| Cite this article: | 吴迪,杨鹏,周黎勇,等.基于Sentinel-2破碎化地块灌区作物种植结构的提取[J].灌溉排水学报,0,():-. |
| WU Di,YANG Peng,ZHOU Li-yong,et al.基于Sentinel-2破碎化地块灌区作物种植结构的提取[J].灌溉排水学报,0,():-. |
|
| |
|
|
| This article has been：Browse 975Times Download 0Times |
|
|
|
| DOI： |
|
| Extraction of Crop Planting Structure in Irrigated Area of Fragmentation Field Based on Sentinel-2 |
|
WU Di1, YANG Peng2, ZHOU Li-yong3, LI Fang-song3, LI Ling-feng3, ZHANG Xu-dong2
|
|
1.China Irrigation and Drainage Development Center,Beijing ,china;2.College of Water Conservancy,Shenyang Agricultural University;3.Xinjiang Institute of Water Resources and Hydropower Research
|
| Abstract: |
| 【Objective】To explore the applicability of decision tree classification model based on Sentinel-2 remote sensing image for crop structure extraction in irrigated area of fragmented land parcel.【Method】The Alagou irrigation area in Xinjiang was selected as the research area, and the Sentinel-2 remote sensing images of the whole growth period of major crops in 2021 were used as data sources, combined with field investigation and visual interpretation sampling of Google HD images. Based on the phenological information and NDVI time series characteristics of major crops, the threshold values of the critical period for crop recognition were determined. The classification model of decision tree was constructed and the accuracy of classification results were verified.【Result】The plots of planting structure distribution map extracted from Sentinel-2 remote sensing images had clear texture and natural block, which could meet the needs of water management in irrigation areas. The established decision tree classification model can realize crop classification at the irrigated area scale. The method is simple and feasible, with an overall accuracy of 81.56% and a Kappa coefficient of 0.7166.【Conclusion】It is feasible to use Sentinel-2 remote sensing data and decision tree classification method to identify complex crop classification in irrigated areas of fragmentation field, which can provide basic information for decision-making of water supply and distribution and refined management of agricultural water in irrigated areas. |
| Key words: Sentinel-2; crops classification; NDVI time series; decision tree |
|
|
|
|
|
|
