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River discharge plays a vital role in the Earth's water cycle. Recent studies have sought to estimate river discharge using optical sensors onboard satellites, typically deriving discharge from multispectral data after excluding many images that are contaminated by cloud or affected by the scan-line-corrector (SLC) failure on Landsat 7. As such, time series of estimated discharge generally include a high proportion of missing values. To overcome these issues, we propose an easy and feasible methodology to temporally densify the time series of discharge estimates by repairing cloud-contaminated and SLC-affected images on the Google Earth Engine platform. The middle Yangtze River (MYR) was chosen as the study area to demonstrate and evaluate the performance of the methodology. First, different water indices were compared to select the most appropriate indicator for water surface retrieval along the MYR. The Pekel method shows the best performance for extracting the water surface when turbid or under thin, semi-transparent clouds. Second, the performances of the Tourian method and the Manning equation for estimating the discharge were compared when using only good-quality images, yielding Nash-Sutcliffe efficiency (Nash) coefficients of 0.69 and 0.79, respectively. Images affected by the SLC and over 50% cloud cover accounted for 41% of 532 records used from 1986 to 2019. The repaired, temporally complete time series of discharge estimates by the Manning equation corresponds to a correlation coefficient of 0.86, a Nash coefficient of 0.73, and a root mean square error of 5697 m3/s from 2000 to 2019. These results show the strong potential of this method for estimating the discharge of rivers with known cross-sectional and the morphological conditions worldwide. [ABSTRACT FROM AUTHOR]

• A Naive Bayes classification was introduced to fill gaps in EO-derived water image. • The correlation between connected or disconnected water bodies is considered. • Rivers, reservoirs or lakes were selected to verify the method. • It will contribute to the high-frequent monitoring of surface water. Optical remote sensing images are a common data sources for surface water monitoring, while they are easily contaminated by clouds, cloud shadows, terrain shadows, etc., resulting in spatial gaps in surface water images. This paper proposes a surface water gap-filling method based on Naive Bayes classification. It uses the historical cloud-free binary (water, non-water) surface water images as prior data and the uncontaminated pixels in the partially contaminated ternary (water, non-water, contaminated pixels) surface water image as evidence to identify the category of gap pixels to achieve the purpose of gap-filling. This method considers the relationship between disconnected water bodies and does not depend on terrain data. When the image is heavily covered by clouds, this method can also reconstruct the complete water extent accurately. Five study areas with different scenarios including rivers, lakes or reservoirs, are selected to evaluate the method. Results show that the average gap-filling accuracy in all five study areas is over 90 %. After gap-filling, the time series of surface water area presents a good correlation with the time series of water level (e.g., the coefficient of determination R 2 = 0.95 in the Dartmouth reservoir). The proposed method is proved effective in filling gaps caused by clouds, cloud shadows and terrain shadows in surface water image, and it would be suitable for high-frequency surface water monitoring and near real-time surface water mapping. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is a biophysical variable related to atmosphere-biosphere exchange of CO2. One way to obtain LAI value is by the Moderate Resolution Imaging Spectroradiometer (MODIS) biophysical products. In this paper, we use this product to improve the physiological principles predicting growth model within a Gaussian Bayesian network (GBN) setup. The MODIS time series, however, contains gaps caused by persistent clouds, cloud contamination, and other technique problems. We used the Expectation Maximization (EM) algorithm to estimate these missing values. During a period of 26 successive months, the EM algorithm is applied to four different cases: successively and not successively missing values during two different winter seasons, successively and not successively missing values during one spring season, and not successively missing values during the full study. Results show that the maximum value of the averaged absolute error between the original values and those estimated equals 0.16. This low value indicates that the estimated values well represent the original values. Moreover, the root mean square error of the GBN output reduces from 1.57 to 1.49 when performing the EM algorithm to estimate the not successively missing values. We conclude that the EM algorithm within a GBN can adequately handle missing MODIS LAI values and improves the estimation of the LAI. [ABSTRACT FROM AUTHOR]