A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication

REMOTE SENSING OF ENVIRONMENT

卷:251

Article

Liu, Chong^1,2,3  Zhang, Qi⁴  Tao, Shiqi⁵  Qi, Jiaguo⁶  Ding, Mingjun⁷  Guan, Qihui⁷  Wu, Bingfang^2,8  Zhang, Miao²  Nabil, Mohsen^2,8,9  Tian, Fuyou^2,8  Zeng, Hongwei^2,8  Zhang, Ning¹⁰  Bavuudorj, Ganbat^2,8,11  Rukundo, Emmanuel^2,8  Liu, Wenjun²  Bofana, Jose^2,8,9,12  Beyene, Awetahegn Niguse^2,8,13  Elnashar, Abdelrazek^2,8,14 

[1] Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Guangzhou 510275, Peoples R China

[2] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[3] Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519000, Peoples R China

[4] Boston Univ, Frederick S Pardee Sch Global Studies, Frederick S Pardee Ctr Study Longer Range Future, Boston, MA 02215 USA

[5] Clark Univ, Grad Sch Geog, Worcester, MA 01610 USA

[6] Michigan State Univ, Ctr Global Change & Earth Observat, E Lansing, MI 48823 USA

[7] Jiangxi Normal Univ, Sch Geog & Environm, Nanchang 332000, Jiangxi, Peoples R China

[8] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

[9] Natl Author Remote Sensing & Space Sci NARSS, Div Agr Applicat Soils & Marine AASMD, New Nozha 1564, Alf Maskan, Egypt

[10] Univ Calif Agr & Nat Resources, Davis, CA 95618 USA

[11] Informat & Res Inst Meteorol Hydrol & Environm, Ulaanbaatar 15160, Mongolia

[12] Catholic Univ Mozambique, Fac Agr Sci, Cuamba 3305, Niassa, Mozambique

[13] Tigray Agr Res Inst, POB 492, Mekelle 251, Ethiopia

[14] Cairo Univ, Fac African Postgrad Studies, Dept Nat Resources, Giza 12613, Egypt

关键词: Cropping intensity;    Remote sensing;    Multiple sensors;    NDVI time series;    Crop phenophase;   

DOI  :  10.1016/j.rse.2020.112095

来源: Elsevier

【 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.

【 授权许可】

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