Winter wheat mapping with globally optimized threshold under total quantity constraint of statistical data

Abstract

Using remote sensing technology to accurately obtain the spatial distribution information of crops in a large region is critical in improving the research level of resources and environment, strengthening the reaction capability of climate change, and ensuring national food security. Threshold method is a common method used to extract crop spatial distribution information based on time-series NDVI remote sensing data. However, several subjectivities and uncertainties were observed in traditional threshold methods when the threshold values were determined by humans, which hindered the improvement of threshold method identification accuracy. Considerable studies on determining the significant threshold value have been conducted in recent years. However, the accuracy and automation should be improved when the threshold values are determined. Considering the above problems, Jingxian County of Hengshui City in Hebei Province, which is the main grain producing region in China, was selected as the typical experimental area. A global optimization algorithm was applied to select the threshold value in the threshold model, and a crop distribution mapping method using global optimization algorithms based on threshold detection was developed. In this study, winter wheat was selected as the research crop, and domestic Gaofen-1 satellite images that cover the entire winter wheat growing season were used as major data source. A new spatial distribution mapping method for winter wheat based on time-series NDVI data for the automatic optimization of threshold model parameters was proposed in this study by using the global optimization algorithm Shuffled Complex Evolution-University of Arizona (SCE-UA) and crop area statistics as the total quantity constraint. First, the NDVI curves of winter wheat growth season were established, and the key phenological characteristics of winter wheat were extracted based on crop phenological and training sample data. Second, the general threshold model of winter wheat spatial distribution extraction was established, and the threshold parameters to be optimized were determined based on the seasonal variation and characteristics of time-series winter wheat NDVI at key phenological stages (such as seeding, tillering, jointing, heading, and maturation stages). Third, the optimal parameters of winter wheat spatial distribution model were obtained by the global optimization algorithm SCE-UA by using the crop planting area statistics at county level as total quantity constraint data and objective function. Finally, the optimal parameters of winter wheat threshold model were obtained in the study region, the spatial distribution of winter wheat was extracted by using the optimized threshold parameters, and the winter wheat distribution extraction results were validated by the ground verification samples. The final validation results showed that the accuracy of winter wheat identification results reached 99.99%, which proved that proposed threshold parameter optimization method had a good total quantity constraint effect and other classification accuracies had reached a high level. The overall accuracy and kappa coefficient were 97.03% and 0.94, respectively. Compared with the traditional threshold, support vector machine, and maximum likelihood methods, the overall classification accuracy of the proposed method increased by 4.55%, 2.43%, and 0.15%, respectively. The kappa coefficient of the proposed method increased by 0.12, 0.06, and 0.01, respectively. The above performances indicated that the crop distribution mapping optimization method based on the threshold model parameters of statistical data total quantity constraint and global optimization algorithms was effective and feasible to accurately obtain the spatial distribution mapping results of crops in a large region. This study can serve as basis to improve the accuracy and automation level of crop spatial distribution identification in China and provide several technical support and recommendations for other studies on crop spatial distribution extraction and crop mapping under complex planting pattern at large scale.