Abstract
Rapid and accurate monitoring of spatial distribution patterns of winter wheat over a long period is of great significance for crop yield prediction and farmland water consumption estimation. However, weather conditions and relatively long revisit cycles often result in an insufficient number of continuous medium-high resolution images over large areas for many years. In addition, the cropland pattern changes frequently in the fallow rotation area. A novel rapid mapping model for winter wheat based on the normalized difference vegetation index (NDVI) time-series coefficient of variation (NDVI_COVfp) and peak-slope difference index (PSDI) is proposed in this study. NDVI_COVfp uses the time-series index volatility to distinguish cultivated land from background land-cover types. PSDI combines the key growth stages of winter wheat phenology and special bimodal characteristics, substantially reducing the impact of abandoned land and other crops. Taking the Heilonggang as an example, this study carried out a rapid mapping of winter wheat for four consecutive years (2014–2017), and compared the proposed COV_PSDI with two state-of-the-art methods and traditional methods (the Spectral Angle Mapping (SAM) and the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA)). The verification results revealed that the COV_PSDI model improved the overall accuracy (94.10%) by 4% compared with the two state-of-art methods (90.80%, 89.00%) and two traditional methods (90.70%, 87.70%). User accuracy was the highest, which was 93.74%. Compared with the other four methods, the percentage error (PE) of COV_PSDI for four years was the lowest in the same year, with the minimum variation range of PE being 1.6–3.6%. The other methods resulted in serious overestimation. This demonstrated the effectiveness and stability of the method proposed in the rapid and accurate extraction of winter wheat in a large area of fallow crop rotation region. Our study provides insight for remote sensing monitoring of spatiotemporal patterns of winter wheat and evaluation of “fallow rotation” policy implementation.
Highlights
Winter wheat is one of the most important food crops in the world and is directly related to global and regional food security issues [1,2]
When the proportion of winter wheat was over 50%, the values of NDVI_COVfp and slope difference index (SDI) varied slowly
After the proportion of winter wheat was less than 50%, the NDVI_COVfp and SDI values decreased and increased rapidly, respectively
Summary
Winter wheat is one of the most important food crops in the world and is directly related to global and regional food security issues [1,2]. 2021, 13, 4870 of agroecosystems to the terrestrial carbon cycle, and assessing the impact of global changes on regional agricultural production [5]. The monoculture cropping pattern has led to severe over-exploitation of the groundwater table, which is severe in northern China [6,7]. In response to this issue, China has adopted a policy of “fallow crop rotation” to reduce groundwater extraction and agricultural non-point source pollution [8,9]. It is important to quickly and accurately obtain the spatiotemporal patterns of winter wheat during a long time for agricultural water consumption estimation, ecological function assessment and agricultural policy formulation [10,11]
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