气候变化领域集成服务门户(CCPortal): A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication

CCPortal

DOI	10.1016/j.rse.2020.112095
	A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication
	Liu C.; Zhang Q.; Tao S.; Qi J.; Ding M.; Guan Q.; Wu B.; Zhang M.; Nabil M.; Tian F.; Zeng H.; Zhang N.; Bavuudorj G.; Rukundo E.; Liu W.; Bofana J.; Beyene A.N.; Elnashar A.
发表日期	2020
ISSN	00344257
卷号	251
英文摘要	Accurate estimation of cropping intensity (CI), an indicator of food production, is well aligned with the ongoing efforts to achieve sustainable development goals (SDGs) under diminishing natural resources. The advancement in satellite remote sensing provides unprecedented opportunities for capturing CI information in a spatially continuous manner. However, challenges remain due to the lack of generalizable algorithms for accurately and efficiently mapping global CI with a fine spatial resolution. In this study, we developed a 30-m planetary-scale CI mapping framework with the reconstructed time series of Normalized Difference Vegetation Index (NDVI) from multiple satellite images. Using a binary crop phenophase profile indicating growing and non-growing periods, we estimated pixel-by-pixel CI by enumerating the total number of valid cropping cycles during the study years. Based on the Google Earth Engine cloud computing platform, we implemented the framework to estimate CI during 2016–2018 in eight geographic regions across continents that are representative of global cropping system diversity. Comparison with PhenoCam network data in four cropland sites suggests that the proposed framework is capable of capturing the seasonal dynamics of cropping practices. Spatially, overall accuracies based on validation samples range from 80.0% to 98.9% across different regions worldwide. Regarding the CI classes, single cropping systems are associated with more robust and less biased estimations than multiple cropping systems. Finally, our CI estimates reveal high agreement with two widely used land surface phenology products, including Vegetation Index and Phenology V004 (VIP4) and Moderate Resolution Imaging Spectroradiometer Land Cover Dynamics (MCD12Q2), meanwhile providing much more spatial details. Due to its robustness, the developed CI framework can be potentially generalized to produce global fine resolution CI products for food security and other applications. © 2020 Elsevier Inc.
英文关键词	Crop phenophase; Cropping intensity; Multiple sensors; NDVI time series; Remote sensing
语种	英语
scopus关键词	Food supply; Mapping; Pixels; Radiometers; Vegetation; Accurate estimation; Cloud computing platforms; Land surface phenology; Moderate resolution imaging spectroradiometer; Multiple cropping systems; Multiple satellites; Normalized difference vegetation index; Satellite remote sensing; Remote sensing; algorithm; estimation method; farming system; food security; land cover; land surface; MODIS; natural resource; phenology; remote sensing; satellite data; spatial resolution; sustainability
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/179117
作者单位	School of Geospatial Engineering and Science, Sun Yat-Sen University, Guangzhou, 510275, China; State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China; Frederick S. Pardee Center for the Study of Longer-Range Future, Frederick S. Pardee School of Global Studies, Boston University, Boston, MA 02215, United States; Graduate School of Geography, Clark University, Worcester, MA 01610, United States; Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48823, United States; School of Geography and Environment, Jiangxi Normal University, Nanchang, 332000, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Division of Agriculture Applications, Soils, and Marine (AASMD), National Authority for Remote Sensing & Space Sciences (NARSS), New Nozha, Alf Maskan,1564, Cairo, Eg...
推荐引用方式 GB/T 7714	Liu C.,Zhang Q.,Tao S.,et al. A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication[J],2020,251.
APA	Liu C..,Zhang Q..,Tao S..,Qi J..,Ding M..,...&Elnashar A..(2020).A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication.Remote Sensing of Environment,251.
MLA	Liu C.,et al."A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication".Remote Sensing of Environment 251(2020).