气候变化领域集成服务门户(CCPortal): Integrating coarse-resolution images and agricultural statistics to generate sub-pixel crop type maps and reconciled area estimates

CCPortal

DOI	10.1016/j.rse.2021.112365
	Integrating coarse-resolution images and agricultural statistics to generate sub-pixel crop type maps and reconciled area estimates
	Hu Q.; Yin H.; Friedl M.A.; You L.; Li Z.; Tang H.; Wu W.
发表日期	2021
ISSN	00344257
卷号	258
英文摘要	Reliable crop type maps are vital for agricultural monitoring, ensuring food security, and environmental sustainability assessments. Coarse-resolution imagery such as MODIS are widely used for crop type mapping due to their short revisit cycles, which is advantageous for detecting the seasonal dynamics of different crop types. However, the inherently low spatial resolution may restrict their utility for mapping crop types in regions with heterogeneous agricultural landscapes. Agricultural statistics, which provide crop acreage information at different spatial and temporal scales, have the potential to improve crop type mapping from remote sensing. Yet, previous studies have often used agricultural statistics as reference data to evaluate the accuracy of satellite-derived crop type maps but have rarely utilized them to improve crop type distribution mapping. The utility of integrating agricultural statistics with satellite images to produce high-accuracy crop type maps is rarely explored. This study presents a methodology for mapping sub-pixel crop type distributions via the integration of MODIS time series and agricultural statistics. We tested our approach in Heilongjiang Province, which has the highest agricultural production in China. First, we used an optimized random forest regression (RF-r) model with training samples derived from high spatial resolution images (i.e. SPOT and Landsat) to predict the sub-pixel crop type distributions from MODIS time series. To optimize the RF-r model, an 8-day MODIS time series of five vegetation indices in 2011 were used as the candidate independent variables, and a backward feature elimination strategy was implemented to select the best variables for model prediction. Second, we developed an Iterative Area Gap Spatial Allocation (IAGSA) method to spatially reconcile the discrepancies between the crop acreage estimated from MODIS-based maps and the agricultural statistics. We found that the MODIS-derived crop fractions agreed with those derived from the high-resolution images, with R2 > 0.75 for all crop types, yet there was a clear discrepancy between the crop acreage estimated from MODIS and agricultural statistics. The sub-pixel crop type maps adjusted by IAGSA were not only consistent with the agricultural statistics for crop acreage, but also retained the spatial distribution patterns of the original MODIS-derived crop fraction. Our results suggest the advantages of integrating coarse-resolution images and agricultural statistics to map sub-pixel crop type distributions, and to provide consistent estimation of crop acreage. The presented methodology has the potential to map large-scale crop type extent across regions in a cost-effective way. © 2021 Elsevier Inc.
英文关键词	Area estimate; Crop type mapping; Feature selection; Iterative area gap spatial allocation; MODIS; Random forest regression
语种	英语
scopus关键词	Decision trees; Feature extraction; Food supply; Forestry; Image resolution; Iterative methods; Mapping; Pixels; Radiometers; Regression analysis; Remote sensing; Sustainable development; Time series; Area estimate; Coarser resolution; Crop type mappings; Features selection; Iterative area gap spatial allocation; Random forest regression; Sub-pixels; Times series; Type maps; Types distributions; Crops; accuracy assessment; agricultural application; agricultural land; assessment method; estimation method; image resolution; MODIS; pixel; satellite imagery; spatial resolution; statistical analysis; sustainability; time series analysis; vegetation mapping; China; Heilongjiang
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/178886
作者单位	Key Laboratory for Geographical Process Analysis & Simulation of Hubei province/School of Urban and Environmental Sciences, Central China Normal University, Wuhan, 430079, China; Department of Earth and Environment, Boston University, Boston, MA 02215, United States; Department of Geography, Kent State University, 325 S. Lincoln Street, Kent, OH 44242, United States; International Food Policy Research Institute, 1201 I Street, NW, Washington, DC, 20005, United States; Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, Wuhan, 430070, China; Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
推荐引用方式 GB/T 7714	Hu Q.,Yin H.,Friedl M.A.,et al. Integrating coarse-resolution images and agricultural statistics to generate sub-pixel crop type maps and reconciled area estimates[J],2021,258.
APA	Hu Q..,Yin H..,Friedl M.A..,You L..,Li Z..,...&Wu W..(2021).Integrating coarse-resolution images and agricultural statistics to generate sub-pixel crop type maps and reconciled area estimates.Remote Sensing of Environment,258.
MLA	Hu Q.,et al."Integrating coarse-resolution images and agricultural statistics to generate sub-pixel crop type maps and reconciled area estimates".Remote Sensing of Environment 258(2021).