Optimization of Characteristic Phenological Periods for Winter Wheat Extraction Using Remote Sensing in Plateau Valley Agricultural Areas in Hualong, China

It is important to develop or validate remote sensing methods to explore agricultural management and food self-sufficiency in the agricultural areas of the Qinghai-Tibet Plateau under the influence of global change, ecological protection, and socio-economic development. Studies on the use of remote sensing to monitor crop planting on the Qinghai-Tibetan Plateau are limited, with inconclusive results. Therefore, in this study, we analyzed Sentinel-2A/B images and field survey data in Hualong, China (located in Hehuang Valley, Qinghai-Tibetan Plateau) for winter wheat identification and verification at different spatial scales based on the time series of the normalized difference phenology index (NDPI) and dynamic time warping (DTW) algorithm. The characteristic phenological period and the corresponding DTW threshold were optimized using remote sensing data extracted for winter wheat. The results showed that NDPI corresponding to the jointing-heading stage, grouting-harvesting stage, and jointing-harvesting stage with DTW could identify winter wheat regardless of whether the spatial scale was a single quadrat, a combination of two quadrats, or the entire study area. The NDPI corresponding to the jointing-heading stage (corresponding DTW threshold T = 0.158) could generate a relatively rational winter wheat map; the NDPI corresponding to the time series of the grouting-harvesting stage (combined with DTW threshold T = 0.195) could detect a planting area with relatively high accuracy when supported by cultivated land, which matches the statistical reporting of the winter wheat area data. Similarly, with the support of cultivated land data, the planted area could be identified early based on the phenological characteristics of winter wheat before overwintering; however, the extraction scheme needs to be optimized further.

Automated summary

It is crucial to develop or validate remote sensing methods for studying agricultural management and food self-sufficiency in the agricultural areas of the Qinghai-Tibet Plateau, taking into account global change, ecological protection, and socio-economic development. There is limited research on using remote sensing to monitor crop planting in this region, leading to inconclusive results. Therefore, this study analyzed Sentinel-2A/B images and field survey data in Hualong, China to identify and verify winter wheat using the normalized difference phenology index (NDPI) and dynamic time warping (DTW) algorithm at different spatial scales. The results showed that NDPI corresponding to specific growth stages of winter wheat, along with DTW, could accurately identify the planted area.