气候变化领域集成服务门户(CCPortal): An integrated methodology using open soil spectral libraries and Earth Observation data for soil organic carbon estimations in support of soil-related SDGs

CCPortal

DOI	10.1016/j.rse.2020.111793
	An integrated methodology using open soil spectral libraries and Earth Observation data for soil organic carbon estimations in support of soil-related SDGs
	Tziolas N.; Tsakiridis N.; Ogen Y.; Kalopesa E.; Ben-Dor E.; Theocharis J.; Zalidis G.
发表日期	2020
ISSN	00344257
卷号	244
英文摘要	There is a growing realization amongst policy-makers that reliable and accurate soil monitoring information is required at scales ranging from regional to global to support ecosystem functions and services in a sustainable manner under the amplifying climate change enabling countries in target setting of the Sustainable Development Goals (SDGs). In this line, the need of access to and integration of existing regional in situ Earth Observation (EO) data and different sources such as contemporary and forthcoming satellite imagery is highlighted. The current study puts major emphasis on leveraging existing open soil spectral libraries and EO systems and bridging them with memory-based learning algorithms that create more cost-efficient and targeted large scale mapping of soil properties. Relying mostly on contemporary capacities and open resources it can be readily applied to countries with differing capacities and levels of development. To test our methodology, the GEOCRADLE SSL developed in the Balkans, Middle East, and North Africa region and a hyperspectral airborne image were utilized to provide Soil Organic Carbon (SOC) maps of cropland fields over an agricultural region near the city of Netanya, Israel. Furthermore, simulated data of forthcoming space-borne satellite (EnMAP) and current super-spectral mission (Sentinel 2) were explored. The SOC content of the collected in situ soil samples was predicted using a novel local regression approach that combines spatial proximity and spectral similarities. These predictions were subsequently used to develop models using the airborne and simulated satellite spectra, achieving a fair prediction accuracy of R2 > 0.8 and RPIQ>2. © 2020 Elsevier Inc.
英文关键词	Hyperspectral imagery; Memory-based learning; Soil organic carbon; Soil spectral library; Sustainable development goals
语种	英语
scopus关键词	Agricultural robots; Climate change; Libraries; Observatories; Open Data; Organic carbon; Photomapping; Satellite imagery; Soils; Earth observation data; Ecosystem functions; Integrated methodology; Memory-based learning; Prediction accuracy; Soil organic carbon; Spectral libraries; Spectral similarity; Soil testing; algorithm; ecosystem function; EOS; integrated approach; Sentinel; soil organic matter; soil property; spectral analysis; Sustainable Development Goal; Balkans; Israel; North Africa
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/179325
作者单位	School of Agriculture, Faculty of Agriculture, Forestry, and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki, 54123, Greece; Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54123, Greece; The Remote Sensing and GIS Laboratory Department of Geography, School of Earth Science, Tel-Aviv University, PO Box 39040, Israel; Interbalkan Environment Center, 18 Loutron Str., Lagadas, Greece
推荐引用方式 GB/T 7714	Tziolas N.,Tsakiridis N.,Ogen Y.,et al. An integrated methodology using open soil spectral libraries and Earth Observation data for soil organic carbon estimations in support of soil-related SDGs[J],2020,244.
APA	Tziolas N..,Tsakiridis N..,Ogen Y..,Kalopesa E..,Ben-Dor E..,...&Zalidis G..(2020).An integrated methodology using open soil spectral libraries and Earth Observation data for soil organic carbon estimations in support of soil-related SDGs.Remote Sensing of Environment,244.
MLA	Tziolas N.,et al."An integrated methodology using open soil spectral libraries and Earth Observation data for soil organic carbon estimations in support of soil-related SDGs".Remote Sensing of Environment 244(2020).