# Editorial for Special Issue \"Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications\"
## 2 Overview of Contributions  
This special issue has three papers related to mapping theories and applications in remote sensing water cycle. [PERSON] et al. [1] evaluated the application of SAR Backscatter and InSAR coherence to monitor the wetland extent across Amazon lowland. In their research, the authors showed that wetland vegetation types were successfully classified with 86% accuracy, using the statistical parameters derived from the multi-temporal intensity and coherence RADARSAT-2 data stacks. [PERSON] et al. [2] used eight years of Odin/SMR retrievals to account the Stable Water Isotopologues (SWIs) in the stratosphere layer, and they also analyzed the spatial and seasonal characteristics of the SWIs. [PERSON] et al. [3] monitored the flood inundation in China during typhoon and extreme precipitation events in 2017, using the Cyclone Global Navigation Satellite System (CYCNSS) data. CYCNSS is a newly launched small satellite constellation designed to measure ocean surface wind speed in hurricanes and tropical cyclones.  
The concept of this paper is innovative since it explores the additional capabilities of CYGNSS for applications of land surface hydrology.  
This special issue contains two papers related to precipitation estimates, using remote sensing data. [PERSON] et al. (2016) and [PERSON] et al. (2016) both developed new methods to derive downscaled precipitation estimates over the Tibetan Plateau. Long-term, fine spatial resolution and good-quality precipitation data sets over the Tibetan Plateau are difficult to acquire due to its harsh environment. In their research, [PERSON] et al. (2016) provided a new retrospective framework to downscale precipitation estimates based on the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) data products and ground observations. The methods of this paper show the potential to obtaining retrogressive precipitation estimates, dating back to 1990s; [PERSON] et al. (2016) compared the downscaled TMPA and Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) products, and they also developed methods to derive annual and monthly precipitation estimates.  
Besides, this special issue encompasses three papers related to water vapor estimates using satellite data. [PERSON] et al. (2016) observed the water vapor in the Planetary Boundary layer (PBL) using short-wave infrared observations from Japanese space agency (JAXA)'s Greenhouse Gases Observing SATellite (GOSAT); this is the first satellite single-sensor estimates of bulk PBL water vapor over land and (sunglint) ocean. [PERSON] et al. (2017) developed a new model to acquire the PWV estimates, using GPS observations in tropical areas. [PERSON] et al. (2017) proposed a unique approach to estimate the vapor pressure deficit using the AMSR-E and AMSR2 satellite observations.  
Further, this special issue encloses two papers addressing other hydrological components, i.e., evapotranspiration and snowfall. [PERSON] et al. (2016) advanced the albedo estimates for modeling energy-balance fluxes and evapotranspiration over agricultural and natural areas in Brazilian Cerrado; [PERSON] et al. (2016) utilized the CloudSat-based observation to assess the Global Precipitation Measurement (GPM)'s microwave ability to observe snowfall.  
As for involving remote sensing data to improve hydrological decision support system, [PERSON] et al. (2016) presented a case study about utilizing satellite-based precipitation observations to enhance the decision support system for water resource management in Lowe Mekong River Basin. This study demonstrated impressive applications in using satellite remote sensing data to pursue hydrologic modeling in regions with scarce or inaccessible in-situ precipitation observations.