Passive Mission Research Articles

Potential of L-Band Radar for Retrieval of Canopy and Subcanopy Parameters of Boreal Forests

In this paper, we study the radar retrieval of soil moisture as well as canopy parameters in a range of boreal forests. The retrieval is formulated as an optimization problem where the difference between data and prediction of a forward scattering model is minimized. The forward model is a discrete scatterer radar model, and the optimization algorithm is a global optimization scheme known as simulated annealing. The inversion method is first applied to synthetic data assuming hypothetical allometric relationships to make the retrieval possible by reducing the number of unknown vegetation parameters. The inversion algorithm is then validated using the data acquired with the National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) in June 2010 in central Canada boreal forests in support of the prelaunch calibration and validation activities of NASA's Soil Moisture Active and Passive (SMAP) mission. The inversion results for synthetic data show that the absolute retrieval error in soil moisture and relative retrieval error in canopy height are small, while the relative output error in trunk density could be large. The inversion results for actual field data show a great accuracy in soil moisture retrieval for Old Jack Pine and Young Jack Pine forests but show large retrieval errors for many of the radar pixels in the Old Black Spruce site. This paper shows that L-band radar is capable of retrieving surface soil moisture in forests with a high biomass where the forest structure allows soil moisture information to be carried by scattering mechanisms.

Sea Surface Salinity and Wind Retrieval Using Combined Passive and Active L-Band Microwave Observations

This paper describes an algorithm to simultaneously retrieve ocean surface salinity and wind from combined passive/active L-band microwave observations of sea surfaces. The algorithm takes advantage of the differing response of brightness temperatures and radar backscatter to salinity, wind speed, and direction. The algorithm minimizes the least square error (LSE) measure, signifying the difference between measurements and model functions of brightness temperatures and radar backscatter. Three LSE measures with different measurement combinations are tested. One of the LSE measures uses passive microwave data only with retrieval errors reaching 2 psu for salinity and 2 m/s for wind speed. The second LSE measure uses both passive and active microwave data for vertical and horizontal polarizations. The addition of active microwave data significantly improves the retrieval accuracy by about a factor of five. To mitigate the impact of Faraday rotation on satellite observations, we propose the third LSE measure using measurement combinations invariant under the Faraday rotation. For Aquarius, the expected root-mean-square SSS error will be less than 0.2 psu for low winds and increases to 0.3 psu at 25-m/s wind speed for warm waters, and the accuracy of retrieved wind speed will be high (about 1-2 m/s or lower). Our results suggest that combining passive and active microwave observations will allow retrieval of sea surface salinity along with the wind speed and direction. In particular, the LSE measure invariant under the Faraday rotation will be directly applicable to spaceborne missions, such as the NASA Aquarius and Soil Moisture Active Passive missions.

Long term analysis of PALS soil moisture campaign measurements for global soil moisture algorithm development

An important component of satellite-based soil moisture algorithm development and validation is the comparison of coincident remote sensing and in situ observations that are typically provided by intensive field campaigns. The planned NASA Soil Moisture Active Passive (SMAP) mission has unique requirements compared to previous soil moisture satellite programs because both active and passive microwave observations are needed. The primary source of these combined observations has been an aircraft-based SMAP simulator called PALS (Passive and Active L-band System). This paper presents an overview of the field experiment data collected using PALS that spans 10years. Data from the various campaigns were merged to form a single data set. Analyses showed that the data set contains an extensive range of soil moisture values collected under a variety of conditions and that the quality of both the PALS and ground truth data meets the needs of SMAP algorithm development and validation. The study suggests that the data set should be expanded in order to achieve globally representative land cover diversity and that more observations under dense vegetation conditions and longer time series would be desirable.

Application of QuikSCAT Backscatter to SMAP Validation Planning: Freeze/Thaw State Over ALECTRA Sites in Alaska From 2000 to 2007

The mapping of the predominant freeze/thaw state of the landscape is one of the main objectives of the National Aeronautics and Space Administration's proposed Soil Moisture Active Passive (SMAP) mission. This study applies Alaska Ecological Transect (ALECTRA) biophysical network temperature measurements and satellite radar scatterometer data from the Quick Scatterometer (QuikSCAT) to evaluate some of the validation issues regarding the planned SMAP freeze/thaw measurements. Although the QuikSCAT data are acquired at Ku-band frequency, rather than at the L-band frequency of the proposed SMAP instrument, QuikSCAT data do provide a high temporal fidelity over the ALECTRA sites, similar to SMAP. The results of this study show that multiple temperature measurements representative of individual landscape components (soil, snow cover, vegetation, and atmosphere) covering different types of terrain within the satellite field of view are important for understanding the freeze/thaw process and the aggregate radar backscatter response to that process. The backscatter temporal dynamics and relative contribution of the freeze/thaw state of these landscape elements to radar signal vary with land cover, seasonal weather, and climate conditions.

Evaluation of MERRA Land Surface Estimates in Preparation for the Soil Moisture Active Passive Mission

Abstract The authors evaluated several land surface variables from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) product that are important for global ecological and hydrological studies, including daily maximum (Tmax) and minimum (Tmin) surface air temperatures, atmosphere vapor pressure deficit (VPD), incident solar radiation (SWrad), and surface soil moisture. The MERRA results were evaluated against in situ measurements, similar global products derived from satellite microwave [the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E)] remote sensing and earlier generation atmospheric analysis [Goddard Earth Observing System version 4 (GEOS-4)] products. Relative to GEOS-4, MERRA is generally warmer (~0.5°C for Tmin and Tmax) and drier (~50 Pa for VPD) for low- and middle-latitude regions (<50°N) associated with reduced cloudiness and increased SWrad. MERRA and AMSR-E temperatures show relatively large differences (>3°C) in mountainous areas, tropical forest, and desert regions. Surface soil moisture estimates from MERRA (0–2-cm depth) and two AMSR-E products (~0–1-cm depth) are moderately correlated (R ~ 0.4) for middle-latitude regions with low to moderate vegetation biomass. The MERRA derived surface soil moisture also corresponds favorably with in situ observations (R = 0.53 ± 0.01, p < 0.001) in the midlatitudes, where its accuracy is directly proportional to the quality of MERRA precipitation. In the high latitudes, MERRA shows inconsistent soil moisture seasonal dynamics relative to in situ observations. The study’s results suggest that satellite microwave remote sensing may contribute to improved reanalysis accuracy where surface meteorological observations are sparse and in cold land regions subject to seasonal freeze–thaw transitions. The upcoming NASA Soil Moisture Active Passive (SMAP) mission is expected to improve MERRA-type reanalysis accuracy by providing accurate global mapping of freeze–thaw state and surface soil moisture with 2–3-day temporal fidelity and enhanced (≤9 km) spatial resolution.

On the Airborne Spatial Coverage Requirement for Microwave Satellite Validation

With the recent launch of the Soil Moisture and Ocean Salinity (SMOS) mission, the passive microwave remote-sensing community is currently planning and undertaking airborne validation campaigns. Given the financial and logistical constraints on the size of validation area that can be covered by airborne simulators and the experiments underway that cover only a part of a satellite footprint, timely and scientifically sound advice on fractional footprint coverage requirements by campaigns for these low-resolution sensors is of paramount importance. Using high-resolution airborne data from an extensive airborne campaign in Southeast Australia, the fractional coverage requirement for L-band passive microwave satellite missions is assessed using a subsampling technique of flight lines through a passive microwave footprint. It is shown that minimum 50% coverage of the total footprint size will typically be required, given a spatial variability value of 20 K at 1-km resolution, to ensure that the footprint mean is estimated with an expected sampling error of less than 4 K, which is the design sensitivity of SMOS.

Evaluation of multiple surface soil moisture for Korean regional flux monitoring network sites: Advanced Microwave Scanning Radiometer E, land surface model, and ground measurements

AbstractIn the past few decades, there have been great developments in remotely sensed soil moisture, with validation efforts using land surface models (LSMs) and ground‐based measurements, because soil moisture information is essential to understanding complex land surface–atmosphere interactions. However, the validation of remotely sensed soil moisture has been very limited because of the scarcity of the ground measurements in Korea. This study validated Advanced Microwave Scanning Radiometer E (AMSR‐E) soil moisture data with the Common Land Model (CLM), one of the most widely used LSMs, and ground‐based measurements at two Korean regional flux monitoring network sites. There was reasonable agreement regarding the different soil moisture products for monitoring temporal trends except National Snow and Ice Data Centre (NSIDC) AMSR‐E soil moisture, albeit there were essential comparison limitations by different spatial scales and soil depths. The AMSR‐E soil moisture data published by the National Aeronautics and Space Administration and Vrije Universiteit Amsterdam (VUA) showed potential to replicate temporal variability patterns (root‐mean‐square errors = 0·10–0·14 m3 m−3 and wet BIAS = 0·09 − 0·04 m3 m−3) with the CLM and ground‐based measurements. However, the NSIDC AMSR‐E soil moisture was problematic because of the extremely low temporal variability and the VUA AMSR‐E soil moisture was relatively inaccurate in Gwangneung site characterized by complex geophysical conditions. Additional evaluations should be required to facilitate the use of recent and forthcoming remotely sensed soil moisture data from Soil Moisture and Ocean Salinity and Soil Moisture Active and Passive missions at representative future validation sites. Copyright © 2011 John Wiley & Sons, Ltd.

Radar Retrieval of Surface and Deep Soil Moisture and Effect of Moisture Profile on Inversion Accuracy

We study the retrieval of surface and deep moisture of bare soil from noisy radar observations using simulated annealing. Due to moisture variations with depth, we model bare soil with a stratified dielectric profile with a rough surface on top. Small perturbation method (SPM) is used as the forward model. We use the full moisture profile for radar data synthesis and study the retrieval accuracy by varying the number of layers that represent the soil profile during inversion. The effect of measurement frequency on the accuracy of deep moisture retrieval is investigated. This work is intended for assessing the effect of subsurface profile on soil moisture retrieval from radar observations of NASA's Soil Moisture Active and Passive (SMAP) mission and future lower frequency airborne or spaceborne systems that may follow SMAP.

An Algorithm for Merging SMAP Radiometer and Radar Data for High-Resolution Soil-Moisture Retrieval

A robust and simple algorithm is developed to merge L-band radiometer retrievals and L-band radar observations to obtain high-resolution (9-km) soil-moisture estimates from data of the NASA Soil Moisture Active and Passive (SMAP) mission. The algorithm exploits the established accuracy of coarse-scale radiometer soil-moisture retrievals and blends this with the fine-scale spatial heterogeneity detectable by radar observations to produce a high-resolution optimal soil-moisture estimate at 9 km. The capability of the algorithm is demonstrated by implementing the approach using the airborne Passive and Active L-band System (PALS) instrument data set from Soil Moisture Experiments 2002 (SMEX02) and a four-month synthetic data set in an Observation System Simulation Experiment (OSSE) framework. The results indicate that the algorithm has the potential to obtain better soil-moisture accuracy at a high resolution and show an improvement in root-mean-square error of 0.015-0.02-cm3/cm3 volumetric soil moisture over the minimum performance taken to be retrievals based on radiometer measurements resampled to a finer scale. These results are based on PALS data from SMEX02 and a four-month OSSE data set and need to be further confirmed for different hydroclimatic regions using airborne data sets from prelaunch calibration/validation field campaigns of the SMAP mission.

Backscattering Coefficients, Coherent Reflectivities, and Emissivities of Randomly Rough Soil Surfaces at L-Band for SMAP Applications Based on Numerical Solutions of Maxwell Equations in Three-Dimensional Simulations

We used Numerical Maxwell Model in 3-D Simulations (NMM3D) to study the backscattering coefficients, coherent reflectivities, and emissivities of soil surfaces using Gaussian random rough surfaces with exponential correlation functions. The surface area used is 8 by 8 square wavelengths. A total of close to 200 cases are computed by varying rms height, correlation length, and soil permittivity. We consider a 40° incidence angle. For each case, 15 realizations of rough surface profiles are generated, and 30 solutions of Maxwell equations are computed because of two polarizations. The method for solving the Maxwell equations is based on the Method of Moments (MoM) with Rao-Wilton-Glisson (RWG) basis functions. The solutions are accelerated by the sparse matrix canonical grid method implemented on parallel computing. The rms height varies up to 0.126 wavelength. The results are compared with the Dubois formulation, Small Perturbation Method (SPM), Kirchhoff Approximation (KA), and Advanced Integral Equation Model (AIEM). The NMM3D results are also compared with VV and HH backscatter data of soil surfaces where ground truth rms heights and correlation lengths were both measured. Good agreement is found between the NMM3D results and experimental measurement data. Based on the computed cases, interpolation tables are derived that can be directly applied to L-band active and passive microwave remote sensing of soil moisture, such as for the upcoming Soil Moisture Active and Passive (SMAP) mission.

The Soil Moisture Active Passive (SMAP) Mission

The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey. SMAP will make global measurements of the soil moisture present at the Earth's land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere. The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers. Soil moisture measurements are also directly applicable to flood assessment and drought monitoring. SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP is scheduled for launch in the 2014-2015 time frame.

Planning for a Canadian Contribution to a Soil Moisture Mission: First Workshop on Canadian SMAP Applications and Cal‐Val; Montreal, Quebec, Canada, 6–7 October 2009

The Soil Moisture Active and Passive (SMAP) mission will combine low‐frequency microwave radiometer and high‐resolution radar instruments to measure surface soil moisture and freeze‐thaw state. This NASA mission, managed by the Jet Propulsion Laboratory, has the potential to enable a diverse range of applications including drought and flood guidance, agricultural productivity estimation and risk mitigation, weather forecasting, climate predictions, human health risk assessment and mitigation, and defense systems. Recognizing the potential relevance of SMAP's measurements for Canada, Environment Canada (EC) and the Canadian Space Agency (CSA) are joining efforts to develop Canadian participation in this mission.As part of this effort, the First Workshop on Canadian SMAP Applications and Cal‐Val was held in Canada. The main objective of this workshop was to develop a consolidated plan for Canadian participation in the SMAP mission that would address the needs of different Canadian government departments and academia.

Planning for a Soil Moisture Satellite Mission: SMAP Algorithms & Cal/Val Workshop; Oxnard, California, 9–11 June 2009

The Soil Moisture Active and Passive (SMAP) mission aims to gather high‐resolution global soil moisture and freeze/thaw state data, which should enable improvements to weather and climate forecasts, flood prediction and drought monitoring, and estimations of net carbon dioxide (CO2) uptake in forested regions. Targeted for launch in 2014, SMAP is one of four missions recommended by the U.S. National Research Council Committee on Earth Science and Applications From Space for launch in the early part of the next decade (Earth Science and Applications From Space: National Imperatives for the Next Decade and Beyond, Natl. Acad. Press, 2007).The SMAP mission concept utilizes an L‐band (1.20–1.41 gigahertz) radar and a radiometer that share a rotating 6‐meter mesh reflector antenna to provide high‐resolution and high‐accuracy global maps of soil moisture and freeze/thaw state every 2–3 days.

Effect of Land Cover Heterogeneity on Soil Moisture Retrieval Using Active Microwave Remote Sensing Data

This study addresses the issue of the variability and heterogeneity problems that are expected from a sensor with a larger footprint having homogenous and heterogeneous sub-pixels. Improved understanding of spatial variability of soil surface characteristics such as land cover and vegetation in larger footprint are critical in remote sensing based soil moisture retrieval. This study analyzes the sub-pixel variability (standard deviation of sub-grid pixels) of Normalized Difference Vegetation Index and SAR backscatter. Back-propagation neural network was used for soil moisture retrieval from active microwave remote sensing data from Southern Great Plains of Oklahoma. The effect of land cover heterogeneity (number of different vegetation species within pixels) on soil moisture retrieval using active microwave remote sensing data was investigated. The presence of heterogeneous vegetation cover reduced the accuracy of the derived soil moisture using microwave remote sensing data. The results from this study can be used to characterize the uncertainty in soil moisture retrieval in the context of Soil Moisture Active and Passive (SMAP) mission which will have larger footprint.

Skylab L-band microwave radiometer observations of soil moisture revisited

In preparing for future L-band passive microwave soil moisture satellite missions, investigators have employed ground, aircraft and satellite sensors. Of the satellite sensors, there has been only one instrument that provides any heritage at L-band: the Skylab S-194 instrument that operated in the 1970s. Here a dataset from the S-194 was located and recovered. Data from these Skylab missions have been analysed and reported in a few applications, however, these studies utilized limited validation and exploited only a portion of the data collected. In this investigation we explored the use of products from climate model reanalysis projects as ancillary or alternative validation data. Analyses showed that the reanalysis outputs were not accurate and would be of limited value. Tests using a radiative transfer based soil moisture retrieval algorithm matched the observations available for validation. These results support the use of this approach as a tool in understanding a wider range of vegetation condition effects on soil moisture retrieval.