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.
Highlights
In recent years, with the gradual expansion of research on climate change and food security, statistical data on crop planting areas have become irreplaceable [1,2,3]
The workflow consisted of the following steps: (1) obtaining the reference and actual cross-correlation curves according to the multitemporal Sentinel-2 data and the cross correlogram spectral matching (CCSM) algorithm; (2) calculating the similarity between two cross-correlation curves and constructing a winter wheat extraction model according to the similarity; (3) optimizing the threshold value in the winter wheat extraction model using the shuffled complex evolution-University of Arizona (SCE-user accuracy (UA)) global optimization algorithm; and, (4) extracting and mapping the spatial distribution of regional winter wheat and performing an accuracy assessment (Figure 3)
The minimum objective function values of Manhattan distance (MD), Euclidean distance (ED), root mean square error (RMSE), and dynamic time warping (DTW) were 0, and the minimum objective function values of spectral angle mapping (SAM) and spectral correlation coefficient (SCC) were only 100 m2. These results indicated that the method proposed for regional winter wheat extraction and mapping based on the threshold optimization of the normalized difference vegetation index (NDVI) time series similarity in this study could ensure high consistency between the regional crop area that was extracted by remote sensing and the regional crop area statistical data
Summary
With the gradual expansion of research on climate change and food security, statistical data on crop planting areas have become irreplaceable [1,2,3]. Being affected by the selection of sampling factors in sampling extrapolation technology [12,13], remote sensing mixed pixels, and complex natural conditions [14], the published official statistics of crop area have always been inconsistent with the crop planting area data that were obtained from crop recognition based on full-coverage remote sensing data [15,16,17] This inconsistency affects the ability to obtain accurate crop planting area data that match the crop area statistics well at a given administrative level when performing scale conversion and data aggregation while using remote sensing-based crop mapping results from different administrative scales (such as from the county level to city level, or from the county level to province level). These problems have hindered the application of remote sensing-based crop mapping results [18]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
