FIREX mission requirements document for renewable resources

The initial experimental program and mission requirements for a satellite synthetic aperture radar (SAR) system FIREX (Free-Flying Imaging Radar Experiment) for renewable resources is described. The spacecraft SAR is a C-band and L-band VV polarized system operating at two angles of incidence which is designated as a research instrument for crop identification, crop canopy condition assessments, soil moisture condition estimation, forestry type and condition assessments, snow water equivalent and snow wetness assessments, wetland and coastal land type identification and mapping, flood extent mapping, and assessment of drainage characteristics of watersheds for water resources applications. Specific mission design issues such as the preferred incidence angles for vegetation canopy measurements and the utility of a dual frequency (L and C-band) or dual polarization system as compared to the baseline system are addressed

Active microwave users working group program planning

A detailed programmatic and technical development plan for active microwave technology was examined in each of four user activities: (1) vegetation; (2) water resources and geologic applications, and (4) oceanographic applications. Major application areas were identified, and the impact of each application area in terms of social and economic gains were evaluated. The present state of knowledge of the applicability of active microwave remote sensing to each application area was summarized and its role relative to other remote sensing devices was examined. The analysis and data acquisition techniques needed to resolve the effects of interference factors were reviewed to establish an operational capability in each application area. Flow charts of accomplished and required activities in each application area that lead to operational capability were structured

Estimating Precipitation from WSR-88D Observations and Rain Gauge Data: Potential for Drought Monitoring

Since its deployment, the precipitation estimates from the network of National Weather Service (NWS) Weather Surveillance Radars-1988 Doppler (WSR-88D) have become widely used. These precipitation estimates are used for the flash flood warning program at NWS Weather Forecast Offices (WFOs) and the hydrologic program at NWS River Forecast Centers (RFCs), and they also show potential as an input data set for drought monitoring. However, radar-based precipitation estimates can contain considerable error because of radar limitations such as range degradation and radar beam blockage or false precipitation estimates from anomalous propagation (AP) of the radar beam itself. Because of these errors, for operational applications, the RFCs adjust the WSR-88D precipitation estimates using a multisensor approach. The primary goal of this approach is to reduce both areal-mean and local bias errors in radar-derived precipitation by using rain gauge data so that the final estimate of rainfall is better than an estimate from a single sensor. This chapter briefly discusses the past efforts for estimating mean areal precipitation (MAP). Although there are currently several radar and rain gauge estimation techniques, such as Process 3, Mountain Mapper, and Daily Quality Control (QC), this chapter will emphasize the Multisensor Precipitation Estimator (MPE) Precipitation Processing System (PPS). The challenges faced by the Hydrometeorological Analysis and Support (HAS) forecasters at RFCs to quality control all sources of precipitation data in the MPE program, including the WSR-88D estimates, will be discussed. The HAS forecaster must determine in real time if a particular radar is correctly estimating, overestimating, or underestimating precipitation and make adjustments within the MPE program so the proper amount of precipitation is determined. In this chapter, we discuss procedures used by the HAS forecasters to improve initial best estimates of precipitation using 24 h rain gauge data, achieving correlation coefficients greater than 0.85. Finally, since several organizations are now using the output of MPE for deriving short- and long-term Standardized Precipitation Indices (SPIs), this chapter will discuss how spatially distributed estimates of precipitation can be used for drought monitoring

Measuring Snow Liquid Water Content with Low-Cost GPS Receivers

The amount of liquid water in snow characterizes the wetness of a snowpack. Its temporal evolution plays an important role for wet-snow avalanche prediction, as well as the onset of meltwater release and water availability estimations within a river basin. However, it is still a challenge and a not yet satisfyingly solved issue to measure the liquid water content (LWC) in snow with conventional in situ and remote sensing techniques. We propose a new approach based on the attenuation of microwave radiation in the L-band emitted by the satellites of the Global Positioning System (GPS). For this purpose, we performed a continuous low-cost GPS measurement experiment at the Weissfluhjoch test site in Switzerland, during the snow melt period in 2013. As a measure of signal strength, we analyzed the carrier-to-noise power density ratio (C/N-0) and developed a procedure to normalize these data. The bulk volumetric LWC was determined based on assumptions for attenuation, reflection and refraction of radiation in wet snow. The onset of melt, as well as daily melt-freeze cycles were clearly detected. The temporal evolution of the LWC was closely related to the meteorological and snow-hydrological data. Due to its non-destructive setup, its cost-efficiency and global availability, this approach has the potential to be implemented in distributed sensor networks for avalanche prediction or basin-wide melt onset measurements

Remote sensing of snow-cover for the boreal forest zone using microwave radar

This doctoral dissertation describes the development of an operationally feasible snow monitoring methodology utilizing spaceborne synthetic aperture radar (SAR) imagery, intended for hydrological applications on the boreal forest zone. The snow-covered area (SCA) estimation methodology developed is characterized using extensive satellite-based datasets, including SAR-based estimation and optical reference data gathered during the snow-melt seasons of 1997-1998, 2000-2002 and 2004-2006 from northern Finland. The methodology applies satellite-based C-band SAR data for snow monitoring during the spring snow-melt season. The SCA information can be utilized for river discharge forecasting and flood predictions and for the optimization of hydropower production. The development efforts included 1) demonstration of a forest compensation algorithm, 2) establishing the use of wide-swath SAR data 3) development of a weather station assimilation procedure and 4) creation of an enhanced reference image selection algorithm for the SCA estimation methodology. The feasibility of a proposed, non-boreal forest specific, SAR-based SCA estimation method was evaluated for the boreal forest zone. The acquired results were compared with the characteristics determined for the boreal-forest specific methodology developed within this dissertation. These results can be used when selecting appropriate SCA estimation approaches for future snow monitoring systems whether conducted in different regions or intended for larger i.e. continental or global scale purposes. An automatic processing system for SCA estimation was developed and demonstrated as part of this work; the system has been delivered to the Finnish Environment Institute for operational use