Plastic greenhouses (PGs) are widely built near cities in China to produce vegetables and fruits. In order to promote sustainable agriculture, rural landscape construction, and better manage water resources, numerous remote sensing methods have been developed to identify and monitor the distribution of PGs, of which many map PGs based on spectral responses and geometric shapes. In this study, we proposed a new fine- and coarse-scale mapping approach using two-temporal Sentinel-2 images with various seasonal characteristics and a one-dimensional convolutional neural network (1D-CNN). Having applied this approach in a pilot area study, the results were summarized as follows: (1) A time-series analysis of Sentinel-2 images showed that the reflectance of greenhouses changes during crop growth and development. In particular, the red-edge and near-infrared bands undergo a significant increase and then decrease during the whole crop growth period. Thus, two critical period images, containing a substantial difference in greenhouse reflectance, were sufficient to carry out an accurate and efficient mapping result. (2) The 1D-CNN classifier was used to map greenhouses by capturing subtle details and the overall trend of the spectrum curve. Overall, our approach showed higher classification accuracy than other approaches using support vector machines (SVM) or random forests (RF). In addition, the greenhouse area identified was highly consistent with the existing surfaces observed in very high-resolution images, with a kappa co-efficient of 0.81. (3) The narrow band feature differences (red-edge and near infrared narrow bands) in two-temporal Sentinel-2 images played a significant role in high-precision greenhouse mapping. The classification accuracy with narrow band features was much better than the maps produced without narrow band features. This scheme provided a method to digitize greenhouse precisely and publish its statistics for free, which enable advanced decision support for agriculture management.

Accurate description of surface soil moisture (SSM) in vegetation-covered areas is of great significance to water resource management and drought monitoring. To remove the effect of vegetation on SSM estimation, the vegetation index obtained from Sentinel-2 (S2) was applied for vegetation water content (VWC) estimation. The VWC model was substituted into the water cloud model (WCM), and thus, the SSM estimation model was developed based on the WCM. The methodology was tested at Daxing, Beijing, and Gu’an, Hebei, in which training and validation data of SSM were acquired by in situ measurements. The results can be described as follows: (1) For the vegetation-covered areas, the Modified Chlorophyll Absorption Ratio Index (MCARI) obtained from the B3, B4, and B5 bands of S2 was the most suitable for removing the influence of vegetation on SSM estimation; (2) Compared to Sentinel-1 (S1) vertical–horizontal (VH) polarization, vertical–vertical (VV) polarization was more suitable for SSM estimation and achieved higher accuracy; (3) The developed model could be used to estimate SSM under crop cover with high accuracy, which indicated the correlation coefficients (R²) between in situ measured and estimated SSM were 0.867, the root mean square error (RMSE) was 0.028 cm³/cm³, and the MAE was 0.023 cm³/cm³. Thus, this methodology has the potential for SSM estimation in vegetated areas.