Radar: In contrast to the radiometer, the SMAP radar operates in a "shared" band between 1215 and 1300 MHz with other services including FAA and DOD aircraft navigation systems. Very strong interference from these systems is expected, and indeed has been observed by previous L-band radar missions. Most potential interfering emissions at L-band are relatively narrow band. Because the SMAP radar itself is a narrow band system (1 MHz linear chirp), a key RFI avoidance strategy for SMAP is to make the center transmit frequency adjustable. If persistent RFI is encountered in a given band over a given region, the center frequency is simply commanded to a different location in the spectrum. Despite best efforts to operate the SMAP radar in a "clear" band, however, it is inevitable that some RFI contamination will be observed. Therefore, RFI detection and removal will be performed as part of the ground data processing.
As with the radiometer, an extensive effort has been made to characterize the RFI environment expected for the radar. An examination of currently operating L-band systems indicates that 87% of RFI is from "pulsed" sources, and 13% is from "other" sources such as CW emitters. A simulation, developed to model RFI and the effectiveness of mitigation algorithms, uses the database of known emitters over North America. RFI emitter characteristics are varied in different runs of the simulation to assess sensitivities. To validate the RFI simulation, it was run assuming the Aquarius instrument parameters and orbit. When compared against the actual Aquarius data, the agreement was found to be excellent (Figure 62). In order to estimate the impact of other regions of the world where the RFI is observed to be somewhat worse (e.g., Europe and East Asia), the simulation can be run with an increased number of emitters, with higher duty cycles, higher powers, etc.
Figure 62: Aquarius noise only data vs. Simulation, multiple ascending nodes (image credit: NASA)
Legend to Figure 62: Complementary cumulative distribution functions of real and simulated Aquarius radar noise-only data for sets of passes over different geographical regions. Power levels above the radar noise floor are due to RFI.
The primary detection/correction algorithm for pulsed interferers is STT (Slow-Time Thresholding). Here, SMAP leverages the fact that RFI signal pulse rates are usually slower than SMAP's radar pulse rate. The STT technique looks at the slow-time series associated with a given range bin, sets an appropriate threshold, and flags any azimuth samples that exceed this threshold as RFI events. When the STT is applied to the results of the North American simulation described above, it is observed that the overall measurement errors due to RFI will be well within the budgeted allocation of 0.4 dB RMS. Further, this mitigation technique appears robust to artificially intensifying the RFI environment in the simulation to approximate regions other than North America (Ref. 97). 99) 100)
The unique aspect of the SMAP application is the necessity for rotating the large antenna. At the nominal SMAP altitude of 685 km, the reflector must be rotated at a rate of 13-14.6 rpm to maintain contiguity (i.e., minimum overlap, SMAP is designed for 14.6 rpm) of the measurements in the along-track direction. Key requirements that must be met by the reflector assembly include:
1) All RF performance requirements (gain, beam efficiency, etc.) must be met under the spinning conditions
2) The total momentum generated must be within the amount the spacecraft is capable of compensating
3) The disturbances resulting from residual imbalances must be sufficiently small as to not affect overall pointing or impart excessive loads to the spin motor bearings.
The deployable antenna is attached to the structure of the SPA (Spun Platform Assembly). The SPA includes the primary and secondary structures. The former provides the backbone of the SMAP instrument, while the radiometer electronics and IFA (Integrated Feed Assembly) are supported by the secondary structure. The feed assembly design employs a single horn, capable of dual-polarization and dual frequency (the radiometer frequency at 1.41 GHz, and the radar frequencies between 1.22 and 1.30 GHz). In order to minimize the moment of inertia, the horn is designed to be as small, compact, and lightweight as possible. To meet measurement performance requirements, the antenna beamwidth of the radiometer frequency band is between 2.3º to 2.5º, and the antenna gain at the radar band is better than 35.5 dBi.
Figure 63: Spun platform assembly and electronics on the spun portion of the SMAP instrumentation (image credit: NASA)
Table 9: SMAP instrument parameters
Figure 64: SMAP measurement geometry showing radiometer swath, and high- and low-resolution radar swaths (image credit: NASA)
Radiometer parameters: Measurement precision for a radiometer is principally affected by NEDT. NEDT (0.65 K) is set by the system noise temperature (777 K), bandwidth (22 MHz), and net integration time (65 ms for a 30 km x 30 km grid cell, including both fore and aft looking radiometer samples). Many other factors contribute to the measurement imprecision, including antenna pattern instability. Altogether, measurement precision is 0.95 K. The radiometer calibration stability is 0.62 K. Calibration stability is achieved by frequent observation of internal calibration sources, observation of stable earth and space targets, and stable thermal design. The root-sum-square of the 0.62 K stability and the 0.95 K precision estimates yields a total error of 1.1 K, satisfying the 1.3 K requirement of the soil moisture science objective. 101) 102)
Initially the radiometer was designed with passive thermal control, and analysis demonstrated that the required thermal stability could be met. Achieving anomaly free performance over a large thermal range, however, carries a development risk due to potential for thermo-mechanically induced calibration shifts associated with internally calibrated radiometers; these have been observed in the prelaunch testing of SAC-D/Aquarius, Jason-2/AMR, and Juno/MWR. It was therefore decided to implement an active thermal control augmentation to the existing passive thermal design. The temperature of the RAD front end (Figure 63) is settable to within ±2ºC over a 15ºC range within the acceptable flight temperature range.
Radar parameters: To obtain the required 3 km and 10 km resolution for the freeze/thaw and soil moisture products, the radar will employ pulse compression in range and Doppler discrimination in azimuth to sub-divide the antenna footprint. This is equivalent to the application of SAR (Synthetic Aperture Radar) techniques to the conically scanning radar case.
Due to squint angle effects, the high-resolution products will be somewhat degraded within the 300 km band of the swath centered on the nadir track (Figure 64 and Figure 65), with azimuth resolution capability decreasing over this region as the target area approaches the nadir track. The baseline system has both V- and H-pol channels. An additional channel measures the HV (or VH)cross-pol return.
Figure 65: Radar measurement geometry as a function of scan angle (image credit: NASA)
Legend to Figure 65: The spacecraft velocity vector is shown as vg. Also shown are the iso-range and iso-Doppler contours that govern the radar pixel formation.
There are two requirements placed on the radar relative error. The soil moisture measurement requirement places a 0.5 dB relative error on both the vertical and horizontal copolarized backscattering coefficient measurements at 10 km resolution. The freeze/thaw state measurement places a 1 dB requirement on the relative error of each vertical and horizontal co-polarized backscatter measurement at 3 km resolution.
The radar relative error depends on the signal-to-noise ratio (SNR) and the number of independent samples, or "looks", averaged in each measurement, as well as the relative calibration error. Looks will be obtained by averaging in both range and azimuth. The 1 MHz bandwidth will yield a ground range resolution of approximately 250 m and will result in a minimum of 12 looks in range for 3 km cells and 40 looks for 10 km cells.
The Doppler diversity (Figure 65) will be maximized at a scan angle perpendicular to the platform velocity, leading to a single-look azimuth resolution of approximately 400 m. The single-look resolution will degrade as the scan angle approaches the platform velocity vector (θaz = 0 in Figure 65), reaching 1500 m at the inner swath edge (150 km cross-track).
To ease volume and mass issues encountered in the spacecraft design, a decision was made to generate both linearly polarized transmit signals sequentially by switching a single high-power amplifier. The current sequential transmit scheme is shown in Figure 66. In this timing configuration, the duration of the total transmit event has been extended, and the nominal PRF (Pulse Repetition Frequency) is lowered to approximately 2.9 kHz in order to accommodate the temporal width of both echo returns. As was the case in the previous design, the two polarized returns are isolated by transmitting signals at offset frequencies (f1 and f2) as shown in Figure 67. This scheme maintains the same science performance.
Figure 66: Instrument timing in sequential transmit configuration (image credit: NASA)
Figure 67: Signals from V- and H-pol channels are isolated in frequency domain (image credit: NASA)
The active radar will utilize onboard SAR processing in order to obtain the sub-footprint resolution necessary for the geophysical retrievals. The OBP (Onboard Processor) software package receives the capability to turn raw data into low-resolution and high-resolution data ready to be downlinked for further ground processing into low-resolution and high-resolution radar products. 103)
Figure 68: Swath size of radar and radiometer instruments on SMAP (image credit: Northrop Grumman/Astro Aerospace)
The SMAP RDE (Radiometer Digital Electronics):
The SMAP radiometer has an entirely digital back-end processor for its DSP (Digital Signal Processing) and RFI (Radio Frequency Interference) mitigation. The mission of SMAP is to provide global high-resolution mapping of soil moisture and its freeze/thaw state to link terrestrial water, energy, and carbon cycle processes. Fusion of SMAP's L-band SAR data and L-band microwave radiometer data will enable this mission. In addition, both of SMAP instruments include new technology that enables mitigation of RFI via ground processing. In particular, the SMAP radiometer employs the first spaceborne digital back-end processor subsystem called the RDE (Radiometer Digital Electronics). The RDE is a FPGA-based digital signal processing system that uses time, frequency, statistical and polarization diversity to detect and flag RFI during its mission lifetime 104)
The SMAP radiometer can be thought of as three major subsystems: the RFE (Radiometer Front End ), RBE (Radiometer Back End), and RDE (Radiometer Digital Electronics). 105)
The RFE includes a calibrated noise source and interfaces to a 6 m conical-scanning deployable mesh antenna that is shared with the radar. The RFE and RBE subsystems together form a polarimetric heterodyne radiometer that downconverts predetected horizontal and vertical polarization component signals to an IF (Intermediate Frequency) of 120 MHz using highside local oscillator injection at 1533.5 MHz. Both channels are subsequently digitized and processed by the RDE at 96 MHz with 14 bit resolution.
Figure 69: Exploded view of the RDE assembly (image credit: NASA/GSFC)
In addition, the RDE interfaces with the spacecraft for commanding/telemetry and science data pipeline, provides the instrument power conditioning and distribution, command/control, housekeeping and overall timing and synchronization. The RDE consists of five CCAs (Circuit Card Assemblies), an enclosure, a baseplate, a connector panel and cover shown in Figure 3. Two identical CCAs, APU-H (Analog Processing Unit-Horizontal) and APU-V, perform quadrature down-conversion, subbanding and generation of the first 4 raw sample moments for fullband and 16 adjacent frequency channels of subband data for each polarization.
The DPU (Data Processing Unit) orchestrates control of the radiometer, processing, timing and packetization of data within the RDE. The DPU also digitizes analog telemetry from the RFE, RBE, and platinum resistance thermometers throughout the radiometer. Furthermore, the DPU correlates fullband and subband signals, producing complex-valued counts for generating the third and fourth Stokes parameters (C3 and C4).
The PDU (Power Distribution Unit) accepts primary power from the spacecraft in the range of 22 to 36 volts. The PDU produces the secondary voltages for the RDE as well as voltages for powering the RBE and RFE subsystems. The last CCA is the high-speed digital backplane that connects the two APU cards to the DPU (Ref. 105).
SMAP instrument antenna control:
SMAP's attitude control and pointing system performs the key on-orbit operations needed to implement the conical scanning scheme employed for data acquisition by the radar and radiometer. The observatory uses a zero momentum bias, dual-spin architecture to rotate its large antenna at a spin rate of 13–14.5 rpm, while the spacecraft bus provides a three-axis controlled platform that maintains both itself and the instrument section's spin axis in a nadir-pointed orientation. The major pointing and control functional aspects and design characteristics are illustrated in Figure 70. A key challenge with the spinning antenna stems from its large spin axis moment of inertia, which at almost 240 kgm2 is larger than that of the spacecraft bus at about 190 kgm2(Ref. 97).
Figure 70: SMAP pointing and control system functional aspects and design characteristics (image credit: NASA)
Figure 71 shows the sensor and actuator suite and locations. This control system configuration was informed by a prior design concept for the NROSS (Navy Remote Ocean Sensing System) satellite, which would have employed a similar sized-rotating antenna and nadir-pointing scheme (NROSS development was halted after Preliminary Design Review in the 1980s). From a control standpoint, SMAP took an integrated approach to momentum management, with momentum compensation for the spun side and three-axis control accomplished with a single set of four RWAs (Reaction Wheel Assemblies), rather than using a dedicated momentum wheel for spun-side momentum compensation. In a recent operational example of a nadir-pointing observatory employing a momentum-compensated rotating antenna, WindSat/Coriolis, a dedicated momentum wheel was also used to counteract the antenna's angular momentum, in a manner similar to that planned for NROSS. SMAP's approach provides a degree of functional redundancy for wheel failure and reduces control complexity.
Figure 71: Observatory sensor and actuator description (image credit: NASA)
The system architecture is illustrated in Figure 72, encompassing the attitude and spin rate determination functions, attitude control modes for both RWA and RCS (Reaction Control System) based three-axis control, spun-side momentum compensation, and the torque rod-based scheme employed for RWA momentum management. For translational maneuvers (needed for orbit altitude maintenance) and select contingency scenarios, the RCS is used due to the much larger control authority offered by the thrusters. For nominal mapping operations, this system can control nadir pointing errors due to precession and nutation to within 0.5º, with a stability tolerance of ±0.3º (3σ).
From a design and verification standpoint, the key challenge for pointing and control is accommodating the flexible modes, especially for the large antenna and its supporting boom, while simultaneously controlling the antenna spin rate and nadir orientation to within the required tolerances. This has been accomplished via careful engineering of the primary frequencies associated with these various elements, to ensure adequate separation and avoid the potential for interference or undesired resonance effects. Frequency distribution of these system components is shown in Figure 73 to illustrate this aspect of the design. The minimum 1st flexible mode frequencies of the solar array, as well as the antenna and boom, became key design requirements on the structure to ensure adequate separation from the spin control system and the observatory's attitude control bandwidth.
Figure 72: Pointing and control system architecture (image credit: NASA)
GDS (Ground Data System):
The ground data system being developed for SMAP is composed of many heterogeneous subsystems, ranging from those that support planning and sequencing to those used for real-time operations, and even further to those that enable science data exchange. The SMAP mission shares in common many characteristics as those of Jason-1 and WISE. One of the mission objectives is to achieve similar cost savings as done by Jason-1 and WISE, through automation of real-time operations. However, the SMAP mission has selected a different GDS subsystem element in place of the one that provides the automation services for Jason-1 and WISE [Note: The GDS subsystem that provides automation of engineering operations for Jason-1 and WISE missions is the JPL ESMC (Earth Science Mission Center)]. The SMAP GDS will use NASA's AMPCS [AMMOS (Advanced Multi-Mission Operations System) Mission Data Processing and Control System] to provide telemetry processing, storage, reporting, display, and a subset of automation capabilities for real-time operations. 106)
Figure 74 shows the four main facilities that support the SMAP mission and the functions executed at each facility. Figure 75 illustrates the SMAP communications paths. Operations are centered at the MOC (Mission Operations Center) of NASA/JPL. Communications with the observatory are handled through the ground and space assets of the NEN (Near-Earth Network) and SN (Space Network). Scheduling and pass reporting for the NEN and SN assets are handled through the DSMC (Data Services Management Center) at the WSC (White Sands Complex), where the primary TDRS (Tracking and Data Relay Satellite) ground terminals are located (Ref. 19).
Science telemetry from the NEN stations flows to the EOS EDOS (Data and Operations System) LZPF (Level Zero Processing Facility) at NASA/GSFC, which formats the data into files and passes the radar and radiometer data to the SDS (Science Data System). Engineering data from the NEN and SN stations flows to the MOC at JPL, which generates displays and other products to support both mission operations and science processing.
The primary path for commanding the observatory and returning science and engineering data is through three northern-hemisphere tracking stations and one southern-hemisphere station in Antarctica. Data return at the northern-hemisphere stations is via 11.3 m antennas located at WGS (Wallops Ground Station), Virginia, ASF (Alaska SAR Facility) at Fairbanks, Alaska, and SGS (Svalbard Ground Station), Norway. Data return at the southern-hemisphere station is via the 10 m antenna at MGS (McMurdo Ground Station), Antarctica. Table 10 gives the characteristics of the four stations and average contact statistics from the science orbit. Since SMAP is in a near-polar orbit, the higher latitude stations have more frequent contact opportunities.
Table 10: Ground station characteristics (X-band)
Figure 74: Ground data system facilities and functions (image credit: NASA)
Figure 75: SMAP communications paths (image credit: NASA)
Mission operators at JPL control the observatory and coordinate the needed infrastructure on the ground that is required to communicate with observatory and return the instrument data for processing. The observatory requires updated ‘instructions' daily so that it is kept current on how to make its measurements and when various ground stations are scheduled to receive its data. Operators gather updates and requests from scientists, ground stations, and other stakeholders to prepare these instructions in a format that can be understood by the observatory. Operators also monitor the health and well being of the observatory and ensure that all of its systems are operating properly. Occasionally, the observatory encounters problems that may interrupt its measurements – operators must then diagnose the problem and prepare additional instructions for the observatory to correct it. 107)
Figure 76: SMAP mission operations flow diagram (image credit: NASA/JPL)
Mission operators communicate with the observatory and receive instrument data through NASA's NEN (Near Earth Network). SMAP produces an average volume of 135 GB/day of science data – this is like downloading more than 30 DVD full-length feature movies every day!
Because the observatory makes a complete orbit over the Earth every 98 minutes, it is only within range of individual ground stations (located at Wallops, Virginia, Fairbanks, Alaska, Svalbard Island, Norway, and at McMurdo Station, Antarctica) for a few minutes at a time. Scientists are anxious to have SMAP's data products as quickly as possible after the instrument takes the data. For these reasons, SMAP typically has one or two downlink sessions per orbit and because of the brief time during which the observatory is in range as it passes overhead, each downlink session can receive between 5-15 GB of data. It is up to the mission operators to manage the limited on-board memory usage by balancing the rate at which it fills with instrument and other data against the rate at which it is emptied during downlink sessions with ground stations.
All SMAP data products are sent to NASA Data Centers where they are available for everyone – scientists, applications users, and the public. The Data Centers also provide a long term archive for all SMAP data. After it is released, SMAP data are delivered to two archives:
- Radar data: Alaska Satellite Facility Data Center
- Other SMAP data: National Snow & Ice Data Center.
Figure 77: SMAP science data system archives (image credit: NASA/JPL)
SDS (Science Data System):
The science data system provides the hardware and software to process the radar and radiometer instrument data and the supporting engineering data into science data products for the science team, applications users, and the public. Files of radar and radiometer data derived from the downlink telemetry are delivered from the GSFC EDOS/LZPF, and engineering ancillary data, including timing, pointing, and ephemeris information, are delivered from the MOC. Algorithms developed before launch are used to process the data into science data products.
The SDS has data latency requirements on delivering data products to the science team and to operational users: Level 1 products (within 12 hours of acquisition), Level 2 (within 24 hours), Level 3 (within 50 hours), and Level 4 (within 7 days for soil moisture and 14 days for carbon net ecosystem exchange). Data latency is defined here as from the time of data acquisition by the observatory to the time data products are available to the public at the NASA DAACs. SMAP data will be archived by the NASA-designated Earth science data centers at the National Snow and Ice Data Center and the Alaska Satellite Facility. During the first year of routine science collection (which comprises the formal mission cal/val period), all SMAP data product algorithms are updated as needed by comparing SMAP estimates of soil moisture and freeze/thaw state with data collected on the ground at specific cal/val sites.
The key SDS operations functions are:
• Ingest instrument and ancillary data and generate higher-level data products. The range of higher-level products is Level 1A (L1A) through Level 4 (L4). The definition of what the SDS produces is dictated by the science requirements.
• Support calibration and validation of science data products
• Provide science data accounting/auditing
• Provide data access to Project, Science, and Flight Engineering Teams
• Manage long-term data storage (products, metadata, test data, etc.); prepare and make available validated products to a public archive data center
• Maintain the SDS production and testbed systems.
The SDS implementation organizations include:
• SMAP Science—responsible for the L1 radiometer and L2-L4 algorithms and science software
• JPL SDS—responsible for L1 radar algorithms and L1-L3 production code and product generation
• GMAO (GSFC Global Modeling & Assimilation Office) — responsible for Level 4 soil moisture and Level 4 carbon production code and product generation.
The SDS architecture and data flow among different entities are summarized in Figure 78.
Figure 78: SMAP Science Data System architecture and data flows (image credit: NASA)
Figure 79: Functional block diagram of the SMAP GDS architecture (image credit: NASA)
The SMAP GDS consists of numerous subsystems that provide specific and necessary services. Although many GDS functional elements will automate routine operations activities, the AOE (Automated Operations Element) will not directly control all of them. 108)
The SMAP GDS team is applying the following strategy to achieve reliable lights-out operations of the SMAP mission:
• Leveraging of core JPL multi-mission GDS capabilities
• Leveraging of architecture from prior JPL Earth Science missions
• Application of test-as-you-fly principles throughout the GDS lifecycle
• Automation ‘hooks' for manual operations processes
• GDS system monitoring.
The SMAP GDS team has identified the following automation patterns that are sufficient to address the SMAP automated operations use cases:
• Pass Driven Automation
- Utilizes a Pass Automation Daemon to provide automation of unattended tracking passes.
• File Driven Automation
- Utilizes a File Notification Service to provides file detection, notification, and workflow automation for files deposited into the SMAP mission file system.
• Time Driven Automation
- Utilizes a generic Automation Scheduler to provide workflow automation driven by user-specified schedules.
Figure 80: SMAP GDS automation architecture (image credit: NASA/JPL)
NowCast: Product development:
The SMAP Applications program is designed to first increase and then sustain the interaction between application users and scientists involved in mission development. The SMAP project has sponsored several applications meetings and workshops. To better reach the applications users, some of these have been held at user locations such as the USDA (U.S. Department of Agriculture), USGS (U.S. Geological Survey), and NOAA headquarters, among others. Feedback from user communities is formally and actively reported to mission scientists to broaden and facilitate eventual SMAP data access and enhance opportunities to use mission data to address societal needs. For example, collaboration between the SMAP mission and the USDA's FAS (Foreign Agriculture Service) has elicited the requirements of yield forecasting and familiarized analysts with soil moisture data. Another example pertains to the Emergency Response and Operational users, who have worked with the SMAP mission to plan for providing data in friendly formats (KMZ and GeoTIFF) for a more rapid ingestion of soil moisture data into decision-making environments. 109)
The SMAP Applications program is groundbreaking and serves as an example for other NASA missions to expand their focus to include user communities' needs in the early phases of mission development. Through a team that includes an applications lead on the SDT (Science Definition Team), leadership from the mission, and an applications coordinator, the applications program works to characterize the community of mission data users through workshops and applied research. The project has also initiated a program of Early Adopters to promote application research in the prelaunch stages of the mission, in order to provide a better understanding of how SMAP data products can be scaled and integrated onto organizations' policy, business, and management activities. These efforts will expand the use of the data after launch, and increase the societal benefit of the mission.
Table 11: Anticipated SMAP mission products
Legend to Table 11: * Mean latency under normal operating conditions. Latency is defined as the time from data acquisition by the instrument to its availability in a designated data archive. The SMAP project will make a best effort to reduce these latencies.
The overall strategy for the SMAP applications program is to develop a community of end users and decision makers who are interested in using SMAP products in their applications by providing opportunities to learn about SMAP's unique capabilities and scientific objectives. The SMAP science objectives are to acquire spaceborne hydrosphere state measurements to 1) understand processes that link the terrestrial water, energy, and carbon cycles; 2) estimate global water and energy fluxes at the land surface; 3) quantify net carbon flux in boreal landscapes; 4) enhance weather and climate forecast abilities; and 5) develop improved flood prediction and drought-monitoring capabilities. To meet its scientific goals, SMAP will fly a dedicated satellite in a near-polar, sun synchronous orbit, crossing the equator at 6:00 a.m. and 6:00 p.m. local time. The satellite will carry an L-band (1.26 GHz) radar and an L-band (1.4 GHz) radiometer that share a deployable lightweight mesh parabolic reflector, which provides a conically scanning antenna beam with a constant surface incidence angle of approximately 40º and will measure a swath approximately 1000 km wide. The combined observations from the two sensors will allow accurate estimation of soil moisture and freeze/thaw states at spatial scales valuable for both hydrometeorological (10 km) and hydroclimatological (40 km) studies.
After launch, the satellite's instruments will be calibrated (an expected time period of three months). Once calibrated, the SMAP mission will deliver estimates of soil moisture in the top 5 cm of soil with an accuracy of 0.04 cm3/cm3 volumetric soil moisture, at 10-km resolution, with 3 day average intervals (Table 11). Global maps will also be available of landscape freeze/thaw state derived from L-band radar at 3 km spatial resolution with a 2 day refresh rate for the high northern latitudes (i.e., latitudes above 45ºN). Measurements will be made over the global land area, excluding regions of snow and ice, mountainous topography, open water, and areas of extremely dense vegetation such as tropical forests.
In addition to the instrument measurements and derived products for the surface layer, SMAP will also provide Level 4 data assimilation products by ingesting active and passive observations into land surface models to provide root-zone soil moisture (to a depth of 100 cm). A net ecosystem exchange product will also be developed that integrates freeze/thaw measurements into a carbon model to provide ecosystem exchange at 9 km resolution. As these two products are intended to serve a broad community, there is an opportunity for user engagement now to optimize the design of these products so that they can ultimately satisfy user requirements (Ref. 109).
Background: The SMOS interferometric radiometer of ESA observes L-band passive microwave emission at a range of incidence angles at a resolution of about 40 km (varying with incidence angle) and with a measurement error standard deviation of approximately 4 K. The SMAP mission will make simultaneous active (radar) and passive (radiometer) measurements in the 1.26-1.43 GHz range (L-band). Similar to SMOS, the SMAP radiometer measurements will be at about 40 km resolution. Unlike SMOS, SMAP will observe brightness temperature at a constant incidence angle of 40º and with a design accuracy of 1.3 K. 110)
SMAP and SMOS observations are directly connected to surface soil moisture (in the top 5 cm of the soil column). Several of the key applications targeted by SMAP, however, require knowledge of root zone soil moisture (~top 1 m of the soil column), which is not directly measured by SMAP. The foremost objective of the SMAP L4_SM product is to fill this gap and provide estimates of root zone soil moisture that are informed by and consistent with SMAP observations. Such estimates are obtained by merging SMAP brightness temperature observations with estimates from a land surface model in a soil moisture data assimilation system (Figure 81). The land surface model component of the assimilation system, the NASA Catchment land surface model, is driven with observations-based surface meteorological forcing data, including precipitation, which is the most important driver for soil moisture. The model also encapsulates knowledge of key land surface processes, including the vertical transfer of soil moisture between the surface and root zone reservoirs. A radiative transfer model is added to the land surface model to simulate microwave radiances. The horizontally distributed ensemble Kalman filter ("3d EnKF") update step considers the respective uncertainties of the model estimates and the observations, resulting in a soil moisture and soil temperature analysis at 9 km resolution that is, in theory, superior to satellite or model estimates alone. Moreover, error estimates for the L4_SM product are generated as a by-product of the data assimilation system.
Figure 81: The SMAP L4_SM soil moisture product merges land model estimates of L-band brightness temperature with SMAP observations in a soil moisture and soil temperature analysis. SMOS observations are used to calibrate and validate the SMAP L4_SM algorithm prior to the launch of SMAP (image credit: NASA)
SMOS has been providing multi-angular, global passive microwave observations at L-band since its launch in November 2009. These observations are highly valuable for the science development of the SMAP L4_SM algorithm. In a first step, the multi-angular SMOS observations were used to calibrate the L4_SM microwave radiative transfer model. In a second step, multi-angular SMOS observations were assimilated into the prototype L4_SM algorithm. The results show that the assimilation estimates of surface and root zone soil moisture are superior to estimates derived from the model alone. The assimilation of SMOS observations represents an important step in the prelaunch calibration of the SMAP L4_SM algorithm.
Development of a ground network of sensors for product validation:
NASA is developing a wireless sensor network, control system, and data analysis technologies for dynamic and near-real-time validation of the spaceborne soil moisture measurements of the SWAP mission. Soil moisture fields are functions of variables that change over time across the range of scales from a few meters to several kilometers. Sensor placement rules are being developed based on spatial statistics of soil moisture. For each location, dynamic scheduling policies are being defined based on physical models of soil moisture temporal dynamics and microwave sensor models for heterogeneous landscapes. Furthermore, the ground-based estimates of the true mean to the spaceborne estimates are being related through a physics-based landscape simulator and statistical aggregation procedure.
An energy-efficient integrated communication and actuation platform was developed and used to command the sensors and transmit their data to a base station in near-real time. Full-scale field experiments were initiated in August 2010 with the deployment of the first full-scale wireless sensor network in Canton, Oklahoma, using the custom-built Ripple-1 architecture. 111) 112)
This project introduces a new concept for a smart wireless sensor web technology for optimal measurements of surface-to-depth profiles of soil moisture using in-situ sensors. The objective is to enable a guided and adaptive sampling strategy for the in-situ sensor network to meet the measurement validation objectives of spaceborne soil moisture sensors. The validation of SWAP mission data products is only one potential application of this technology. The project is being carried out at the University of Michigan (Umich) and at MIT (Massachusetts Institute of Technology) through a grant from the NASA/ESTO (Earth Science Technology Office).
Figure 82: Global architecture of the Ripple wireless sensor network system (Umich, MIT)
The wireless sensor network will communicate with a coordinator, and actuate measurements only when their measurement significantly adds value to the across-network computation of the field mean. The principal technology innovations that make this possible are:
• Optimal design of sensor node placement and scheduling based on modeled soil moisture spatial statistics, and joint placement, scheduling and mean estimation
• Strategies for deriving large-scale spaceborne estimates of heterogeneous soil moisture that are compatible with ground-based estimates of true mean of soil moisture fields
• Wireless communication protocols and actuation systems that configure the sampling within the network to yield large-scale field mean conditions.
Upon successful completion of the baseline project in 2012, the TRL (Technology Readiness Level) is expected to be at 6, on-track for integration into an operational scenario for SMAP by the time it launches in the 2015 timeframe.
Ripple-1 network deployment in Canton, Oklahoma:
A conceptual and architectural depiction of the Ripple data collection system is illustrated in the Figure 83. The system consists of a field element and a remote element. A wireless sensor network is deployed over a target field, along with a base station that performs data collection and sensing control, and a database collocated with the base station for local data storage. At each sensing site (where a sensor node is placed), 3 moisture sensors are deployed at different depths with wire connection to the sensor node on the ground. The base station receives sensing data from each sensor node, but can also control the sensor measurement schedules on demand. It also periodically (every half an hour) uploads the collected sensor data through a 3G connection to a database server. The database server and web server constitute the remote element of the architecture.
The Ripple-1 (Figure 83) system is built using the ZigBee (IEEE 802.15.4 plus higher layer specifications) standard. ZigBee allows the formation and self-configuration of a multi-hop network, which can potentially provide the required coverage. In addition, ZigBee is a relatively mature technology with many products on the market to choose from, which can significantly shorten the development and production cycle. - The disadvantage with this choice is that a router node under the ZigBee specification cannot be put to sleep mode, which means it will consume significantly more energy and will require larger batteries and larger solar panels. For the end device, the project selected to use the Xbee Pro SOC as both the MCU Microcontroller Unit) and radio.
Figure 83: Photo of the Ripple-1 end device (image credit: Umich, MIT)
Prelaunch SMAP campaigns:
SMAPVEX12 (SMAP Validation Experiment 2012) Field Campaign and Aircraft Observations
SMAPVEX12 is the primary prelaunch field campaign for SMAP established to provide data for algorithm evaluation and testing and applications development. Several agencies in the U.S. and Canada are cooperating in the SMAPVEX12 data acquisition, processing and analysis.
The SMAP Algorithm Development Teams were asked to provide an assessment of what outstanding issues could be addressed with a field campaign. All of the soil moisture algorithms had two common requirements for a field campaign; an extended time series and diverse vegetation conditions. Data sets that supported the combined active passive algorithm were considered the top priority, which necessitated an aircraft instrument suite that could provide data to simulate the SMAP sensor system. This evaluation also indicated that it was critical that any campaign be conducted as soon as possible in order for the algorithm teams to effectively utilize the results.
In response to the issues identified, a field campaign SMAP Validation Experiment 2012 (SMAPVEX12) was designed and executed. The primary objectives were as follows (Ref. 117):
• Collect an extended times series of concurrent active and passive microwave observations
- Capture a wide range of soil moisture conditions
- Observe a wide range of vegetation conditions including variable types and growth stages
- Multiple resolution observations for scaling.
• Find ways to better mitigate low-level RFI effects observed in North America
• Improve the parametrization of vegetation (and its water content) and soil roughness
• Contribute to establishing an in situ Cal/Val site for SMAP post-launch validation.
The ground and airborne data acquisition phase of SMAPVEX12 took place over a period of approximately six weeks from June 6 to July 19, 2012 in an agricultural region south of Winnipeg, Manitoba (Canada). The campaign was organized jointly with the Canadian SMAP Science Team who were responsible for coordinating the site logistics and ground data sampling.
The site in the Carmen-Elm Creek area of Manitoba was chosen due to the existing soil moisture monitoring stations that have been installed on private farms in the region over the past growing season, and is expected to serve as a long-term site for assessing the satellite data post-launch. During this campaign, NASA conducted flights carrying instruments similar to the SMAP satellite several times per week over the selected study area covering forested and agricultural land. The study area had a size of of ~13 km x 70 km. 113) 114) 115)
SMAPVEX12 was funded by several agencies in Canada (CSA, AAFC, EC, and NSERC) and US (NASA). It complements the Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) by providing extensive data sets for the development and validation of SMAP passive and active soil moisture retrieval algorithms. 116)
Figure 84: The SMAPVEX12 intensive sample site (image credit: NASA/JPL, CSA)
Aircraft observations: 117)
Two aircraft-based instruments were deployed for SMAPVEX12 on different aircraft; the PALS (Passive/Active L-band Sensor) and the UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar).
• PALS provides both radiometer (vertically and horizontally polarized brightness temperatures) and radar products (normalized radar backscatter cross-section for V-transmit/V-receive, V-transmit/H-receive, H-transmit/H-receive, and H-transmit/V-receive). In addition, it can also provide the polarimetric third Stokes parameter measurement for the radiometer and the complex correlation between any two of the polarized radar echoes (VV, HH, HV and VH).
PALS was mounted at a 40 º incidence angle looking to the rear of the aircraft, a Twin Otter. The lowest elevation that PALS can operate at is determined by the minimum distance for radar data acquisition (~1000 m), which results in a spatial resolution of ~350 m cross-track and 650 m along-track for the low altitude flights. The highest flight altitude for SMAPVEX12 was determined by the operational and available maximum altitude of ~2500 m, which corresponds to a spatial resolution of 900 m cross-track and 1500 m along-track.
The PALS flightlines were designed to satisfy the major objectives of SMAPVEX. Four low altitude lines were used to provide high spatial resolution data for fields with homogeneous vegetation conditions. An attempt was made to locate many of the sampled fields directly on these lines. High altitude lines mapped a larger region and provide data for simulating SMAP combined algorithms. A total of eight lines covered a domain ~12.8 by 70 km. Lines were spaced ~ 1.6 km apart.
• UAVSAR is an aircraft based fully polarimetric L-band radar that is also capable of interferometry. It is currently implemented on a NASA Gulfstream-III aircraft. For SMAPVEX12, the nominal flight altitude was 13 km. UAVSAR looks to the left of flight direction and collects data over a swath between 25 and 65º, which is a nominal swath of 21 km. The most relevant portion of the data swath for SMAP, which has an incidence angle of 40º, are data collected between ~35 and 45º, which is a narrower swath of ~3.8 km. In order to provide coverage of the study domain, four overlapping flight lines were used. Spatial resolution can be as high as 3 m.
Outcome and outlook:
Both the weather conditions and instrument performance exceeded expectations resulting in SMAPVEX12 being very successful. A total of seventeen days of flights were conducted with supporting ground observations over 42 days (June 7-July 19, 2012). This simulated the temporal frequency expected from SMAP at mid-latitudes.
Following the first flight, which had moderate soil moisture levels, there was a series of rain events that resulted in very wet conditions for several days. This wet period was followed by an extended drying of the soils that lasted over two weeks. The remainder of the campaign had mixed conditions. - Many of the crops were near bare soil conditions at the outset of SMAPVEX12 and reached peak biomass/vegetation water content by its end. Winter wheat reached its peak biomass early in the campaign and then entered senescence.
Both instruments/aircraft were able to collect data on every flight day; however,, there were a few dates that only one of the two aircraft flew.. Very little data was lost due to RFI or instrument failure. Based upon the wide range of soil moisture and vegetation conditions that existed and that we were able to sample with both the ground-based and aircraft instruments, SMAPVEX12 was highly successful in achieving its objectives. Data processing is currently underway. Following a data quality and team evaluation period, all data will be archived at a public website.
SLAP (Scanning L-band Active/Passive)
SLAP is a recently-developed NASA airborne instrument specially tailored to simulate the new Soil Moisture Active Passive (SMAP) satellite instrument suite. SLAP conducted its first test flights in December, 2013 and participated in its first science campaign—the IPHEX ground validation campaign of the GPM mission—in May, 2014. Here are the results from additional test flights and science observations scheduled for 2015. 118)
SLAP has both passive (radiometer) and active (radar) microwave L-band imaging capabilities. The radiometer observes at 1.4 GHz using duplicate front end hardware from the SMAP satellite radiometer. It also includes a duplicate of the digital backend development unit for SMAP, thus the novel RFI (Radio Frequency Interference) detection and mitigation features and algorithms for SMAP are duplicated with very high fidelity in SLAP. The digital backend provides 4-Stokes polarization capability. The real-aperture radar operates as a scatterometer in the 1215-1300 MHz band with quad-pol capability. Radar and radiometer share one antenna via diplexers that are spare units from the Aquarius satellite instrument.
Previous flight results: SLAP's initial flights were conducted in December 2013 over the eastern shore of Maryland and successfully demonstrated radiometer imaging over 2 full SMAP 36 x 36 km grid cells at ~1km resolution within 3 hrs (Figure 85). A second flight on the same day also demonstrated SLAP's quick-turn abilities and high-resolution/wideswath capabilities with 200 m resolution across a 1500 m swath from 2000 ft AGL (About Ground Level), Figure 86. Additional flights were conducted as part of the GPM iPHEX campaign in May, 2014. Figure 87 is an example of simultaneous radiometer and radar imagery from this campaign. Imagery from these previous flights will be reviewed.
Figure 85: Two 36x36 km SMAP grid cells (white squares) imaged by SLAP radiometer from 11000 ft AGL in December, 2013, yielding 1.3 km resolution. Note cooler thin blue brightness temperatures features following rivers and red RFI hotspot (image credit: NASA/GSFC)
Figure 86: SLAP 260 m resolution radiometer imagery from December 2013 demonstrating high-resolution capabilities (image credit: NASA/GSFC)
Figure 87: Simultaneous radiometer (top) and radar (bottom) imagery from IPHEX campaign. NE-facing fore half scans. The resolution varies from 200 m to 1000 m due to variable topography (image credit: NASA/GSFC)
ComRAD ground-based SMAP simulator
The ComRAD (Combined Radar/Radiometer) microwave instrument system used in this investigation has been developed jointly by NASA/GSFC and George Washington University. ComRAD includes a dual-pol 1.4 GHz radiometer and a quad-pol 1.24-1.34 GHz radar sharing a new 1.22 m Cassegrain parabolic dish antenna and subreflector to achieve a very low loss system. The absolute accuracy and the sensitivity of the instrument are ±1 K and ±0.1 K, respectively. External calibration is achieved using cold sky and ambient microwave absorber targets for the radiometer, and flat plates and dihedral reflectors for the radar. 119)
When deployed in the field, ComRAD is mounted on a 19 m hydraulic boom truck (Figure 88 top) and can operate over a range of incidence angles from 0º to 175º and a 300º range in azimuth. The mounting platform can also accommodate additional small instruments such as a CropScan visible/infrared sensor for vegetation reflectance measurements and a thermal infrared sensor for scene physical temperature.
Figure 88: Top: ComRAD truck-mounted instrument system deployed at the USDA OPE3 test site during July, 2012. Bottom: Schematic of ComRAD data-taking positions over soybeans (sector #2) and corn (sector #1). Approximately 60 independent radar measurements were acquired during 120º azimuthal sweeps of the boom over each crop (blue A1 – A60), while passive data were collected at 7 discrete locations (orange P1 through P7) within the 120º sectors for each crop (image credit: NASA/GSFC, USDA)
An extensive field experiment from crop planting through senescence was conducted from June to October, 2012 at the heavily instrumented USDA/ARS (U.S. Department of Agriculture/Agricultural Research Service) OPE3 (Optimizing Production Inputs for Economic and Environmental Enhancement) test site in Beltsville, MD, to acquire data needed to address active/passive microwave algorithm needs for accurate soil moisture retrieval. Vegetation cover in the experiment consisted of two crops, corn and soybeans, planted on either side of the ComRAD truck staging area (Figure 88 top). Corn was planted at the site on May 16 and harvested on October 17-18; soybeans were planted approximately one month later on June 14 and harvested on October 26. In situ soil moisture, soil temperature, and leaf wetness sensors were installed by USDA to provide continuous ground truth data.
These data were supplemented by additional soil moisture data collected manually twice a week by USDA personnel, along with weekly plant architectural, water content, and density measurements. The OPE3 site also contains a SCAN meteorological station and a flux tower which record precipitation and other micrometeorological data. ComRAD microwave measurements at the SMAP incidence angle of 40° were made on 75 days between June 1 and October 24, 2012. Active and passive data were acquired autonomously every 90 minutes (weather permitting) accordingly to the schematic in Figure 88 bottom. Manual calibration of the radiometer was performed weekly.
Analysis: Time series data from the 2012 ComRAD field experiment will be used to refine soil moisture retrieval algorithms being developed for the SMAP mission. An example of a ComRAD time series over corn from a 5-day period in late summer is shown in Figure 89. Diurnal patterns as well as an overall drying trend can be seen in the plotted data. Similar data throughout the growing season should prove useful to improving the parametrizations (related to both changing vegetation conditions and to simultaneous active/passive responses) in many of the SMAP baseline algorithms.
Figure 89: Top: Example of ComRAD time series microwave measurements over corn during August 27 – September 1, 2012 at the SMAP incidence angle of 40º. Bottom: L-band brightness temperatures at horizontal (red) and vertical (blue) polarizations along with the scene infrared temperature (green). L-band radar backscatter at HH, VV, HV, and VH polarizations acquired over the same time period as the passive data (image credit: NASA/GSFC, USDA)
SMAPEx (Soil Moisture Active Passive Experiments) campaign -Australia
The SMAPEx field campaigns utilized airborne and ground data to contribute to the development of radar-only and combined radar-radiometer soil moisture retrieval algorithms for SMAP. A key objective is calibration and scaling of an Australian cal/val site for post-launch validation of SMAP products over Australia. The Australian SMAPEx program is led by: Jeffrey Walker of Monash University and Rocco Panciera of the University of Melbourne. 120)
The study site is located in the semi-arid agricultural area near Yanco, in the Murrumbidgee Catchment, south-eastern Australia (Figure 90). This site has been extensively monitored for soil moisture with in-situ stations since 2003, and has been the focus of several airborne field experiments. It is also listed as an official SMAP core calibration/validation site. The SMAPEx campaign, held in this site consist of a series of three field campaigns specifically designed to contribute to the development of radar and radiometer soil moisture retrieval algorithms for the SMAP mission. 121) 122) 123) 124) 125)
The airborne data were collected using a SMAP simulator, which included the PLMR (Polarimetric L-band Multibeam Radiometer) and the PLIS (Polarimetric L-band Imaging Synthetic aperture radar) aboard the same aircraft. The system provided brightness temperature and backscatter coefficient at 1km and 10 m, respectively, from an altitude of 3,000 m. Such data can be used to replicate the SMAP data stream for validating algorithms applicable to the SMAP mission viewing configuration. The main SMAPEx flights included simulation of a time series of SMAP observations by coverage of a 36 km x38 km area (equivalent to a pixel of the SMAP AS grid at S latitude) with a 2-3 days revisit time. Ground sampling of soil moisture, vegetation and roughness data were conducted concurrently with the airborne flights, in order to provide ancillary and validation data. The radiometer and radar data used in this study were from the third SMAPEx campaign (SMAPEx-3, September 5-23, 2011).
Figure 90: Overview of the SMAPEx site and its focus areas, with a size of ~36km×38km, approximating one SMAP radiometer pixel. Inset shows the airborne sensors PLMR and PLIS (image credit: Monash University, University of Melbourne, NASA/JPL)
Methodology and results: Before testing the baseline downscaling algorithm with SMAPEx data, the airborne observations were processed to closely represent the SMAP data stream in terms of spatial resolution, incidence angle, and azimuth direction. Consequently, the PLMR brightness temperatures observed at ±7º, ±21.5º, and ±38.5º incidence angles, and the PLIS backscattering observed at incidence angles ranging from 15º to 45º, were both normalized to the 40º of SMAP. Subsequently, the 10 m resolution PLIS and 1km resolution PLMR were upscaled to 1km and 36 km, respectively, being the SMAP radar and radiometer L1 product resolution. The impact of changes in the azimuthal view angle was also assessed and found to be unimportant at these spatial resolutions.
An example of the resulting radar and radiometer observations is shown in Figure 91. These data were then used to test the baseline downscaling algorithm for SMAP, which is based on the hypothesis of a near linear relationship between radiometer brightness temperature and radar backscatter observations at the same resolution.
Figure 91: Example of the simulated SMAP prototype data from PLMR and PLIS observations, with the incidence angle normalized to 40°: (1) PLMR observed at 1km & at h-pol; (2) PLMR upscaled to 36km & at h-pol; (3) PLIS observed at 10 m & at vv-pol; and (4) PLIS upscaled to 1km & at vv-pol (image credit: Monash University, University of Melbourne, NASA/JPL)
Application of the algorithm involves three main steps:
1) estimation of a parameter β using a time-series of vv-pol radar and h-pol radiometer data at coarse scale
2) estimation of a parameter γ using the same time-series of radar data but at hv-pol and at vv-pol
3) estimation of vegetation conditions by the respective variation of radar backscatter at hv- and vv-pol across the entire area.
Figure 92 shows an example of the downscaling results at 1km resolution obtained from this downscaling algorithm. The RMSE (Root Mean Square Error) of the downscaled brightness temperatures with respect to the PLMR observed brightness temperatures is approximately 8-9 K at 1 km resolution and 2-4 K at 9 km resolution.
Figure 92: Example of the downscaling results at 1km resolution: (1) downscaled brightness temperature at h-pol; (2) PLMR brightness temperature observations at h-pol as the reference; (3) difference between downscaled result and PLMR observations in each pixel (image credit: Monash University, University of Melbourne, NASA/JPL)
This study has tested the baseline active passive downscaling algorithm that has thus far received quite limited evaluation using experimental data. Based on an airborne simulation of the SMAP data stream from the SMAPEx field campaigns, the downscaling algorithm was found to yield an accuracy of downscaled brightness temperature between 8 and 9 K at 1km for h-pol. The accuracy improved to between 2 and 4 K when applied at 9 km resolution. The performance of the algorithm was slightly better at v-pol (improvement of 0.7 K) than at h-pol at 9 km. The results also indicated that the error of downscaled brightness temperature is generally smaller in grassland than in crop areas by about 1 K at 9 km. The accuracy of the downscaled brightness temperature from this study also depends on the robustness of β and γ estimates derived from the SMAPEx data. Based on the downscaled brightness temperature results, it should be possible to achieve a soil moisture product at medium resolution within the specified accuracy requirement.
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65) Venkat Lakshmi, Bin Fang, "Downscaling satellite soil moisture for the NASA SMAP mission," Proceedings of the IGARSS (International Geoscience and Remote Sensing Symposium) 2015, Milan, Italy, July 26-31, 2015
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69) Kamal Sarabandi, Mani Kashanianfard, Adib Nashashibi, Ryan Hampton, Leland Pierce, "A Polarimetric Active Transponder with Extremely Large RCS for Absolute Radiometric Calibration of SMAP Radar," Proceedings of the IGARSS (International Geoscience and Remote Sensing Symposium) 2015, Milan, Italy, July 26-31, 2015
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71) Jeffrey R. Piepmeier, Jinzheng Peng, Derek Hudson, David Le Vine, Emmanuel Dinnat, Sidarth Misra, Edward Kim, Giovanni DeAmici, Priscilla N. Mohammed, "SMAP L-band microwave radiometer calibration," Proceedings of the IGARSS (International Geoscience and Remote Sensing Symposium) 2015, Milan, Italy, July 26-31, 2015
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83) Nan Ye, Jeffrey Walker Christoph Rüdiger, Xiaoling Wu, Thomas Jackson , Dara Entekhabi, Richard DeJeu, Olivier Merlin, Edward Kim, Luigi Renzullo, "The fourth SMAP Experiment (SMAPEx-4): towards validation of SMAP observations and soil moisture retrievals," Proceedings of the IGARSS (International Geoscience and Remote Sensing Symposium) 2015, Milan, Italy, July 26-31, 2015
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87) Alan Buis, "NASA's New Soil Moisture Mapper Goes for a Spin," NASA/JPL, March 24, 2015, URL: http://www.nasa.gov/jpl/smap/nasas-new-soil-moisture-mapper-goes-for-a-spin/
88) Alan Buis, Carol Rasnussen, "Let It Go! SMAP Almost Ready to Map Frozen Soil," NASA/JPL, March 13, 2015, URL: http://www.jpl.nasa.gov/news/news.php?feature=4510
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90) Alan Buis, "New NASA Space Cowboy Successfully Deploys Its 'Lasso'," NASA/JPL, Feb. 26, 2015, URL: http://www.jpl.nasa.gov/news/news.php?feature=4494
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95) A. J. Mastropietro, Eug Kwack, Rebecca Mikhaylov, Michael Spencer, Pamela Hoffman, Douglas Dawson, Jeff Piepmeier, Derek Hudson, James Medeiros, "Preliminary Evaluation of Passive Thermal Control for the Soil Moisture Active and Passive (SMAP) Radiometer," 41st ICES (International Conference on Environmental Systems), Portland, OR, USA, July 17-21, 2011, URL: http://smap.jpl.nasa.gov/files/publications/ICES_2011_Mastropietro.pdf
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99) Samuel Chan, Mark Fischman, Michael Spencer, "RFI Mitigation and Detection for the SMAP Radar," Proceedings of IGARSS (International Geoscience and Remote Sensing Symposium), Vancouver, Canada, July 24-29, 2011
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105) Damon Bradley, Cliff Brambora, Ali Feizi, Rafael Garcia, Lynn Miles, Priscilla Mohammed, Jinzheng Peng, Jeff Piepmeier, Kamdin Shakoorzadeh, Mark Wong, "Preliminary results from the Soil Moisture Active/Passive (SMAP) Radiometer Digital Electronics Engineering Test Unit (ETU)," Proceedings of IGARSS (International Geoscience and Remote Sensing Symposium), Munich, Germany, July 22-27, 2012
106) Joshua S. Choi, Antonio L. Sanders, "Cost-Effective Telemetry and Command Ground Systems Automation Strategy for the Soil Moisture Active Passive (SMAP) Mission," Proceedings of SpaceOps 2012, The 12th International Conference on Space Operations, Stockholm, Sweden, June 11-15, 2012, URL: http://www.spaceops2012.org/proceedings/documents/id1275978-Paper-004.pdf
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The information compiled and edited in this article was provided by Herbert J. Kramer from his documentation of: "Observation of the Earth and Its Environment: Survey of Missions and Sensors" (Springer Verlag) as well as many other sources after the publication of the 4th edition in 2002. - Comments and corrections to this article are always welcome for further updates (firstname.lastname@example.org).
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