The recognition method of wine grape plots based on multi-temporal Sentinel-1/2

Ningxia Hui Autonomous Region is a key wine grape production area in China. Obtaining timely and accurate information on the distribution of wine grape plots is crucial for adjusting planting structures and estimating yields. This study addresses the challenge of identifying fragmented and irregularly shaped wine grape cultivation areas by utilizing Sentinel-1 and Sentinel-2 remote sensing images on the Google Earth Engine (GEE) cloud platform. Firstly, the study selected three primary wine grape growing regions in Ningxia Hui Autonomous Region: Yongning County and Xixia District as test areas, and Qingtongxia City as the validation area. Sample points were randomly selected within the test area and divided into training and validation sets at a 7:3 ratio. A total of 120 random sample points were selected within the study area to analyze the temporal variation curves of the normalized difference vegetation index (NDVI) reflectance from 2020 Sentinel-2 imagery, as well as the VV and VH polarization backscatter coefficients from Sentinel-1 imagery, to determine the selection dates for multi-temporal images. Based on this foundation, five categories of feature variables were extracted: spectral features (18), vegetation index features (48), texture features (18), polarization features (14), and terrain features (4). Secondly, the Gini index algorithm based on random forests is used to analyze the importance of all feature variables. Features are added in order of importance, from highest to lowest, to achieve the highest classification accuracy and determine the optimal number of features. This optimal number is then used to construct a selected feature set. Additionally, the cumulative feature values of Sentinel-1 and Sentinel-2 images on different dates are calculated. Select the image date with the highest feature score to construct a single temporal feature set. Finally, seven different feature combination schemes were constructed based on single-temporal and multi-temporal feature sets to identify wine grape plots. The optimal feature combination was selected to explore the impact of multi-source and multi-temporal images on the identification of wine grapes, and a spatial distribution map of wine grapes in the study area was created. The study results indicate that combining multisource remote sensing imagery significantly enhances the accuracy of identifying wine grapes compared to using only Sentinel-1 or Sentinel-2 imagery features. Additionally, using multitemporal feature combinations improves classification outcomes by reducing misclassification and omission errors compared to single-temporal feature combinations. Among the seven feature combination schemes, the optimal feature combination demonstrated the best identification performance. In the test area, it achieved an overall accuracy of 95.46%, a Kappa coefficient of 0.94, a user accuracy of 93.33%, and a producer accuracy of 95.06%. The relative error between the extracted area and the area reported in the statistical yearbook is approximately 9.47 percentage points. In the validation area, using sample points from Qingtongxia City, the results indicated an overall accuracy of 91.89%, a Kappa coefficient of 0.89, a user accuracy of 89.00%, and a producer accuracy of 95.93%. The relative error between the extracted area and the area reported in the statistical yearbook is approximately 8.15 percentage points. This study provides valuable insights into classifying and identifying wine grapes through multi-temporal remote sensing images.