Abstract

As a routine agricultural practice, irrigation is fundamental to protect crops from water scarcity and ensure food security in China. However, consistent and reliable maps about the spatial distribution and extent of irrigated croplands are still unavailable, impeding water resource management and agricultural planning. Here, we produced annual 500-m irrigated cropland maps across China for 2000–2019, using a two-step strategy that integrated statistics, remote sensing, and existing irrigation products into a hybrid irrigation dataset. First, we generated intermediate irrigation maps (MIrAD-GI) by fusing the MODIS-derived greenness index and statistical data. Second, we collected all existing available irrigation maps over China and integrated them with MIrAD-GI into an improved series of annual irrigation maps, using constrained statistics and a synergy mapping method. The resultant maps had moderate overall accuracies (0.732~0.819) based on nationwide reference ground samples and outperformed existing irrigation products by inter-comparison. As the first of this kind in China, the annual maps delineated the spatiotemporal pattern of irrigated croplands and could contribute to sustainable water use and agricultural development.

Measurement(s)

irrigation area and distribution

Technology Type(s)

statistics and satellite remote sensing

Factor Type(s)

agricultural irrigation

Sample Characteristic - Organism

agricultural irrigation

Sample Characteristic - Environment

agricultural field

Sample Characteristic - Location

China

Details

Title
Mapping 20 years of irrigated croplands in China using MODIS and statistics and existing irrigation products
Author
Zhang, Chao 1 ; Dong, Jinwei 2   VIAFID ORCID Logo  ; Ge, Quansheng 2   VIAFID ORCID Logo 

 Chinese Academy of Sciences, Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309); University of Chinese Academy of Sciences, Beijing, China (GRID:grid.410726.6) (ISNI:0000 0004 1797 8419) 
 Chinese Academy of Sciences, Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2690048937
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.