Abstract
The northeastern margin of the Qinghai–Tibet Plateau (QTP) is an agricultural protection area in China’s new development plan, and the primary region of winter wheat growth within QTP. Winter wheat monitoring is critical for understanding grain self-sufficiency, climate change, and sustainable socioeconomic and ecological development in the region. However, due to the complex terrain and high altitude of the region, with discontinuous arable land and the relatively low level of agricultural development, there are no effective localization methodologies for extracting and monitoring the detailed planting distribution information of winter wheat. In this study, Sentinel-2A/B data from 2019 to 2020, obtained through the Google Earth Engine platform, were used to build time series reference curves of vegetation indices in Minhe. Planting distribution information of winter wheat was extracted based on the phenology time-weighted dynamic time warping (PT-DTW) method, and the effects of different vegetation indices’ time series and their corresponding threshold parameters were compared. The results showed that: (1) the three vegetation indices—normalized difference vegetation index (NDVI), normalized differential phenology index (NDPI), and normalized difference greenness index (NDGI)—maintained high mapping potential; (2) under the optimal threshold, >88% accuracy of index identification for winter wheat extraction was achieved; (3) due to improved extraction accuracy and resulting boundary range, NDPI and its corresponding optimal parameter (T = 0.05) performed the best. The process and results of this study have certain reference value for the study of winter wheat planting information change and the formulation of dynamic monitoring schemes in agricultural areas of QTP.
Highlights
This article is an open access articleWheat is a widely grown cereal worldwide, and its caryopsis is a staple for humankind [1,2,3]
Using the quality mosaic function of Google Earth Engine (GEE), it was found that when normalized difference vegetation index (NDVI) was designated as the quality band, image mosaic and cloud removal effects were more precise compared to when normalized differential phenology index (NDPI) or normalized difference greenness index (NDGI) were employed
In GEE, the image expression interface was used to program and carry out the vegetation index algorithms according to Equations (1)–(3) by calling the 678 atmospheric corrected images covering Minhe from the Sentinel 2A/B dataset; afterwards, NDVI, NDPI, and NDGI
Summary
This article is an open access articleWheat is a widely grown cereal worldwide, and its caryopsis is a staple for humankind [1,2,3]. 2022, 14, 343 stage [4,5,6] To address these issues, enhancement of crop planting information extraction and monitoring is essential, and could provide data that would form a basis for the formulation of polices to avert potential food crises. Remote sensing technologies offer the ability to quickly obtain spatial distribution information of surface objects across a continuous time series and, are commonly employed in the fields of crop planting identification and monitoring, and fruitful results have been obtained [7,8]. For the extraction of winter wheat information via remote sensing, methods are mainly divided into three categories: machine learning [9,10,11], time series vegetation indices [12,13,14], and classification via interpretation models [15,16,17]
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