Comparison of Regional Winter Wheat Mapping Results from Different Similarity Measurement Indicators of NDVI Time Series and Their Optimized Thresholds

Abstract

Generally, there is an inconsistency between the total regional crop area that was obtained from remote sensing technology and the official statistical data on crop areas. When performing scale conversion and data aggregation of remote sensing-based crop mapping results from different administrative scales, it is difficult to obtain accurate crop planting area that match crop area statistics well at the corresponding administrative level. This problem affects the application of remote sensing-based crop mapping results. In order to solve the above problem, taking Fucheng County of Hebei Province in the Huanghuaihai Plain of China as the study area, based on the Sentinel-2 normalized difference vegetation index (NDVI) time series data covering the whole winter wheat growth period, the statistical data of the regional winter wheat planting area were regarded as reference for the winter wheat planting area extracted by remote sensing, and a new method for winter wheat mapping that is based on similarity measurement indicators and their threshold optimizations (WWM-SMITO) was proposed with the support of the shuffled complex evolution-University of Arizona (SCE-UA) global optimization algorithm. The accuracy of the regional winter wheat mapping results was verified, and accuracy comparisons with different similarity indicators were carried out. The results showed that the total area accuracy of the winter wheat area extraction by the proposed method reached over 99.99%, which achieved a consistency that was between the regional remote sensing-based winter wheat planting area and the statistical data on the winter wheat planting area. The crop recognition accuracy also reached a high level, which showed that the proposed method was effective and feasible. Moreover, in the accuracy comparison of crop mapping results based on six different similarity indicators, the winter wheat distribution that was extracted by root mean square error (RMSE) had the best recognition accuracy, and the overall accuracy and kappa coefficient were 94.5% and 0.8894, respectively. The overall accuracies of winter wheat that were extracted by similarity indicators, such as Euclidean distance (ED), Manhattan distance (MD), spectral angle mapping (SAM), and spectral correlation coefficient (SCC) were 94.1%, 93.9%, 93.3%, and 92.8%, respectively, and the kappa coefficients were 0.8815, 0.8776, 0.8657, and 0.8558, respectively. The accuracy of the winter wheat results extracted by the similarity indicator of dynamic time warping (DTW) was relatively low. The results of this paper could provide guidance and serve as a reference for the selection of similarity indicators in crop distribution extraction and for obtaining large-scale, long-term, and high-precision remote sensing-based information on a regional crop spatial distribution that is highly consistent with statistical crop area data.

Early-Season Mapping of Winter Crops Using Sentinel-2 Optical Imagery

“…Winter wheat is an important food crop and has an extensive global planting area. Therefore, there have been many studies on remote sensing mapping of winter wheat [9,[17][18][19][20]; however, several problems exist, such as the limited spatial resolution of images, the utilisation of full-season images, heavy dependence on training data and a lack of consideration of winter canola and winter garlic crops. Winter canola and winter garlic can interfere with the remote sensing mapping of winter wheat due to their similar growth cycles [13,21,22].…”

A Rapid Model (COV_PSDI) for Winter Wheat Mapping in Fallow Rotation Area Using MODIS NDVI Time-Series Satellite Observations: The Case of the Heilonggang Region

“…The proposed COV_PSDI model achieved rapid winter wheat mapping in fallow rotation areas with frequent changes of cropland distribution and improved accuracy. As shown in our study, COV_PSDI outperformed the existing time-series indices (CBAH and WWI) [33,36] for winter wheat and mapping methods (SAM and ISODATA) [77,78]. In CBAH, the NDVI values of the three growth stages (seeding, heading and harvesting) were utilized for winter wheat mapping.…”

Optimal Bands Combination Selection for Extracting Garlic Planting Area with Multi-Temporal Sentinel-2 Imagery

“…The accuracy of the crop area extraction and mapping results is mainly verified from two aspects: crop identification accuracy (i.e., location accuracy) and crop area estimation accuracy (i.e., total area accuracy) to evaluate the accuracy of the spatial distribution extraction results of garlic and the degree of consistency with the statistical data on crop planting area [ 48 ]. Crop identification accuracy was primarily used to assess the accuracy of crop spatial distribution extraction results, and the indicators primarily included the overall accuracy (OA), kappa coefficient (Kappa), producer accuracy (PA), and user accuracy (UA), all of which were obtained from the confusion matrix [ 14 , 49 ].…”