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The Gravity Recovery and Climate Experiment (GRACE) mission provided monthly global estimates of the vertically integrated terrestrial water storage with about 300–400-km horizontal resolution between 2002 and 2017. Since 2009, the Soil Moisture and Ocean Salinity (SMOS) mission observes global L-band brightness temperatures, which are sensitive to near-surface soil moisture, with a revisit time of 1–3 days at a nominal 43-km spatial resolution. This work investigates if the multi-sensor assimilation of these observations into the Catchment land surface model can improve the estimation of 0–5 cm “surface” soil moisture, 0–100 cm “rootzone” soil moisture, and shallow (unconfined) groundwater levels. Single-sensor GRACE or SMOS assimilation and multi-sensor GRACE+SMOS assimilation experiments were performed over the continental U.S. for 6 years (July 2010–June 2016). GRACE data assimilation mostly improves estimates of shallow groundwater, whereas SMOS data assimilation mainly improves estimates of surface soil moisture. The benefits introduced by the single-sensor assimilation are merged in the multi-sensor assimilation experiment, suggesting that better and more consistent soil moisture and groundwater estimates can be achieved when multiple observation types are assimilated. Interestingly, in the multi-sensor GRACE+SMOS experiment, the water storage increments introduced by the GRACE analysis and the SMOS analysis are anti-correlated. That is, when the GRACE assimilation increments remove water from the overall profile storage, the SMOS assimilation increments add water to it, and vice versa. This anti-correlation could be caused by the SMOS analysis trying to undo the increments from the GRACE analysis. ispartof: REMOTE SENSING OF ENVIRONMENT vol:227 pages:12-27 status: published

This article presents an online teaching tool that introduces students to basic concepts of remote sensing and its applications in hydrology. The learning module is intended for junior/senior undergraduate students or junior graduate students with no (or little) prior experience in remote sensing, but with some basic background of environmental science, hydrology, statistics, and programming. This e-learning environment offers background content on the fundamentals of remote sensing, but also integrates a set of existing online tools for visualization and analysis of satellite observations. Specifically, students are introduced to a variety of satellite products and techniques that can be used to monitor and analyze changes in the hydrological cycle. At completion of the module, students are able to visualize remote sensing data (both in terms of time series and spatial maps), detect temporal trends, interpret satellite images, and assess errors and uncertainties in a remote sensing product. Students are given the opportunity to check their understanding as they progress through the module and also tackle complex real-life problems using remote sensing observations that professionals and scientists commonly use in practice. The learning tool is implemented in HydroLearn, an open-source, online platform for instructors to find and share learning modules and collaborate on developing teaching resources in hydrology and water resources.

This paper presents a newly proposed data assimilation method for historical snow water equivalent SWE estimation using remotely sensed fractional snow-covered area fSCA. The newly proposed approach consists of a particle batch smoother (PBS), which is compared to a previously applied Kalman-based ensemble batch smoother (EnBS) approach. The methods were applied over the 27-yr Landsat 5 record at snow pillow and snow course in situ verification sites in the American River basin in the Sierra Nevada (United States). This basin is more densely vegetated and thus more challenging for SWE estimation than the previous applications of the EnBS. Both data assimilation methods provided significant improvement over the prior (modeling only) estimates, with both able to significantly reduce prior SWE biases. The prior RMSE values at the snow pillow and snow course sites were reduced by 68%–82% and 60%–68%, respectively, when applying the data assimilation methods. This result is encouraging for a basin like the American where the moderate to high forest cover will necessarily obscure more of the snow-covered ground surface than in previously examined, less-vegetated basins. The PBS generally outperformed the EnBS: for snow pillows the PBS RMSE was ~54% of that seen in the EnBS, while for snow courses the PBS RMSE was ~79% of the EnBS. Sensitivity tests show relative insensitivity for both the PBS and EnBS results to ensemble size and fSCA measurement error, but a higher sensitivity for the EnBS to the mean prior precipitation input, especially in the case where significant prior biases exist.

With more than 30 years of imagery, the Landsat database offers potential insights into changes in the global cryosphere. In this study, a Landsat-based sub-pixel fractional snow covered area retrieval model based on spectral unmixing is applied and validated over a southern Andes study region. An application analyzing minimum annual snow/ice covered areas is presented as an example of the utility of the existence of such a model and the Landsat database. The model was validated using high-resolution imagery aggregated to the nominal Landsat resolution, showing good correlation for both fresh snow and ice/old snow presence. Using annual minimum snow/ice retrievals from Landsat 5 and 7, the presence of trends in each snow/ice-covered pixel area was assessed for the 1986–2013 historical period, and three metrics were examined: pixels with significant negative trends, total area loss, and areas with any absolute negative change. Significant negative (area reduction) trends in most of the glacier fronts were found, with a loss of approximately 15% of the original 241 km2 glacierized/snow area between 1986 and 2011. Trends in fractional values were compared with those derived from traditional binary classification of snow and significant discrepancies were observed. Even though the binary classification identified a greater total areal loss (33 vs. 23 km2) when comparing scenes from the beginning and the end of the study period, the fractional algorithm identified a larger number of pixels with significant negative trends between 1986 and 2013 (42.3 vs. 10.5 km2). The binary algorithm resulted in consistent overestimations of ice and snow area and does not offer the ability for identifying subpixel changes. The method could enable future monitoring of Andean and other glaciers and snow cover at a sub-pixel scale over large scales, and with Landsat 8 in orbit a continuous stream of more than 30 years of data is already available.