PERMAFROST and seasonally frozen ground occupy about

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1 542 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 53, NO. 1, JANUARY 2015 Classification of Alaska Spring Thaw Characteristics Using Satellite L-Band Radar Remote Sensing Jinyang Du, Member, IEEE, John S. Kimball, Member, IEEE, Marzieh Azarderakhsh, R. Scott Dunbar, Mahta Moghaddam, Fellow, IEEE, and Kyle C. McDonald, Senior Member, IEEE Abstract Spatial and temporal variability in landscape freeze thaw (FT) status at higher latitudes and elevations significantly impacts land surface water mobility and surface energy partitioning, with major consequences for regional climate, hydrological, ecological, and biogeochemical processes. With the development of new-generation spaceborne remote sensing instruments, future L-band missions, including the NASA Soil Moisture Active and Passive mission, will provide new operational retrievals of landscape FT state dynamics at moderate ( 3 km) spatial resolution. We applied theoretical simulations of L-band radar backscatter using first-order radiative transfer models with twoand three-layer modeling schemes to develop a modified seasonal threshold algorithm (STA) and FT classification study over Alaska using 100-m-resolution satellite Phased Array L-band Synthetic Aperture Radar (PALSAR) observations. The backscatter threshold distinguishes between frozen and nonfrozen states, and it is used to classify the predominant frozen or thawed status of a grid cell. An Alaska FT map for April 2007 was generated from PALSAR (ScanSAR) observations and showed a regionally consistent but finer FT spatial pattern than an alternative surface air temperature-based classification derived from global reanalysis data. Validation of the STA-based FT classification against regional soil climate stations indicated approximately 80% and 75% spatial classification accuracy values in relation to respective station air temperature and soil temperature measurement-based FT estimates. An investigation of relative spatial scale effects on FT classification accuracy indicates that the relationship between grid cell size and classified frozen or thawed area follows a general logarithmic function. Index Terms Alaska, freeze thaw (FT), microwave scattering model, Modern Era Retrospective Analysis for Research and Applications (MERRA), Phased Array L-band Synthetic Aperture Radar (PALSAR), Soil Moisture Active and Passive (SMAP). Manuscript received August 20, 2013; revised December 23, 2013 and March 28, 2014; accepted May 5, Part of this work was performed at the University of Montana and Jet Propulsion Laboratory, California Institute of Technology, supported through the National Aeronautics and Space Administration under Contract NNX08AQ63A and Contract NNX11AP68A. This work was undertaken in part within the framework of the JAXA ALOS Kyoto & Carbon Initiative. J. Du and J. S. Kimball are with the Flathead Lake Biological Station, University of Montana, Polson, MT USA, and also with the Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT USA ( jinyang.du@ntsg.umt.edu; johnk@ntsg.umt.edu). M. Azarderakhsh is with the Earth and Atmospheric Sciences, The City College of New York, New York, NY USA ( mazarderakhsh@ccny. cuny.edu). R. S. Dunbar is with the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA USA ( Roy.S.Dunbar@jpl.nasa.gov). M. Moghaddam is with the The Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA USA ( mahta@usc.edu). K. C. McDonald is with Earth and Atmospheric Sciences, The City College of New York, New York, NY USA, and also with the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA USA ( kmcdonald2@ccny.cuny.edu). Digital Object Identifier /TGRS I. INTRODUCTION PERMAFROST and seasonally frozen ground occupy about 24% and 56% of the exposed land area (excluding glaciers, ice sheets, and water bodies) of the Northern Hemisphere [1], [2]. Seasonal freeze thaw (FT) events occur over approximately 66 million km 2 (i.e., 52.5%) of the global land area each year [3]. The landscape FT transitions coincide with relatively abrupt phase changes of moisture in vegetation, snow, and soil that dramatically impact surface water mobility and energy partitioning, with major consequences for atmospheric profile development and regional weather patterns [4], vegetation productivity [5] [7], and hydrological processes [8]. The characteristics of permafrost and soil active layer FT processes are also susceptible to global change. The active layer is the surficial layer of earth materials subject to freezing and thawing on an annual basis [9], [10]. Due to recent global warming, widespread earlier spring thawing and lengthening of the nonfrozen season has been documented over the northern high latitudes [11] [13]. Earlier thawing and associated permafrost degradation have been also linked to ecosystem deterioration and desertification of water-limited regions such as the Tibetan Plateau [14]. Moreover, the degradation of permafrost may also exert a positive feedback to global warming by increasing greenhouse gas emissions through enhanced decomposition of northern soil carbon stocks [15], [16]. The key importance of landscape FT processes to climate, hydrological, ecological, and biogeochemical processes highlights the need for accurate detection and monitoring of FT state dynamics in permafrost landscapes. Along with in situ measurements from sparse station networks, satellite microwave remote sensing has been widely used in mapping the spatial and temporal distribution of regional FT states [3], [17], [18]. The basic physical principle applied in FT mapping is the relatively strong sensitivity of passive microwave brightness temperature (Tb) and active radar backscatter to large temporal shifts in landscape dielectric properties as the land surface transitions between predominantly frozen and nonfrozen conditions. The relative magnitude of the Tb or backscatter response to the FT transition is dependent upon microwave frequency and the relative abundance of liquid water in the soil surface layer and above ground land features, including vegetation and snow cover [19]. In particular, for boreal and tundra landscapes, the FT signal generally dominates the seasonal range of microwave signal variability [20]. By exploiting this principle, a number of algorithms for FT detection have been developed and applied to different spaceborne active and passive microwave IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.

2 DU et al.: CLASSIFYING ALASKA SPRING THAW CHARACTERISTICS 543 sensors. The classification methods included FT threshold [3], [21] [24], decision tree [25], and moving-window approaches [18], [26] [28]. However, current FT mapping activities have several limitations. (a) Available spaceborne radiometers and scatterometers from polar orbiting satellites have generally high-temporal fidelity ( 1 2 days), particularly at higher (> 50 N) latitudes, but coarse ( 25 km) spatial resolution, which severely constrains the characterization of FT spatial heterogeneity and underlying processes. (b) Alternatively, currently available spaceborne synthetic aperture radar (SAR) instruments have relatively fine (on the order of m) spatial resolution but coarse (on the order of weeks) temporal repeat observations, which constrains effective classification of dynamic FT variability. (c) Most of the global FT products are generated from satellite radiometer or scatterometer observations at moderately high frequencies (C-band or higher). Higher frequency retrievals are less sensitive to surface soil conditions relative to lower (e.g., L-band) frequency measurements, with more direct signal contributions from intervening snow cover and vegetation layers. These constraints may be partially overcome with new generation sensors such as the NASA Soil Moisture Active and Passive (SMAP) mission [29]. The SMAP mission is scheduled to launch in 2014 and will provide low-frequency (L-band) SAR operational FT state retrievals at moderate (3-km) spatial resolution and 3-day global repeat observations. Despite the availability of global FT data records from current satellite microwave Earth observations [3], [30] and planned L-band FT measurements from upcoming satellite missions, there is still much uncertainty regarding the sensitivity of satellite L-band radar backscatter observations to soil FT processes in permafrost landscapes. Little is known regarding the accuracy and reliability of L-band SAR-based detection algorithms or spatial scaling properties of the resulting FT retrievals for boreal Arctic landscapes. This information is needed to verify and refine classification algorithms and define optimum spatial scales for FT mapping in permafrost landscapes and their relations with coarser scale and higher frequency FT retrievals available from current global data records. The objectives of this investigation were to: 1) analyze the L-band radar backscatter sensitivity to surface soil and vegetation FT signals representative of Alaska boreal forest and tundra landscapes using theoretical radiative transfer (RT) model simulations of L-band radar backscatter dynamics; 2) apply the RT model simulation results with available satellite L-band SAR data to develop a suitable FT detection algorithm and Alaska regional FT classification with verified accuracy; and 3) apply the resulting baseline (100-m-resolution) FT classification with coarser resolution retrievals to clarify the relative dropoff in spatial classification accuracy from larger footprint satellite measurements. The FT classification results from this paper were validated using in situ observations, including surface air and soil temperature measurements from regional monitoring sites distributed across the Alaska domain. II. APPROACHES AND METHODS A seasonal threshold algorithm (STA) [30]-based FT classification method was applied to Phased Array L-band SAR Fig. 1. Map of the Alaska study region and regional land-cover classification. The subregions used for studying the FT spatial scaling properties are delimited by the red rectangles and the in situ sites used to validate the radar FT retrievals are denoted by the red points. (PALSAR) backscatter time series over the Alaska domain. The STA approach involves a temporal change classification of radar backscatter in relation to reference frozen and nonfrozen state threshold conditions. The FT threshold values at different sensor measurement incident angles were determined from both PALSAR observations and forward RT model L-band radar backscatter simulations for each 100-m pixel within the study region; these results were then used with the PALSAR multiangle backscatter data for regional FT classification. The resulting FT classification map was evaluated using an alternative FT classification derived using relatively coarse ( 0.5 ) resolution daily surface air temperatures from the Modern Era Retrospective Analysis for Research and Applications (MERRA) global reanalysis [31]. The PALSAR classification results were also validated against in situ FT estimates determined from regional station air and soil temperature network observations. For studying the relationship between the pixel size of sensor retrievals and relative FT spatial classification accuracy, a scaling analysis was also carried out based on the resulting PALSAR 100-m-resolution FT classification map. The following section describes the Alaska study domain and subregions; in situ stations used for theoretical RT model simulations; and FT classification algorithm development,

3 544 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 53, NO. 1, JANUARY 2015 TABLE I SURFACE WEATHER STATIONS USED FOR VALIDATING THE FT DETECTION ALGORITHM AND CLASSIFICATION. LAND-COVER-TYPE INFORMATION IS BASED ON THE ALASKA SCIENCE CENTER NATIONAL LAND-COVER MAP (NLCD) [41], AND LEVEL-2 ECOREGIONS ARE DEFINED BY THE U.S. GEOLOGICAL SURVEY [34]. WEATHER STATION INFORMATION IS FROM THE GEOPHYSICAL INSTITUTE PERMAFROST LABORATORY [37], NATURAL RESOURCES CONSERVATION SERVICE [38], AND BONANZA CREEK LTER UNIVERSITY OF ALASKA FAIRBANKS [39] validation, and scaling analysis. Section II-B describes the PALSAR observations, MERRA data sets, and the Alaska landcover map used for FT classification and validation. A. Study Domain and In Situ Validation Sites 1) Alaska Study Region: We selected an Alaska domain for this investigation (see Fig. 1). The study region is predominantly composed of boreal forest and tundra biomes characterized by a relatively short nonfrozen season in spring and summer that effectively bounds the active vegetation growing season [28], [30]. The growing season length varies spatially and temporally across Alaska and is approximately 120 days for tundra and 170 days for boreal forest biomes [32]. The effective FT classification area for the domain was defined by the spatial coverage of available L-band satellite SAR data from the JAXA Advanced Land Observing Satellite (ALOS) PALSAR sensor [33]. The Alaska domain encompasses three general ecoregions, including polar, boreal, and maritime zones [32], [34]. The polar ecoregion (24.8% of Alaska) includes Arctic and Bering tundra zones characterized as nonforested terrain with a cold dry climate. The boreal ecoregion (56.9%) is identified by predominantly evergreen coniferous needleleaf forest, with a severe continental climate ranging between warm summers and very cold winters. The maritime ecoregion (18.3%) is characterized by productive coniferous forests and a cool wet climate. About km 2 (81%) of Alaska s land surface lies within the northern permafrost zone, ranging from continuous permafrost in polar tundra areas to discontinuous, sporadic, or degraded permafrost in the boreal zone [32]. PALSAR data for years 2007 and 2010 were used for regional FT mapping in this investigation. These data covered approximately 63.5% ( km 2 ) of the Alaska domain and mainly consisted of polar and boreal ecoregions. 2) Subregion for Studying FT Scaling Effects: In Alaska, mountain ranges act as topographic barriers that strongly influence vegetation and climate distributions. The Brooks Range extends in a general east west direction separating cold northern Arctic tundra from warmer boreal forest zones, with the northern extent of taiga timberline on the southern slopes of the Brooks Range near latitude 68 N [35]. The Alaska domain is also characterized by a strong latitudinal climate gradient. To investigate FT scaling properties, we defined three subregions (see Fig. 1) within the larger Alaska domain spanning a latitudinal gradient encompassing the spring FT transition from predominantly frozen to nonfrozen conditions represented by the resulting PALSAR FT classification map from this paper. The northern subregion (1; N, W)

4 DU et al.: CLASSIFYING ALASKA SPRING THAW CHARACTERISTICS 545 is located within the Arctic foothills of the Alaska North Slope and is predominantly composed of tundra underlain by continuous permafrost. The central subregion (2; N, W) lies within the Brooks Range complex and is characterized by complex topography; vegetation is predominantly composed of tundra, dwarf scrub, and shrub at lower elevations and barren land at upper elevations, whereas the region is largely underlain by continuous permafrost. The southern subregion (3; N, W) is predominantly composed of boreal evergreen forest and rolling topography underlain by discontinuous permafrost. 3) In Situ Validation Sites: Two groups of in situ validation sites were used in this paper. The first group was used for theoretical RT model validation and analysis and consisted of a representative tundra site (A-1) and boreal forest site (Smith 1). Located within the Alaska Arctic National Wildlife Refuge, the A-1 site represents an Alaska coastal tundra ecosystem [36]. In situ biophysical measurements within the 100 m 100 m area of the A-1 site were used for validating two-layer first-order RT model simulations of L-band radar backscatter characteristics. The site measurements included surface soil moisture (0 15-cm depth), soil surface roughness (rootmean-square height and correlation length of soil surface), and vegetation height; the in situ measurements were obtained with concurrent PALSAR observations [36]. The Smith 1 site [37] provided measurements on air and soil temperatures ( cm), vegetation type, and density; these data were used with overlying PALSAR observations for validation and analysis of L-band backscatter simulations over boreal forest using a three-layer first-order RT model. A second group of 30 in situ soil climate stations spanning the latitudinal range of the Alaska domain (see the red points in Fig. 1) was used for independent validation of the PALSAR FT classification. The validation sites are summarized in Table I, whereas detailed site descriptions are provided elsewhere [37] [39]. The site measurements included in situ air and surface (< 10-cm depth) soil temperature measurements that were used to infer site FT state conditions for validating the overlying PALSAR FT classification. B. Satellite SAR Observations and Ancillary Data 1) ALOS PALSAR: The PALSAR sensor on board JAXA ALOS is one of the few L-band spaceborne radars. PALSAR operated from January 2006 to May 2011 and provides a valuable database for studying L-band SAR-based landscape FT detection. The PALSAR ScanSAR mode was capable of acquiring radar backscatter data over a swath as large as 350 km, resulting in a variable incident angle of the radar backscatter retrievals. Different from the coarse-resolution ( 25 km) and high-temporal repeat observations ( 1 2 days revisit time) of spaceborne radiometers and scatterometers, PALSAR ScanSAR had a much finer spatial resolution ( 100-m ground resolution) but much lower temporal fidelity (46-day orbit revisit). In addition to less frequent observations, the incident angle of each ScanSAR observation of a specific location may vary from 18 to 43. The lack of constant incident angle and sparse temporal coverage are primary constraints for developing an effective PALSAR FT detection algorithm. In this paper, an algorithm was developed for regional FT classification using multitemporal imagery from PALSAR L-band (1.27-GHz) HH-polarized ScanSAR data, with coverage over Alaska for years 2007 and PALSAR data for the 2007 study period were used for establishing the FT classification algorithm, whereas both the 2007 and 2010 PALSAR data sets were used for algorithm validation. The ScanSAR mode images acquired from the PALSAR sensor were processed based on a protocol developed for the NASA- MEaSUREs Inundated Wetlands Earth System Data Record, which includes coregistration of the SAR data with the National Elevation Data set 2 arc-second digital elevation map, correction for topographic effects, removal of processing artifacts, antenna pattern correction, removal of cross-track banding, and absolute calibration [40]. In addition, a nonadaptive median filter was applied to the images to suppress speckle noise. 2) Ancillary Data: Two ancillary data sets were adopted to develop and validate the FT detection algorithm: (a) The Alaska Science Center NLCD [41] was resampled from its original 30-m-resolution Albers Equal Area Conic projection to the same 100-m-resolution format of the PALSAR data. These data provided the background land-cover information (e.g., Fig. 1) for the FT retrieval and validation assessment; (b) gridded daily maximum and minimum surface air temperatures (grid resolution 1/2 2/3 ) were extracted from the NASA Global Modeling and Assimilation Office (GMAO) MERRA global reanalysis product [31]; MERRA air temperatures were used as a surrogate indicator of land surface FT state conditions for PALSAR FT algorithm development and validation, as described in Sections III and IV. The MERRA land product has been previously verified over northern land areas [42] and is being used as a surrogate for the development of future level-4 (L4) model enhanced soil moisture and carbon products for the NASA SMAP mission [29]. III. METHODS A. General Principles of PALSAR FT Mapping An STA was adopted for FT classification from L-band PALSAR backscatter over the Alaska domain. A similar STA approach has been used for constructing a long-term global FT data record from higher frequency (37-GHz) satellite microwave Tb records [30] and will be also used for production of an operational global FT product derived from NASA SMAP L-band radar backscatter retrievals [29], [43]. The STA approach relies on a pixelwise FT temporal change classification of radar backscatter differences from reference frozen or nonfrozen conditions. The STA generally defines the FT threshold using backscatter values corresponding to frozen and nonfrozen reference state conditions [30]. However, due to the lack of a constant incident angle of PALSAR ScanSAR [33], an angular backscatter curve was used instead of a single reference value to define representative frozen and nonfrozen reference state backscatter values for each pixel according to the variable incident angle SAR observations. The resulting freeze and thaw reference state curves were used to define reference frozen and nonfrozen state conditions for this paper. To compensate for the temporally coarse (46-day revisit)

5 546 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 53, NO. 1, JANUARY 2015 fidelity of the Alaska PALSAR observations, the reference curves were determined using both the PALSAR observations and RT model radar backscatter simulations. Here, we determined the reference curve that has the minimum root-meansquare difference (RMSD) between the simulations and the PALSAR observations at frozen or thawed conditions from the theoretical prediction database. This process actually generalizes available PALSAR observations, which could be made under different land surface parameters and observation configurations into two reference curves, which best resemble the actual PALSAR observations. With the freeze and thaw reference curves defined for each pixel, the FT state can be inferred from the PALSAR observations as FT state is thawed if σ(θ, t) σ fr (θ) > σ(θ, t) σ th (θ) FT state is frozen if σ(θ, t) σ fr (θ) σ(θ, t) σ th (θ) (1) where σ(θ, t) is the PALSAR observation at incident angle θ and time t, σ fr (θ) is the reference value for the frozen state at incident angle θ, and σ th (θ) is the reference value for the thawed state. Similarly, for a given incident angle θ, the conventional threshold T for determining the FT state is implicit in (1) and can be written as T (θ) = (σ th(θ)+σ fr (θ)). (2) 2 If the SAR backscatter for a given pixel is higher than threshold T (θ) or the observed backscatter is closer to the thaw reference curve value, then the pixel is classified as thawed. Likewise, a pixel is classified as frozen when the observed backscatter is closer to the frozen reference curve value. B. Radar Backscatter Modeling A set of forward radar backscatter model simulations was carried out to describe the characteristics of radar backscatter from boreal forest and tundra under frozen and thawed conditions. These simulations were used to clarify L-band radar backscatter sensitivity to temporal FT transitions in surface soil and vegetation layers. The theoretical RT model simulations were also used for determining the freeze/thaw reference curves used in the PALSAR FT classification. The relatively strong sensitivity of landscape dielectric properties and associated radar backscatter to the predominant frozen or nonfrozen condition of liquid water in surface soil, snow, and vegetation elements within the satellite field-of-view provides the basis for active remote sensing of the FT state signal [19], [20]. In addition to dielectric properties, radar backscattering from a natural landscape is also affected by other factors, including sensor (frequency, polarization, and incidence geometry), vegetation (scatterer density, size, shape, and orientation), and soil surface roughness parameters. Theoretical models accounting for most of the above factors are needed for realistic simulation and understanding of the interactions between microwave and landscape components and radar backscatter under frozen and thawed conditions. For the L-band scattering problem, the first-order solution of the RT equation has been found to be generally consistent with observational data [44], [45]. Based on the general first-order RT solution, we applied a two-layer model and a three-layer model to simulate radar backscatter properties under typical scenarios of Alaska tundra and boreal forest, respectively. Alaska tundra is characterized by lower biomass tussock and shrub vegetation cover and cold climate conditions [32]. For modeling relatively short tundra vegetation without a significant tree stem layer, we applied a two-layer first-order RT modeling framework similar to a previous study involving Alaska tundra [36]. The first RT model layer represents a simple vegetation mat, whereas the second layer represents the surface soil layer. The vegetation scatterers of the first layer are modeled as prolate ellipsoids. To calculate the phase matrix of the vegetation layer, a generalized Rayleigh Gans model is employed [45], [46]. For the soil layer, the advanced integral equation model (IEM) [47] was adopted to simulate the direct ground backscatter and the incoherent interactions between the vegetation layer and underlying soil surface. With a wide range of potential input parameters available to define backscatter properties of the vegetation layer, including the scatterers dielectric and geometric properties and the layer depth, the model can be used to simulate the copolarized L-band backscatter from tundra vegetation, including dwarf shrub, herbaceous tussock vegetation, and moss-dominated systems. For boreal forest, a more complex modeling scheme such as the Michigan Microwave Canopy Scattering (MIMICS) model [44] is needed to account for the scattering from forest woody stem and branch layers. MIMICS is a three-layer first-order RT model, where the tree canopy is divided into a crown layer, trunk layer, and the underlying soil surface [44]. The MIMICS model has been widely used for simulating radar backscatter dynamics over forest [44], [48]. In this paper, the two layer RT model and three-layer MIMICS model were adopted to simulate radar backscatter dynamics for Alaska tundra and boreal forest, respectively, in context with satellite L-band SAR observations from ALOS PALSAR. C. Procedures for Model-Based PALSAR FT Mapping The following steps were conducted to acquire the freeze/ thaw reference curves and accomplish the pixelwise FT retrievals (described above). 1) Generating the Theoretical Simulation Database: The forward RT model simulations of L-band radar backscatter dynamics were conducted for representative Alaska tundra and boreal forest vegetation types to describe the theoretical backscatter behavior, including relative L-band sensitivity to soil and vegetation FT conditions. The resulting backscatter simulations were also used to guide the PALSAR data-driven FT classifications over the Alaska domain. The RT model simulations used to define the reference curves for L-band FT detection were carried out for boreal forest and tundra, separately. The radar backscatter simulations for tundra vegetation were defined using a set of representative inputs (see Table II) and the two-layer RT model. The input parameters of the two-layer RT model correspond to variable soil and vegetation conditions. For example, when the vegetation (first layer) optical thickness is near zero and its dielectric value is around 1.0, it actually indicates a bare-soil case without significant vegetation cover. Similarly, when the vegetation optical

6 DU et al.: CLASSIFYING ALASKA SPRING THAW CHARACTERISTICS 547 TABLE II RT MODEL INPUT PARAMETERS USED FOR GENERATING THE NONFOREST L-BAND RADAR BACKSCATTER DATABASE TABLE III RT MODEL INPUT PARAMETERS USED FOR GENERATING THE BOREAL FOREST L-BAND RADAR BACKSCATTER DATABASE thickness is greater than zero, the model simulations represent backscattering from a vegetation-covered soil surface, such as grassland or shrubland. For the boreal forest, simulations were made using the three-layer MIMICS model with associated input parameters defined in Table III. The MIMICS model inputs (see Table III) encompassed Alaska forest parameters acquired from previous field experiments [48], [49] but defined a much larger range of variability in these parameters with a number of possible soil surface parameters, vegetation water content, and forest geometry parameters considered. Four databases were thus generated based on the above simulations, including backscatter databases for boreal forest when frozen and thawed, and backscatter databases for nonforest vegetation types when frozen and thawed. 2) Defining Reference Curves for the FT State: To classify the FT state on a pixelwise basis, the Alaska domain was first mapped to a 100-m-resolution raster map in the Alaska Albers Equal Area Conic projection as the base map, consistent with the NLCD Alaska land-cover map [41] and PALSAR ScanSAR product resolution. The theoretical RT model simulations produced a group of angular backscatter curves for variable SAR incident angles under estimated real-world conditions for forest and nonforest vegetation types. With the broad range of model input parameters (see Tables II and III), there was one angular curve determined from the resulting radar backscatter simulations that best represented the predominant frozen or thawed state of each 100-m-resolution pixel. In addition to the RT model backscatter simulation databases, another step necessary for obtaining the frozen and nonfrozen reference curves was to identify the FT state of the pixel during each PALSAR observation. Considering the relatively sparse network of in situ biophysical measurements over the Alaska domain, daily surface minimum and maximum air temperatures from the GMAO MERRA global reanalysis [31] were used as a surrogate indicator of frozen or nonfrozen conditions. For each 100-mresolution pixel, the reference frozen state was determined only when the daily maximum air temperature obtained from MERRA was colder than 5 C and the reference thawed state was determined only when the daily minimum temperature was warmer than 5 C. This criterion helped minimize potential classification errors due to uncertainties from temperature spatial heterogeneity and the coarse (1/2 2/3 ) MERRA grid. Following the above criteria, the PALSAR observations for each grid cell over the 2007 study period were extracted and classified as frozen or thawed based on temperature conditions of the overlying coarser resolution MERRA grid cell. Finally, the best reference curves with lowest RMSD between PALSAR observations, and RT model simulations were determined for each grid cell from one of the four theoretical backscatter databases, depending on the MERRA-indicated FT state and NLCD defined vegetation (forest or nonforest) type. The resulting freeze and thaw reference curves, or the FT threshold at

7 548 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 53, NO. 1, JANUARY 2015 TABLE IV RT MODEL INPUT PARAMETERS FOR THE A-1 ALASKA TUNDRA SITE the specified sensor incident angle determined by the reference curves, were then used for FT classification of the PALSAR backscatter time series. IV. RESULTS AND DISCUSSION A. Model Simulation Results for Tundra Comparisons between the radar backscatter simulations from the two-layer RT model and PALSAR observations were made for the representative Arctic tundra site (A-1). The Arctic tundra was characterized using two RT model layers following [36] that included a shallow ( 10 cm thick) surface sphagnum moss layer underlain by a 10-cm-thick organic soil layer. The model inputs were listed in Table IV, with dielectric value ε, and surface roughness parameters, ks (i.e., s is the surface rootmean-square height; k is the wavenumber) and kl (i.e., l is the surface correlation length), obtained from in situ measurements. Similar to [36], the albedo and optical thickness for the A-1 site simulations were computed as best fit parameters to minimize the difference between RT-model-predicted and PALSAR-measured backscatter coefficients. As shown in Fig. 2(a) and (b), the RT model backscatter simulations from this paper are similar to the limited PALSAR observations from the 2007 summer (nonfrozen) record with a model observation difference of less than 0.6 db. The contributions to the total estimated radar backscatter signal from the different scattering mechanisms [see Fig. 2(a) and (b)] indicate that the dominant contribution to the copolarized backscatter could come from surface scattering from the thin wet surface moss layer, which is consistent with previous findings [36]. Considering that the surface moss layer for tundra generally varies in wetness over the nonfrozen season, simulations were also made at a 40 incident angle with first-layer dielectric constant at 5.3, 4.1, 2.9, and 1.7 while keeping the other parameters unchanged; the resulting VV-polarized backscatter component of the first-layer exponentially decreased with the proportional contribution to the total signal of 43%, 27%, 11%, and 2%, respectively. However, with more backscatter contributions from the underlying wet-soil layer, the total backscattering coefficient increased from 11.8 to 8.05 db. The HH-polarized backscatter simulations for the A-1 site showed similar results. Additional RT simulations were made for the A-1 tundra scenario under frozen moss and soil layer conditions. Assuming that the liquid water contained in moss and soil layers becomes frozen, the dielectric values of both the moss and soil layers are therefore substantially decreased under frozen conditions, and the dominant scatterers of the frozen first (moss) layer were modeled as ice particles. As illustrated in Fig. 2(c) and (d), the moss layer is almost transparent to L-band under winter frozen conditions and the dominant scattering contribution comes from the underlying soil layers. With the low dielectric value of the frozen soil, the backscatter from the frozen case is generally Fig. 2. Two-layer RT model simulations of PALSAR L-band (1.27-GHz) (a) VV radar backscatter under nonfrozen (Thaw), (b) HH radar backscatter under nonfrozen (Thaw), (c) VV radar backscatter under frozen, and (d) HH radar backscatter under frozen temperature conditions for the A-1 tundra site; solid black rectangles and circles denote observed PALSAR backscatter at respective HH and VV polarizations; S1 and S2 denote estimated direct backscattering from the surface of the (vegetation) first and (soil) second model layers, respectively; Vol denotes the estimated volume scattering inside the first layer.

8 DU et al.: CLASSIFYING ALASKA SPRING THAW CHARACTERISTICS 549 TABLE V RT MODEL (MIMICS) INPUT PARAMETERS FOR THE SMITH 1 ALASKA BOREAL FOREST Fig. 3. Comparisons between PALSAR HH-polarized measurements and model simulations for the Smith1 boreal forest site for year 2007 [(circles) PALSAR measurements under classified thawed conditions; (rectangles) PALSAR measurements under classified frozen conditions; (solid line) simulations for the thawed state; (dotted line) simulations for the frozen state]. lower than that of the thaw case, with similar results for HH- and VV-polarized backscatter simulations. B. Model Simulation Results for Boreal Forest Previous MIMICS-based radar backscatter modeling studies have been conducted for Alaska boreal forest, and the model was generally successful in simulating the forest backscatter response under frozen and nonfrozen conditions relative to in situ measurements of L-band surface dielectric properties and airborne SAR backscatter retrievals [49]. To analyze the performance of MIMICS in capturing PALSAR HH-polarized signals for Alaska boreal forest, a comparison between the MIMICS model simulations and PALSAR observations was made for the Smith 1 (see Table I) boreal forest reference site. The model input parameters (see Table V) were selected from Table III as the best fit between the MIMICS model simulations and PALSAR L-band radar backscatter observations for this site location and the 2007 sensor record. The comparison results were plotted in Fig. 3, where the circles and squares represented PALSAR measurements under respective nonfrozen and frozen soil conditions indicated from the in situ surface (2.5-cm depth) soil temperature measurements. For the Smith 1 site, soil FT conditions determined from the in situ soil temperature measurements are generally consistent with FT conditions determined from the colocated station air temperature measurements during the PALSAR overpasses. These results indicate that the PALSAR measurements are similar to the MIMICS model estimated forest backscatter for frozen and thawed conditions, with associated RMSD values of 1.06 db. The RMSD is also less than half of the theoretically predicted backscatter dynamic range between frozen and nonfrozen conditions, which is about Fig. 4. MIMICS RT model simulated scattering elements and total radar backscatter for the Smith 1 boreal forest site under (a) thawed and (b) frozen conditions. 3.0 db, and indicates a relatively large signal-to-noise for the FT retrieval. The MIMICS estimated contribution to the total forest radar backscatter signal from the three dominant L-band scattering mechanisms is presented in Fig. 4, including trunk ground interactions, direct crown backscattering and direct ground backscattering. For the 40 incident angle, which is the planned incident angle of the SMAP sensor, trunk ground interactions provide the dominant boreal forest radar backscatter contribution for both frozen and nonfrozen conditions. The simulation results also agree with previous findings [49] indicating that direct backscattering from the crown or interactions between the trunk and ground are the dominant L-band scattering mechanisms for mature Alaska boreal forest, including black spruce (Picea mariana), white spruce (Picea glauca), and balsam poplar (Populus balsamifera) forest types. It is noted that the above simulation assumes a bare surface without considering the potential presence of understory vegetation or moss layers. As pointed out by [50], bistatic scattering characteristics of the boreal forest floor may deviate significantly from the scattering signatures of bare-soil surfaces. Moreover, for low biomass conditions, including a forest stand with sparse or young regrowth trees, direct surface backscatter can be dominant [51], [52]. For Alaska forest with a variety of tree species

9 550 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 53, NO. 1, JANUARY 2015 TABLE VI PALSAR DERIVED FT STATE BACKSCATTER (IN DECIBELS) THRESHOLDS DETERMINED AT 40 INCIDENCE ANGLE FOR DIFFERENT NLCD LAND-COVER TYPES [41]; SPATIAL SD OF THE DECIBEL THRESHOLDS IN EACH LAND-COVER CLASS AND THE PROPORTIONAL (IN PERCENTAGE) COVERAGE OF EACH CLASS WITHIN THE ALASKA DOMAIN ARE ALSO SHOWN Fig. 5. FT backscatter (in decibels) threshold map for PALSAR L-band radar backscatter observations at the 40 incidence angle over the Alaska domain. Areas in black denote open water bodies, missing PALSAR data, and other areas outside of the FT classification domain. and complex forest stand conditions, trunk ground interactions, direct crown backscatter, as well as direct backscatter from the forest floor could be important depending on the forest and soil parameters, such as forest geometric and physical characteristics and soil surface roughness and wetness. Additional factors, including forest understory and moss layer characteristics and snow cover, can also affect SAR backscatter but were not represented by the relatively simple modeling scheme used in this paper. The forward model simulations of L-band radar backscatter under frozen and nonfrozen conditions and varying incident angles compared favorably with independent PALSAR observations at the tundra and boreal forest test sites. The same model framework was then applied to analyze and interpret PALSAR radar backscatter dynamics and map FT status over the larger Alaska domain using the 2007 sensor record. C. FT Threshold Map for Alaska Based on the procedures described in the previous section, the backscatter reference curves and FT threshold values can be defined for each ScanSAR incident angle and for each pixel using the RT model simulation databases. For example, the spatial distribution of the FT threshold at 40 incident angle of the radar backscatter retrieval was mapped in Fig. 5. The FT threshold and its spatial standard deviation (SD) for each Alaska NLCD land-cover class were summarized in Table VI. The estimated FT threshold values show large spatial variability consistent with regional land-cover heterogeneity (see Fig. 1), whereas forested areas have generally higher thresholds than the other land-cover types. This is expected since the RT model simulations indicate that L-band volume scattering from the forest crown layer and scattering interactions between stem and ground elements are dominant components of the radar backscatter signal for both frozen and thawed conditions. Lower biomass land-cover areas show a lower estimated FT threshold since the relatively low dielectric value of frozen vegetation and soil conditions results in a lower L-band radar backscatter signal. The observed spatial variability (SD) in FT thresholds (see Table VI) within individual land-cover classes reflects the diversity of underlying vegetation structural characteristics, terrain, microclimate, and soil conditions. However, the threshold differences were generally larger between different land-cover types than within individual vegetation classes. These results also indicate that grid cells having the same vegetation type and similar vegetation properties (e.g., biomass) and soil parameters (e.g., soil texture and surface roughness) are likely to have similar FT threshold values. D. Alaska FT Classification and Validation We applied the STA classification algorithm represented in (1) and the estimated FT threshold map to classify Alaska FT conditions from the PALSAR radar backscatter monthly time series. The PALSAR data record used for the regional FT classification extended from January to December 2007 and April to December 2010 with approximately 46-day temporal repeat observations. The maximum thawed area of the Alaska domain for April 2007 as determined from PALSAR was mapped in Fig. 6(a). In order to qualitatively assess the accuracy of the PALSAR FT classification map, the maximum thawed area inferred from MERRA daily maximum air temperatures was also plotted for the same period [see Fig. 6(b)]. The MERRA FT pixel was classified as thawed if the corresponding daily maximum air temperature was above 0 C during the PAL- SAR observation period. Both the MERRA and PALSAR FT maps show similar regional distributions of frozen and thawed areas, although the PALSAR results show much finer spatial

10 DU et al.: CLASSIFYING ALASKA SPRING THAW CHARACTERISTICS 551 Fig. 6. Comparisons between (a) the regional maximum thawed FT state maps derived from 100-m-resolution PALSAR retrievals and (b) coarser (1/2 2/3 ) MERRA maximum daily air temperatures for April 2007; blue and red areas denote respective frozen and nonfrozen conditions. heterogeneity in FT conditions. The April 2007 FT classification results show predominantly frozen conditions over the Alaska North Slope (> 68 N) tundra zone and nonfrozen conditions in boreal land areas south of the Brooks Range. The PALSAR results show a larger extent of frozen tundra relative to MERRA, with much greater spatial heterogeneity in FT conditions for higher elevations and complex terrain areas of the Brooks and Alaska ranges that are largely absent from the coarser scale MERRA results. The terrain effects on FT spatial heterogeneity are evident in the PALSAR results. Subregion 3 (see Fig. 1), for example, was classified as entirely thawed in the MERRA FT map [see Fig. 6(b)], whereas the corresponding PALSAR FT classification showed residual frozen areas encompassing 8.9% of this region [see Fig. 6(a)]. The percentage of PALSAR estimated frozen area showed a positive correlation (i.e., R = 0.53 and p < 0.05) with mean elevation within 25-km pixels over this subregion. The PALSAR results also captured predominantly frozen tundra conditions over the Seward Peninsula, which was much less extensive in the MERRA results. Quantitative validation of the PALSAR FT classification time series was also carried out for the 30 temperature station sites (see Fig. 1 and Table I) for years 2007 and 2010 relative to sitelevel FT conditions estimated from the in situ daily average air and soil temperature measurements. The in situ surface soil temperatures used to infer site soil FT characteristics were measured at variable soil depths for each station location but were within 10 cm of the soil surface (see Table VII). These results indicate mean PALSAR FT classification accuracy values of 82.2% and 79.6% for 2007 and 2010, respectively, when compared with in situ air temperature derived FT conditions. The PALSAR FT classification accuracy is reduced by approximately 6% 75.9% and 73.8% for 2007 and 2010, respectively, when evaluated against the in situ soil temperature measurements. When comparing against air temperature derived FT conditions, the boreal forest (evergreen and deciduous forest) sites have the best classification accuracy (87.6% and 81.2% for 2007 and 2010, respectively). The tundra shrubland areas also show favorable classification accuracy (82.8% and 80.0% for 2007 and 2010). The classification accuracy is also lower in relation to the in situ soil temperature observations for the boreal forest (79.5% and 71.5% for 2007 and 2010) and tundra shrub (75.8% and 77.4% for 2007 and 2010) sites. The RT model simulations (see Sections IV-A and IV-B) indicate that major backscatter contributions to the PALSAR FT signal could be from an intervening wet layer (either wet moss layer or wet snow in the spring) overlying the soil or from the tree canopy. Either of these two situations could cause the PALSAR FT classification to agree better with the air temperature than soil temperature-based FT conditions. In addition, the Ward Farm site showed the lowest FT classification accuracy and is characterized as barren land. The barren land-cover class also showed the lowest FT classification reliability relative to the vegetated land-cover classes, as described in the following sections. E. Classification Reliability The STA FT classification is sensitive to the reliability of estimated threshold values, which may be influenced by RT model simulation errors and uncertainties associated with land surface parameters that may vary with vegetation structure, terrain, and other environmental factors, including precipitation. To assess the reliability of the STA classification method for PALSAR data, the RMSD between the theoretical FT reference curve and the 2007 PALSAR observations was determined and evaluated for each pixel within the Alaska study

11 552 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 53, NO. 1, JANUARY 2015 TABLE VII VALIDATION OF PALSAR FT DETECTION ACCURACY AGAINST IN SITU STATION AIR TEMPERATURE (TA) AND SOIL TEMPERATURE (TS) STATION NETWORK MEASUREMENTS FOR PALSAR OBSERVATION PERIODS IN 2007 AND 2010 domain. The pixel-based RMSDs are denoted as ε th and ε fr for respective thawed and frozen state conditions. Assuming that the differences between the PALSAR observations and the FT reference values are normally distributed, the possibility of PALSAR backscatter σ(θ) > (σ th (θ) ε th ) or σ(θ) < (σ fr (θ)+ε fr ) will be 84.15%. This expected performance level is consistent with the observed validation results. Therefore, the STA FT classification using PALSAR observations at incident angle θ may be regarded as reliable if the following criteria are met: (σ th (θ) ε th ) >T(θ) (σ fr (θ)+ε fr ) <T(θ) (3) since most of the radar backscatter observations for a given pixel are expected to be classified into the correct FT category by threshold T (θ). The relative proportions of pixels within each NLCD landcover class indicating reliable STA classification performance for PALSAR observations at the 40 sensor incident angle are summarized in Table VIII. These results indicate that the PALSAR-driven STA classification approach can be applied to most Alaska land-cover areas with 70% 90% of the pixels producing a reliable FT classification. However, the algorithm performance is much lower over barren land areas, which may reflect large spatial variability in the radar backscatter response to surface wetness, soil structure, and microtopographic heterogeneity. For vegetated areas, land surface heterogeneity effects are partially obscured by the overlying vegetation cover, where the FT signal is dominated by large dielectric changes in the vegetation and surface soil layers. These results also imply that the seasonal transition of the landscape between predominant frozen and nonfrozen states dominates the PALSAR backscatter seasonal signature over Alaska vegetated areas. Wet snow can also affect radar backscatter and FT classification accuracy but was not addressed in this paper. As discussed in [53], increased water content in surface snow cover causes a larger dielectric difference between snow and air and also stronger microwave absorption within the snow layer. Therefore, surface scattering from the snow air interface is increased

12 DU et al.: CLASSIFYING ALASKA SPRING THAW CHARACTERISTICS 553 TABLE VIII AREAL PROPORTION OF MAJOR NLCD LAND-COVER TYPES [41] WITH ESTIMATED RELIABLE FT DETECTION FOR L-BAND RADAR BACKSCATTER OBSERVATIONS AT 40 INCIDENCE ANGLE WITHIN THE ALASKA DOMAIN Fig. 7. Relationships between the pixel size of the FT classification image and the corresponding relative spatial classification error for the three selected subregions delimited in Fig. 1; the X-axis is the pixel size in logarithmic scale and the Y -axis is the spatial classification error relative to the 100-m baseline for the selected subregions. with snow wetness while the radar backscatter contribution from underlying snow and ground layers is reduced. The final effect of wet snow on the magnitude of radar backscatter can be either positive or negative. For C- and X-band wavelengths, which are strongly affected by volume scattering from snow, the radar backscatter from wet snow is generally lower than from dry snow [54]. For L-band, ground-based scatterometer measurements and backscatter simulations for wet snow have shown weak sensitivity of radar backscatter to snow wetness with less than 1-dB difference for snow with volumetric water content between 0.19% and 4.3% [55]. In the case similar to [55], the impacts of snow wetness on the FT classification would be small. Further field and modeling studies considering an additional wet snow layer are needed for a more thorough evaluation on the effect of wet snow. F. Scaling Effects To evaluate the impacts of relative sensor footprint size on FT classification accuracy, we averaged the baseline 100-mresolution pixels of the PALSAR FT results into progressively coarser resolution pixels over the three selected subregions (see Fig. 1). The coarser pixel with proportional frozen area above or below 50% was classified as frozen or thawed, respectively. The resulting proportional frozen or thawed area calculations at each pixel resolution were then compared back to the baseline 100-m-resolution FT classification to quantify the relative change in spatial classification accuracy. Among the three subregions, Subregion 1 is predominantly frozen, whereas Subregion 3 is predominantly thawed. Subregion 2, which extends over complex mountain terrain, is a mixed frozen/nonfrozen region and demonstrates a complex FT spatial distribution. As shown in Fig. 7, the results of the scaling investigation indicate that the relationship between relative footprint size and FT spatial classification error follows a general logarithmic function (see Fig. 7). For all three subregions, a significant increase in FT classification error is observed at coarser footprint sizes in relation to the 100-m-resolution baseline FT classification, ranging from 1.9% at 200-m resolution to approximately 3.6% at 3-km resolution for Subregion 1, from 6.8% to 12.8% for Subregion 2, and from 5.0% to 8.7% for Subregion 3. The increase in pixel size and associated FT classification error coincides with an associated drop in the estimated proportional frozen or thawed area for each subregion; the estimated frozen area decreases from 3.9% (100-m resolution) to 0.3% (3-km resolution) for Subregion 1 and from 23.1% (100 m) to 10.3% (3 km) for Subregion 2; the estimated nonfrozen area decreases from 8.9% (100 m) to 0.2% (3 km) for Subregion 3. The relative change in FT spatial classification accuracy is fairly stable at coarser spatial footprints beyond approximately 1 3 km for Subregion 1 and Subregion 3. However, the relative FT spatial classification accuracy continues to degrade up to approximately 7 10-km resolution over complex terrain conditions represented by Subregion 2. Subregion 2 shows a significant accuracy dropoff from 100- to 200-m resolution, corresponding to respective proportional frozen area estimates of 23.1% and 16.3%; the estimated frozen area proportion decreases to 8.4% at 7-km resolution and becomes relatively stable at coarser spatial scales. Overall, the increase in FT spatial classification error from 100 m to 3 km, and coarser spatial scales is less than 12.8% and 16.4%, respectively, whereas scaling error is generally greater over complex terrain conditions represented by Subregion 2. By comparing the curves of the three subregions (see Fig. 7), the shape of the logarithmic scaling function shows dependence on FT spatial heterogeneity. We note, however, that these results may be sensitive to the scaling methodology; an alternative approach of spatially averaging the radar backscatter and then applying the FT classifications on the coarser resolution backscatter data may produce different results. Although the relatively coarse temporal fidelity of the PALSAR observation record constrains our ability to evaluate temporal changes in the scaling curves, these results imply that the relative decrease in FT spatial classification accuracy at coarser sampling footprints is likely to become sharper during active seasonal FT transition periods in spring and autumn when FT spatial heterogeneity is maximized and is flatter under more homogeneous conditions in summer and winter. V. S UMMARY AND CONCLUSION This investigation led to the development of an STA FT detection algorithm based on theoretical radar backscatter model simulations of L-band PALSAR ScanSAR observations over

13 554 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 53, NO. 1, JANUARY 2015 Alaska. The RT model simulations were used with 2007 and 2010 PALSAR data records to generate a 100-m-resolution regional FT classification map. These results were compared with surrogate FT estimates inferred from relatively coarse MERRA reanalysis surface air temperature records and validated against other FT estimates determined from in situ soil and air temperature network measurements. An FT scaling analysis was also applied to determine the relative increase in FT spatial classification error expected from coarser spatial scale pixels and relative to the 100-m-resolution baseline classification. These results clarify the potential utility of similar L-band radar FT classifications from the NASA SMAP mission, which will provide global FT mapping capabilities at approximately 3-km spatial resolution [29]. The following conclusions can be drawn from this paper. 1) The L-band radar backscatter simulations indicate that the dominant landscape FT signal can come from either surface soil or vegetation layers. Over dense vegetation, such as the boreal forest, the major scattering elements come from trunk ground interactions and direct backscattering from the crown layer. For lower vegetation biomass areas such as tundra, most radar backscattering may also come from wet vegetation cover. Under the above circumstances, the FT state of the surface soil layer may only be indirectly detected. Lower frequency (e.g., P-band) observations may be required for more direct observations of soil active layer FT processes. 2) Based on the seasonal threshold (STA) classification method and the RT simulated backscatter databases, the L-band FT spatial classification accuracy from 100-mresolution PALSAR observations is approximately 80% and 75% relative to in situ air and soil temperature network measurements, respectively. The resulting FT classification accuracy is similar to the targeted accuracy requirements of the planned SMAP operational FT product [43]. The statistical analysis from this investigation also indicates that the STA method using a limited number of PALSAR observations can be applied over most Alaska land-cover types with 70% 90% of the pixels showing a reliable FT classification. However, the reliability of the STA classification method is lower for bare-soil conditions, which represented approximately 7.7% of the Alaska PALSAR FT classification domain. Considering the highly variable soil moisture conditions during the thaw seasons but relatively stable dielectric properties of frozen soil, the reference freeze threshold curve along with an alternative moving-window FT classification method may provide improved classification accuracy in bare-soil regions. 3) The range of the threshold values for judging FT state varies with land-cover type. The threshold values also vary within individual land-cover classes and reflect the spatial diversity of vegetation structure and soil conditions. In addition to general land-cover-type information, additional ancillary data on vegetation structure, soil parameters, and terrain may enhance landscape delineation of a threshold database and improve the resulting FT classification accuracy. 4) The spatial scaling analysis indicates that the relationship between pixel size and relative FT spatial classification error follows a general logarithmic function with marked accuracy improvement for finer spatial resolution pixels. The optimum resolution for an accurate FT classification is expected to depend on the landscape spatial heterogeneity of the FT distribution. However, the results of this investigation indicate that the regional FT spatial scaling error is less than 12.8% and 16.4% at respective 3- and 25-km-pixel scales relative to the baseline 100-m FT classification results. The RT backscatter simulations and resulting threshold database and STA classification may inform prelaunch algorithm calibration and initialization activities for the SMAP FT product [43]. In addition to the 3-km resolution of the planned SMAP L-band FT product, other global FT products are currently available at coarser ( 25 km) spatial resolution determined from available spaceborne passive microwave sensors and scatterometers [3], [30]. This investigation provides an initial step toward assessing linkages among these potentially synergistic retrievals as a means for generating spatially and temporally consistent global FT Earth system data records. The results of this paper also indicate that effective FT retrievals sensitive to both surface air and soil conditions are attainable using moderate ( 3 km) resolution satellite L-band radar backscatter measurements from SMAP. The apparent loss of FT spatial classification accuracy is significant moving from 100-m baseline to 3-km-pixel resolutions but generally within the planned performance and accuracy requirements anticipated for the SMAP operational FT product [29]. Further research should include a more sophisticated modeling scheme accounting for dry/wet snow cover and forest understory effects on radar backscatter and resulting FT classification accuracy and sensitivity and also more extensive validation on the RT models using independent data sets. For the FT classification developed using the NLCD land-cover map, possible NLCD land-cover errors can be propagated to the resulting FT classification; therefore, land-cover maps with higher accuracy may also improve FT classification accuracy. Moreover, an alternative approach to study scaling effects based on spatial aggregation of backscatter prior to the FT classification should be also explored. 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Borr, Soil moisture limitations on monitoring boreal forest regrowth using spaceborne L-band SAR data, Remote Sens. Environ., vol. 115, pp , [53] J. Shi and J. Dozier, Inferring snow wetness using C-band data from SIR-C s polarimetric synthetic aperture radar, IEEE Trans. Geosci. Remote Sens., vol. 33, no. 4, pp , Jul [54] T. Nagler and H. Rott, Retrieval of wet snow by means of multitemporal SAR data, IEEE Trans. Geosci. Remote Sens,vol.38,no.2,pp , Mar [55] W. H. Stiles and F. T. Ulaby, The active and passive microwave response to snow parameters: 1. Wetness, J. Geophys. Res., Oceans, vol. 85, no. C2, pp , Jan Jinyang Du (M 10) received the Ph.D. degree in geographic information systems and cartography from the Chinese Academy of Sciences, Beijing, China, in He is currently a Research Scientist with Flathead Lake Biological Station, University of Montana, Polson, MT, USA, and the Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT, USA. From 2006 to 2007, he was a Visiting Scientist with the Hydrology and Remote Sensing Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD, USA. His research interests include microwave modeling of vegetation, soil and snow signatures, and inversion models for retrieving land surface parameters from remote sensing data. R. Scott Dunbar received the B.S. degree in physics and astronomy from the University at Albany, State University of New York, Albany, NY, USA, in 1976 and the Ph.D. degree in physics from Princeton University, Princeton, NJ, USA, in Since 1981, he has been with the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA. Over the last 30 years, he has contributed to the development of science algorithms for the NASA Scatterometer and SeaWinds ocean vector wind scatterometer projects, and since 2009, he has worked on Soil Moisture Active and Passive soil moisture and freeze thaw algorithm development. Mahta Moghaddam (S 86 M 87 SM 02 F 08) received the B.S. degree (with highest distinction) from The University of Kansas, Lawrence, KS, USA, in 1986 and the M.S. and Ph.D. degrees from the University of Illinois at Urbana-Champaign, Champaign, IL, USA, in 1989 and 1991, respectively, all in electrical and computer engineering. She is currently a Professor of electrical engineering with the University of Southern California, Los Angeles, CA, USA. Prior to that, she was with the University of Michigan, Ann Arbor, MI, USA, ( ) and NASA Jet Propulsion Laboratory (JPL), California Institute of Technology, Pasadena, CA, USA ( ). She was a Systems Engineer for Cassini Radar. She has introduced new approaches for quantitative interpretation of multichannel radar imagery based on analytical inverse scattering techniques applied to complex and random media. Her most recent research interests include the development of new radar instrument and measurement technologies for subsurface and subcanopy characterization, the development of forward and inverse scattering techniques for layered random media particularly for soil moisture applications, and transforming concepts of radar remote sensing to near-field and medical imaging. Prof. Moghaddam served as the Science Chair of the JPL Team X (Advanced Mission Studies Team). She is a member of the NASA advisory Council Earth Science Subcommittee and of the Soil Moisture Active and Passive mission Science Team. She is the Principal Investigator of the AirMOSS NASA Earth Ventures mission. John S. Kimball (M 07) received the Ph.D. degree in bioresource engineering and geosciences from Oregon State University, Corvallis, OR, USA, in He is currently a Professor of systems ecology with the University of Montana, Missoula, MT, USA. He is also with Flathead Lake Biological Station, University of Montana, Polson, MT, USA. He is a member of the NASA Soil Moisture Active Passive mission and EOS MODIS and AMSR-E Science Teams and is working toward improved measurement and monitoring of global carbon and water cycles through synergistic use of biophysical process models and satellite remote sensing. His research interests include integration of ecological theory with satellite remote sensing for better understanding terrestrial ecosystem structure and function, from single plot to global scales. Marzieh Azarderakhsh received the Ph.D. degree in 2012, where she worked on various remotesensing-based data sets over large tropical basins.she is currently a Research Associate with the Department of Earth and Atmospheric Sciences, The City College of New York, New York, NY, USA. Her research ionterests include the use of remote sensing data to study hydrological cycle and environmental dynamics and the use of SAR data to better understand hydrological cycle and land surface processes. Kyle C. McDonald (SM 98) received the Bachelor of Electrical Engineering degree (cooperative plan with highest honors) from the Georgia Institute of Technology, Atlanta, GA, USA, in 1983; the M.S. degree in numerical science from The Johns Hopkins University, Baltimore, MD, USA, in 1985; and the M.S. and Ph.D. degrees in electrical engineering from the University of Michigan, Ann Arbor, MI, USA, in 1986 and 1991, respectively. Since 1991, he has been with the Science Division, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA, where he is currently a Research Scientist with the Water and Carbon Cycles Group. He is also a professor with the City College of New York. He specializes in electromagnetic scattering and propagation, with emphasis on microwave remote sensing of terrestrial ecosystems. His research interests have primarily involved the application of microwave remote sensing techniques for monitoring seasonal dynamics in boreal ecosystems, as related to ecological and hydrological processes and the global carbon and water cycles. He has been a Principal and Co-Investigator on numerous NASA Earth Science investigations. He is a member of NASA s North American Carbon Program Science Team, NSF s Pan-Arctic Community-wide Hydrological Analysis and Monitoring Program (Arctic-CHAMP) Science Steering Committee, and the ALOS PALSAR Kyoto & Carbon Initiative science panel. He has been a member of the NASA BOREAS and BOREAS Follow-on science teams, the NASA Scatterometer instrument team, the NASA Ocean Vector Winds Science Team, and the NASA Cold Land Processes Steering Committee.