In recent years, the Yellow River Delta has been affected by invasive species Spartina alterniflora (S. alterniflora), resulting in a fragile ecological environment. It is of great significance to monitor the ground object types in the Yellow River Delta wetlands. The classification accuracy based on Synthetic Aperture Radar (SAR) backscattering coefficient is limited by the small difference between some ground objects. To solve this problem, a decision tree classification method for extracting the ground object types in wetland combined time series SAR backscattering and coherence characteristics was proposed. The Yellow River Delta was taken as the study area and the 112 Sentinel-1A GRD data with VV/VH dual-polarization and 64 Sentinel-1A SLC data with VH polarization were used. The decision tree method was established, based on the annual mean VH and VV backscattering characteristics, the new constructed radar backscattering indices, and the annual mean VH coherence characteristics were suitable for extracting the wetlands in the Yellow River Delta. Then the classification results in the Yellow River Delta wetlands from 2018 to 2021 were obtained using the new method proposed in this paper. The results show that the overall accuracy and Kappa coefficient of the proposed method w5ere 89.504% and 0.860, which were 9.992% and 0.127 higher than multi-temporal classification by Support Vector Machine classifier. Compared with the decision tree without coherence, the overall accuracy and Kappa coefficient were improved by 8.854% and 0.108. The spatial distributions of wetland types in the Yellow River Delta from 2018 to 2021 were obtained using the constructed decision tree. The spatio-temporal evolution analysis was conducted. The results showed that the area of S. alterniflora decreased significantly in 2020 but it increased to the area of 2018 in 2021. In addition, S. alterniflora seriously affected the living space of Phragmites australis (P. australis) and in 4 years, 10.485 km2 living space of P. australis was occupied by S. alterniflora. The proposed method can provide a theoretical basis for higher accuracy SAR wetland classification and the monitoring results can provide an effective reference for local wetland protection.

Wetlands in estuary deltas functionally protect biodiversity, store water, and regulate ecological balance. However, wetland monitoring accuracy is low when using only synthetic aperture radar (SAR) images or optical images. This study proposes a novel method for extracting ground objects in a wetland using principal component analysis (PCA) and random forest (RF) classification, which combines the features of fully polarimetric SAR images and optical images. Firstly, polarization decomposition features and texture features were extracted based on polarimetric SAR data, and spectral features were extracted based on optical data. Secondly, the optical image was registered to SAR image. Then PCA was performed on the nine polarimetric features of the SAR images and the four spectral features of the optical images to obtain the first two principal components of each. After combining these components, a RF classification algorithm was used to extract the objects. The objects in the Yellow River Delta wetland were successfully extracted using our proposed method with Gaofen-3 fully polarimetric SAR data and Sentinel-2A optical data acquired in November 2018. The overall accuracy of the proposed method was 86.18%, and the Kappa coefficient was 0.84. This was an improvement of 18.96% and 0.22, respectively, over the GF-3 polarimetric features classification, and 11.02% and 0.13, respectively, over the Sentinel-2A spectral features classification. Compared with the results of the support vector machine, maximum likelihood, and minimum distance classification algorithms, the overall accuracy of the RF classification based on joint features was 2.03, 5.69, and 23.36% higher, respectively, and the Kappa coefficient was 0.03, 0.07, and 0.27 higher, respectively. Therefore, this novel method can increase the accuracy of the extraction of objects in a wetland, providing a reliable technical means for wetland monitoring.

Scientific and accurate estimation of rice yield is of great significance to food security protection and agricultural economic development. Due to the weak penetration of high frequency microwave band, most of the backscattering comes from the rice canopy, and the backscattering coefficient is highly correlated with panicle weight, which provides a basis for inversion of wet biomass of rice ear. To solve the problem of rice yield estimation at the field scale, based on the traditional water cloud model, a modified water-cloud model based on panicle layer and the radar data with Ku band was constructed to estimate rice yield at panicle stage. The wet weight of rice ear scattering model and grain number per rice ear scattering model were constructed at field scale for rice yield estimation. In this paper, the functional area of grain production in Xiashe Village, Xin'an Town, Deqing County, Zhejiang Province, China was taken as the study area. For the first time, the MiniSAR radar system carried by DJI M600 UAV was used in September 2019 to obtain the SAR data with Ku band under polarization HH of the study area as the data source. Then the rice yield was estimated by using the newly constructed modified water-cloud model based on panicle layer. The field investigation was carried out simultaneously for verification. The study results show: the accuracies of the inversion results of wet weight of rice ear scattering model and grain number per rice ear scattering model in parcel B were 95.03% and 94.15%; and the accuracies of wet weight of rice ear scattering model and grain number per rice ear scattering model in parcel C+D+E were over 91.8%. In addition, different growth stages had effects on yield estimation accuracy. For rice at fully mature, the yield estimation accuracies of wet weight of ear and grain number per ear were basically similar, both exceeding 94%. For rice at grouting stage, the yield estimation accuracy of wet weight of ear was 92.7%, better than that of grain number per ear. It was proved that it can effectively estimate rice yield using the modified water-cloud model based on panicle layer constructed in this paper at panicle stage at field scale.

In the past 35 years, the natural coastline along Jiaozhou Bay has undergone extensive changes under the influence of human activities, and the coastal wetland area has been drastically reduced. Therefore, it is of great importance to study the spatio-temporal changes of the Jiaozhou Bay coastline, and their trends and causes, for sustainable economic development and the rational utilization of coastal resources. This paper constructed a comprehensive method for extracting the coastline information and change analysis based on long time series remote sensing data. Based on multi-spectral optical data and dual-polarization SAR data, the Normalized Difference Water Index (NDWI) and the Sentinel-1 Dual-polarized Water body Index (SDWI) combined with the Otsu threshold segmentation method were used to automatically extract the spatial distribution of coastline. The U-Net semantic segmentation model was used to classify the land cover types in the land direction of the coastline to count the coastline types. The End Point Rate (EPR) and Linear Regression Rate (LRR) were used to analyze the coastline changes, and the land reclamation was calculated according to the changing trends. The Pearson coefficient was used to study the reasons for the coastline changes. With an average time interval of 5 years, eight coastlines of Jiaozhou Bay in different years were extracted, and the coastline types were obtained. Then, the changes of the coastlines in Jiaozhou Bay from 1987 to 2022 were analyzed. The results show that: 1) Coastline type information provides important information for analyzing the coastline changes in long time series, and coastline information can be effectively extracted using multi-spectral optical data and dual-polarization SAR data. When the resolution of remote sensing data is 30m, the average error of the two types of data is better than one pixel, and the error between the data is about 1-2 pixels. 2) Based on the U-Net model, the overall accuracy of coastline classification using multi-spectral optical data and dual-polarization SAR data is 94.49% and 94.88%, respectively, with kappa coefficients of 0.9143 and 0.8949. 3) In the past 35 years, Jiaozhou Bay area has shown an obvious trend towards the ocean, with an average annual expansion of 16.723m. 4) The coastline of the Jiaozhou Bay area is dynamic. Due to the frequent human activities, the coastline has been reconstructed on a large scale, and the length of artificial coastline has increased significantly. The proportion of artificial coastline length has increased from 33.72% in 1987 to 59.33% in 2022. 5) In the past 35 years, the land reclamation area has reached 41.45km2, of which Shibei District, Licang District, and Huangdao District are the three most frequent areas, with an area of 34.62 km2.