气候变化领域集成服务门户(CCPortal): Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison

CCPortal

DOI	10.1016/j.rse.2020.112117
	Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison
	Silvero N.E.Q.; Demattê J.A.M.; Amorim M.T.A.; Santos N.V.D.; Rizzo R.; Safanelli J.L.; Poppiel R.R.; Mendes W.D.S.; Bonfatti B.R.
发表日期	2021
ISSN	00344257
卷号	252
英文摘要	There is a worldwide need for detailed spatial information to support soil mapping, mainly in the tropics, where main agricultural areas are concentrated. In this line, satellite images are useful tools that can assist in obtaining soil information from a synoptic point of view. This study aimed at evaluating how satellite images at different resolutions (spatial, spectral and temporal) can influence the representation of soil variability over time, the percentage of bare soil areas and spatial predictions of soil properties in southeastern Brazil. We used single-date and multi-temporal images (SYSI, Synthetic Soil Images) of bare soil pixels from the Sentinel2-MultiSpectral Instrument (S2-MSI) and the Landsat-8 Operational Land Imager (L8-OLI) to conduct this research. Two SYSIs were obtained from images acquired in four years (2016–2019) for each satellite (SYSI S2-MSI and SYSI L8-OLI) and a third SYSI, named SYSI Combined, was obtained by combining the images from both satellites. The single-date images for each satellite was acquired in September, when the influence of clouds was low and bare soil pixels was predominant. Single-date images and SYSIs were compared by means of their spectral patterns and ability to predict topsoil properties (clay, sand, silt, and organic matter contents and soil color) using the Cubist algorithm. We found that the SYSIs outperformed single-date images and that the SYSI Combined and SYSI L8-OLI provided the best prediction performances. The SYSIs also had the highest percentage of areas with bare soil pixels (~30–50%) when compared to the single-date images (~20%). Our results suggest that bare soil images obtained by combining Landsat-8 and Sentinel-2 images are more important for soil mapping than spatial or spectral resolutions. Soil maps obtained via satellite images are important tools for soil survey, land planning, management and precision agriculture. © 2020 Elsevier Inc.
英文关键词	Bare soil pixels; Digital soil mapping; Machine learning; Remote sensing; Spatial resolution; Spectral resolution; Time series
语种	英语
scopus关键词	Agricultural robots; Agriculture; Forecasting; Photomapping; Pixels; Satellites; Soil surveys; Different resolutions; Multi-temporal image; Multispectral instruments; Operational land imager; Organic matter content; Prediction performance; Spatial informations; Synthetic soil images; Soils; bare soil; Landsat; precision agriculture; quantitative analysis; satellite imagery; Sentinel; soil survey; Brazil
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179093
作者单位	“Luiz de Queiroz” College of Agriculture, University of São Paulo, Brazil
推荐引用方式 GB/T 7714	Silvero N.E.Q.,Demattê J.A.M.,Amorim M.T.A.,et al. Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison[J],2021,252.
APA	Silvero N.E.Q..,Demattê J.A.M..,Amorim M.T.A..,Santos N.V.D..,Rizzo R..,...&Bonfatti B.R..(2021).Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison.Remote Sensing of Environment,252.
MLA	Silvero N.E.Q.,et al."Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison".Remote Sensing of Environment 252(2021).