution on the Digital Mapping of Soil Organic Carbon by Airborne Hyperspectral VNIR Imaging

1
College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
2
Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, China
3
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
4
College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China
5
Geographical and Sustainability Sciences, The University of Iowa, Iowa City, IA 52246, USA
6
School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
7
Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1032; https://doi.org/10.3390/rs11091032
Received: 1 April 2019 / Revised: 19 April 2019 / Accepted: 21 April 2019 / Published: 1 May 2019
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Abstract

Accurate digital mapping of soil organic carbon (SOC) is important in understanding the global carbon cycle and its implications in mitigating climate change. Visible and near-infrared hyperspectral imaging technology provides an alternative for mapping SOC efficiently and accurately, especially at regional and global scales. However, there is a lack of understanding of the impacts of spatial resolution of hyperspectral images and spatial autocorrelation of spectral information on the accuracy of SOC retrievals. In this study, the hyperspectral images (380–1700 nm) with a spatial resolution of 1 m were acquired by Headwall Micro-Hyperspec airborne sensors. Then, hyperspectral images were resampled into three different spatial resolutions of 10 m, 30 m, and 60 m by near neighbor (NN), bilinear interpolation (BI), and cubic convolution (CC) resampling methods. The geographically weighted regression (GWR) model was used to explore the role of spatial autocorrelation in predicting SOC contrast with the partial least squares regression (PLSR) model. Results showed that (1) the hyperspectral images can be used to predict SOC and the spatial autocorrelation can improve the prediction accuracy, as the ratio of performance to interquartile range (RPIQ) values of PLSR and GWR were 1.957 and 2.003; (2) The SOC prediction accuracy decreased with the degradation of spatial resolution, and the RPIQ values of PLSR were from 1.957 to 1.134, and of GWR were from 2.003 to 1.136; (3) Three resampling methods had a much weaker influence than spatial resolution on SOC predictions because the differences of RPIQ values of NN, BI, and CC resampling methods were 0.146, 0.175, and 0.025 in the spatial resolutions of 10 m, 30 m, and 60 m, respectively; (4) Finally, the Global Moran’s I and the Anselin Local Moran’s I proved the existence of the spatial autocorrelation in SOC maps. We hope that this study can offer valuable information for digital soil mapping by satellite hyperspectral images in the near future. View Full-Text
Keywords: soil organic carbon; spatial autocorrelation; resampling methods; airborne hyperspectral images; geographically weighted regression soil organic carbon; spatial autocorrelation; resampling methods; airborne hyperspectral images; geographically weighted regression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Guo, L.; Shi, T.; Linderman, M.; Chen, Y.; Zhang, H.; Fu, P. Exploring the Influence of Spatial Resolution on the Digital Mapping of Soil Organic Carbon by Airborne Hyperspectral VNIR Imaging. Remote Sens. 2019, 11, 1032.

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