The first issue of WRR appeared eight years after the launch of Sputnik, but by WRR's 25th anniversary, only seven papers that used remote sensing had appeared. Over the journal's second 25 years, that changed remarkably, and remote sensing is now widely used in hydrology and other geophysical sciences. We attribute this evolution to production of data sets that scientists not well versed in remote sensing can use, and to educational initiatives like NASA's Earth System Science Fellowship program that has supported over a thousand scientists, many in hydrology. We review progress in remote sensing in hydrology from a water balance perspective. We argue that progress is primarily attributable to a creative use of existing and past satellite sensors to estimate such variables as evapotranspiration rates or water storage in lakes and reservoirs and to new and planned missions. Recent transforming technologies include the Gravity Recovery and Climate Experiment (GRACE), the European Soil Moisture and Ocean Salinity (SMOS) and U.S. Soil Moisture Active Passive (SMAP) missions, and the Global Precipitation Measurement (GPM) mission. Future missions include Surface Water and Ocean Topography (SWOT) to measure river discharge and lake, reservoir, and wetland storage. Measurement of some important hydrologic variables remains problematic: retrieval of snow water equivalent (SWE) from space remains elusive especially in mountain areas, even though snow cover extent is well observed, and was the topic of 4 of the first 5 remote sensing papers published in WRR. We argue that this area deserves more strategic thinking from the hydrology community.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2015WR017616

Inroads of remote sensing into hydrologic science during the WRR era

Water resources management (WRM) for sustainable development presents many challenges in areas with sparse in situ monitoring networks. The exponential growth of satellite based information over the past decade provides unprecedented opportunities to support and improve WRM. Furthermore, traditional barriers to the access and usage of satellite data are lowering as technological innovations provide opportunities to manage and deliver this wealth of information to a wider audience. We review data needs for WRM and the role that satellite remote sensing can play to fill gaps and enhance WRM, focusing on the Latin American and Caribbean as an example of a region with potential to further develop its resources and mitigate the impacts of hydrological hazards. We review the state-of-the-art for relevant variables, current satellite missions, and products, how they are being used currently by national agencies across the Latin American and Caribbean region, and the challenges to improving their utility. We discuss the potential of recently launched, upcoming, and proposed missions that are likely to further enhance and transform assessment and monitoring of water resources. Ongoing challenges of accuracy, sampling, and continuity still need to be addressed, and further challenges related to the massive amounts of new data need to be overcome to best leverage the utility of satellite based information for improving WRM.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2017WR022437

Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data-Poor Regions

The selection of calibration method is one of the main factors influencing measurement accuracy of soil properties estimation in visible and near infrared reflectance spectroscopy. In this study, the performance of three regression techniques, namely, partial least-squares regression (PLSR), support vector regression (SVR), and multivariate adaptive regression splines (MARS) were compared to identify the best method to assess organic matter (OM) and clay content in the salt-affected soils. One hundred and two soil samples collected from Northern Sinai, Egypt, were used as the data set for the calibration and validation procedures. The dry samples were scanned using a FieldSpec Pro FR Portable Spectroradiometer (Analytical Spectral Devices, ASD) with a measurement range of 350–2500 nm. The spectra were subjected to seven pre-processed techniques, e.g., Savitzky–Golay (SG) smoothing, first derivative with SG smoothing (FD-SG), second derivative with SG smoothing (SD-SG), continuum removed reflectance (CR), standard normal variate and detrending (SNV-DT), multiplicative scatter correction (MSC) and extended MSC. The results of cross-validation showed that in most cases MARS models performed better than PLSR and SVR models. The best predictions were obtained using MARS calibration methods with CR prep-processing, yielding R2, root mean squared error (RMSE), and ratio of performance to deviation (RPD) values of 0.85, 0.19%, and 2.63, respectively, for OM; and 0.90, 5.32%, and 3.15, respectively, for clay content.

/pdf/estimating-the-soil-clay-content-and-organic-matter-by-means-1lm4dtdd0x.pdf

Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy

The correlation between the brightness temperature of snow-covered terrain (dry snow) and the snow-water equivalent in Finland was investigated using Nimbus-7 SMMR data. The satellite data set covers the winters of 1978-9 through 1981-2. The correlation analysis was performed for 17 different brightness temperature functions, each involving one or several frequencies and polarizations. The highest correlation coefficients between the satellite-derived brightness temperature functions and the manually measured snow-water equivalent values were obtained by using the brightness temperature difference between 37 GHz and either 18 GHz or 10.7 GHz, vertical polarization. >

Comparison of algorithms for retrieval of snow water equivalent from Nimbus-7 SMMR data in Finland

A freeze indicator (FI), based on a low 37-GHz radiobrightness and a low 10.7, 18, and 37-GHz radiobrightness spectral gradient, has been used to classify frozen surfaces in the northern Great Plains. By modeling the radiometer beampatterns as Gaussian, freeze/thaw boundaries can be located at the (fine) resolution of the 37-GHz channel. The performance of the freeze indicator, and subsequent boundary location estimate, depends on the accuracy of the boundary decision criteria. It is shown that decision criteria based on clustering and unsupervised classification yield good performance. A simple algorithm for registering coarse-resolution FI boundaries to equivalent boundaries in fine-resolution 37-GHz radiobrightness images is also presented. >

Radiobrightness decision criteria for freeze/thaw boundaries

Utilizing data from three winter seasons, snow cover distributions in the relatively flat terrain of the Missouri and Upper Mississippi River basins are mapped from satellite photography. Techniques are developed for identifying snow cover and for differentiating between snow and cloud. Comparisons between snow data observed by satellites and snow data conventionally acquired indicate that, in almost all cases, snow can be distinguished from cloud on the bases of current cloud reports, pattern continuity, landmark identification, and texture. Snow cover can be mapped from Tiros or Essa resolution photography with an accuracy of approximately ±20 miles, often providing a more detailed mapping than can be obtained from conventional station networks. Qualitative estimates of snow depth can also be obtained from satellite photography. In nonforested areas, reflectivity increases with snow depth, until an accumulation of about 4 to 6 inches is reached ; after that, the reflectivity remains unchanged. In forested areas, no discernible relationship between reflectivity and snow depth is observed.

Snow cover distribution as mapped from satellite photography

Determination of snowpack properties from satellite passive microwave measurements

A comparative analysis of satellite visible channel imagery and ground based aerosol measurements is carried out for three cases representing a significant pollution episodes based on low surface visibility and high sulfate levels. The feasibility of detecting pollution episodes from space is also investigated using a simulation model. The model results are compared to quantitative information derived from digitized satellite data. The results show that when levels are or = 30 micrograms/cu, a haze pattern that correlates closely with the area of reported low surface visibilities and high micrograms sulfate levels can be detected in satellite visible channel imagery. The model simulation demonstrates the potential of the satellite to monitor the magnitude and areal extent of pollution episodes. Quantitative information on total aerosol amount derived from the satellite digitized data using the atmospheric radiative transfer model agrees well with the results obtained from the ground based measurements.

Evaluation of the capabilities of satellite imagery for monitoring regional air pollution episodes

In much of the western United States a large part of the utilized water comes from accumulated mountain snowpacks; thus, accurate measurements of snow distributions are required for input to streamflow prediction models The application of ERTS imagery for mapping snow has been evaluated for two geographic areas, the Salt-Verde Watershed in central Arizona and the southern Sierra Nevada in California Techniques have been developed to identify snow and to differentiate between snow and cloud The snow extent for these two drainage areas has been mapped from the MSS-5 (06-07 micron) imagery and compared with aerial survey snow charts, aircraft photography, and ground-based snow measurements

Mapping snow extent in the Salt-Verde Watershed and the southern Sierra Nevada using ERTS imagery

Because of the effect of sea ice on the heat balance of the Arctic and because of the expanding economic interest in arctic oil and other minerals, extensive monitoring and further study of sea ice is required. The application of ERTS data for mapping ice is evaluated for several arctic areas, including the Bering Sea, the eastern Beaufort Sea, parts of the Canadian Archipelago, and the Greenland Sea. Interpretive techniques are discussed, and the scales and types of ice features that can be detected are described. For the Bering Sea, a sample of ERTS imagery is compared with visual ice reports and aerial photography from the NASA CV-990 aircraft.

James C. Barnes

Papers

Snow cover distribution as mapped from satellite photography

Determination of snowpack properties from satellite passive microwave measurements

Evaluation of the capabilities of satellite imagery for monitoring regional air pollution episodes

Mapping snow extent in the Salt-Verde Watershed and the southern Sierra Nevada using ERTS imagery

Monitoring Arctic Sea ice using ERTS imagery