## 3 Methods  
This research attempts to propose a strategy for the long-term monitoring of the wetland ecosystem and detecting the abrupt wetland vegetation changes under global warming using the fused observations. The method consists of three main steps: (1) collecting and preprocessing raw Landsat and MODIS observations of the period 2000-2019 to predict high spatial and temporal resolution time series; (2) blending Landsat and MODIS NDVI images to obtain the monthly cloud-free fused time series (30-m resolution) over the study area using the FSDAF algorithm; (3) using the BEAST method to decompose the NDVI time series, trend and seasonality of wetland vegetation over the east Dongting Lake, before analyzing the vegetation dynamics from the aspects of duration, distribution, elevation and climate. The flowchart of the proposed approach is shown in Figure 4.  
Generation of Monthly Cloud-Free NDVI Time Series Based on the Cross-Fusion Method and FSDAF Algorithm  
In order to suppress the influence of cloudy and rainy weather on wetland vegetation dynamics detection, the Flexible Spatiotemporal Data Fusion (FSDAF) method was adopted to generate the consecutive monthly NDVI time series. The FSDAF method blends the coarse- and fine-resolution images at time t\({}_{1}\) with the coarse-resolution image at the prediction time t\({}_{2}\) to predict a fine-resolution image at t\({}_{2}\). Five main steps are included in the FSDAF method: (1) classify the fine-resolution image at t\({}_{1}\) using the iterative self-organizing data analysis technique algorithm (an unsupervised classification algorithm: cluster the similar pixels based on distance function to accomplish image classification) [53]; (2) detect the temporal change of each class in the coarse-resolution images between t\({}_{1}\) and t\({}_{2}\); (3) predict the fine-resolution image at t\({}_{2}\) and calculate residuals at each coarse pixel; (4) distribute the temporal prediction residuals to fine pixels by the Thin Plate Spline interpolator; (5) obtain the final prediction of fine-resolution image at t\({}_{2}\). Detailed descriptions of FSDAF can be found in [19].  
Figure 4: The flowchart of the proposed approach.  
The performance of the fused data is greatly affected by the selected input images [54]. Some studies selected the base image pair acquired at the closest date to the prediction image date to ensure the highest similarity between them. Some research determined the input base images by the correlation coefficient between the coarse images acquired on the base date and that on the prediction date [55; 56]. The \"cross-fusion\" algorithm takes both similarity and correlation criteria into consideration, which is superior to other input image pair selection methods [54]. In order to improve the fusion result, the cross-fusion method was exploited to quantitatively consider the consistency between the input images acquired at the base time and that acquired at the prediction time, which can automatically determine the optimal input image pairs for FSDAF fusion. In the cross-fusion, a number of base image pairs were selected as candidates from all available images in the light of similarity criterion. All candidate base image pairs were used to predict the fused image by weighted average.  
In this study, we generated a monthly cloud-free NDVI time series (30-m resolution) by cross fusing Landsat and MODIS images using the FSDAF algorithm. Then, the fusion results were applied to enhance the data credibility and reconstruct NDVI time series for dynamic monitoring.