The NASA ISRO Synthetic Aperture Radar (NISAR) is scheduled for launch early in 2024 from the Satish Dhawan Space Centre (SDSC), at Sriharikota, near Chennai, India. This mission is the result of a collaboration between NASA and Indian Space Research Organization (ISRO), where NASA has contributed elements of the mission such as an L-band SAR, and ISRO has contributed other elements, such as an S-band SAR. After successful launch, the NISAR mission will collect left-looking L-band SAR data over most of the Earth’s land areas twice during every 12-day exact repeat orbit. (once while in an ascending orbit direction and once while in a descending orbit direction). NASA and ISRO have individual and joint requirements on the mission that include the performance of the imaging radars onboard the spacecraft. For example, NASA must demonstrate that this L-band SAR will achieve a set of identified science measurement accuracy requirements that span Ecosystem science, Solid Earth science, and Cryosphere science disciplines. Likewise, ISRO has several applications objectives on both the L-band and S-band data from NISAR that the ISRO science team and project will be developing and testing. Pre-launch and post-launch activities have been planned to validate that these requirements are met. Here, we will discuss how the NASA plans are being executed and will present any initial results at the conference.

In preparation for the NISAR launch and data collection in 2024, the NISAR Project Science Team is building workflows for each Science Team discipline (Ecosystems, Cryosphere, and Solid Earth). This abstract focuses on the Ecosystem disciplines and the development of on-demand cloud-processing workflows for wetlands inundation, forest biomass, agricultural active crop area, and forest disturbance. The workflow simulates NISAR data using UAVSAR or ALOS-2 Single Look Complex data, which are processed to Level 2 geocoded polarimetric covariance matrix products using InSAR Scientific Computing Environment 3.0 software and to Level 3 science products using the Algorithm Theoretical Basis Documents. In this presentation, we describe these workflows and efforts to improve efficiency and data accessibility by using a cloud processing system. We present preliminary sample products from each Ecosystem discipline: inundation, forest biomass, crop area, and forest disturbance..

Globally, wetlands are a critical habitat of numerous plants and animal species and play a major role in maintaining Earth’s biodiversity. Wetlands ecosystems are particularly challenging to characterize and monitor because many tend to be in remote regions that are difficult to access. As optical satellite remote sensing instruments observe only the top of the canopy, they are limited in they cannot directly observe surface inundation beneath vegetation canopies. The NISAR mission is tasked with mapping surface inundation in wetlands. NISAR’s unique capability enables consistent and reliable observations of wetlands inundation extent and dynamics. This capability supports characterization of changes in wetlands systems driven by a changing climate and will support a better understanding of the impacts to carbon fluxes, ecosystems services and biodiversity in these critical systems.Focusing on our efforts in Pacaya Samiria Nation Reserve, Peru, we provide an overview of NISAR’s capability to study wetlands ecosystems. Pacaya Samiria has been selected to be the NISAR calibration-validation site for tropical wetlands. Here, we have established a network of in situ sensors for validation of inundation state and extent in a complex tropical wetlands environment. Our sensor network is supported by ancillary measurements of vegetation structure drawn from drone and ground LiDAR and photogrammetry. We also describe the calibration validation approach that will be implemented as part of supporting the NISAR objectives, and we present preliminary results using ALOS PALSAR-2 data and possibly first light images from NISAR.

The NISAR mission which in its most recent round of launch preparations was set to launch in the spring of 2024, and now delayed until later in the fall or early spring of 2025, will serve as an unprecedented resource for the Remote Sensing of Ecosystems Science community. The two frequency, L- and S-band will full-polarimetric capability over a 250 km wide swath using the SweepSAR technique [1] will collect reliable set of observations (60 per year; 30 each for ascending and descending passes) on a continuing basis that will allow for the modeling and observation of time-varying processes that are prevalent in the living environment broadly described as Ecosystems. Among the prime science goals of the NISAR Ecosystems disciplines are in the characterization of agriculture, disturbance, biomass, forest structure and water dynamics seen in the world’s rivers, coasts, and permafrost regions. In this paper we provide an overview of the Ecosystem science that will be enabled by the NISAR mission and give a status of the basic algorithms that are being used to provide a basic set of tools to the community to make use of the data that NISAR will provide.

L-band FMCW radar is implemented for monitoring forest dynamics. It took short-term and long-term measurements with an internal calibration system that guarantees stability and precision. The radar data is compared to in-situ measurement, which infers causal relationships between radar backscatter signal and forest physiology index such as tree dielectric. This paper explains the relationship between radar signals and environmental components such as precipitation based on the measurement. The radar demonstrates some interesting observations, for example, trees’ diurnal activity and freeze-thaw process.

The NISAR mission, an L-band and S-band 12-day repeat-pass InSAR, currently scheduled to be launched in January 2023, will serve as an unprecedented resource for the Remote Sensing of Ecosystems Science community. The reliable set of 30 ascending and 30 descending 250 km swath of observations on a continuing basis will allow for the modeling and observation of hydrologic processes that serves as a forcing function and mode of energy transport for most living things, as well as changes in the landcover that are associated with agriculture, river and coastal dynamics, and disturbance. In this paper we provide an overview of the Ecosystem science that will be enabled by the NISAR mission and give a status of the basic algorithms that are being used to provide a basic set of tools to the community to make use of the data that NISAR will provide.

This study presents a preliminary assessment of vegetation and inundation characteristics in the Amazon basin using ALOS-2 PALSAR-2 L-band SAR and Sentinel-1 C-band SAR backscatter combined with ICESat-2 Lidar photon height data. Key Findings: -Photon height from ICESat-2 found useful to capture seasonal water level variations along major rivers and open water areas. -Comparisons between Lidar photon height and L- and C-band backscatter along forest-river transects show good agreement for the wet and dry seasons.

This letter summarizes substantial modifications made to the Surface Water Microwave Product Series (SWAMPS), a coarse-resolution (~25 km) global inundated area fraction data record derived from active and passive microwave remote sensing. SWAMPS is the most temporally dense, long-term record of global surface water dynamics publicly available today. This update improves upon the original release by: 1) incorporating a customized, consistent resampling and assembly of the Special Sensor Microwave Imager and Special Sensor Microwave Imager Sounder brightness temperature record; 2) eliminating signal contamination from ocean waters along coastlines; 3) inclusion of permanent surface waters as a component of the data record; and 4) reducing anomalous inundation retrievals over arid and semiarid regions. This update provides for the enhanced scientific utility of the full 25+ years of data records. Remaining uncertainties in the surface water fraction retrievals are principally in areas with bare, sandy surface cover and in areas with dense vegetation cover that diminishes radiometric sensitivity to surface water. This data record and associated documentation are freely available through the Alaska Satellite Facility, Fairbanks, AK, USA.

The states of the earth surface in terms of freeze and thaw (FT) cycles especially in high-latitude regions have a crucial role in many applications such as biogeochemical transitions, hydrology and ecosystem studies. This study uses Synthetic Aperture Radar (SAR) c-band backscatter data from Sentinel 1 from April 2014 to December 2017 to detect high-resolution freeze/thaw states in Alaska. The contrasts between frozen and thawed seasons are used to define FT states. Comprehensive in situ observations of soil temperature, air temperature, snow depth, and soil moisture were obtained to develop appropriate sigma (dB) thresholds of backscattering between freeze and thaw states. The developed thresholds were used to detect FT in Alaska, USA. The results of this method revealed that the estimates are reasonably able to detect the states of surface when compared with ground measurements from SNOw TELemetry (SNOTEL) observations. The developed method that mainly relies on using ground measurements from different land cover type shows an improvement with respect to previous methods that had used the average of frozen and thawed backscattering scenarios as FT references.

Permanently frozen and seasonally frozen soils occur over a large portion of the Earth's land surface. Changes in the freeze/thaw state of the land surface reflects major changes in thermal and hydraulic properties as well as acting as a “switch” for many ecological processes. In short, soil freeze/thaw state is a fundamental land surface variable in the water and energy cycles, and it connects to the carbon cycle. Surface freeze/thaw state is observable by passive and active microwave sensors. For example, NASA's Soil Moisture Active Passive (SMAP) mission includes a freeze/thaw data product. Such satellite sensing offers routine all-season and all-weather global observations of soil freeze/thaw state with the application of suitable algorithms. We describe early finding from the SLAPex Freeze/Thaw campaign, believed to be the first airborne campaign of its type, focusing on soil freeze/thaw.

"Dividual Plays Experimental Lab" is an extract from the dance piece "Dividual Plays". Dividual Plays was produced as the first research outcome of "Reactor for Awareness in Motion [RAM]", a research project we have been involved since 2010 (http://ram.ycam.jp/en/). Dividual Plays Experimental Lab consists of essential elements of Dividual Plays, virtual environments for dance "scenes", a programming toolkit "RAM Dance Toolkit", and a motion capture system "MOTIONER". With these systems, the lab allows the visitors to explore and create their own body movements correspond with the experience of the dancers in Dividual Plays.

Ground-based passive microwave observations at 18.7- and 36.5-GHz frequencies and active microwave observations in L- [1.4 GHz] and Ku- [15.5 GHz] bands are used within an ensemble-based data assimilation (DA) framework to characterize the snow water equivalent (SWE) and the underlying soil freeze-thaw state (including soil surface temperature and both soil ice/liquid water content). The proposed framework is tested at the local-scale observation site of the National Aeronautics and Space Administration (NASA) Cold Land Processes Experiment field campaign during the third intensive observation period (February 18-26, 2003) for which the best set of collocated ground-based passive/active microwave observations, SWE, soil surface temperature, and moisture measurements are available. The DA approach effectively merges an a priori estimate of the soil freeze-thaw state and SWE generated by a land surface model (LSM) with information contained in passive/active microwave observations in order to overcome errors in the forcing data of LSM. Results indicate that the root-mean-square errors of SWE, soil surface temperature, and soil ice+liquid water content after the assimilation of passive (active) observations respectively decrease to 25.4 mm (22.8 mm), 0.61 K (0.52 K), and 0.063 (0.057) from 90.55 mm, 2.17 K, and 0.13 before assimilation, resulting in improvements of 75% (77%), 72% (76%), and 51% (56%). Also, it is found that the simultaneous assimilation of passive and active measurements further improves the estimates of SWE and soil temperature as well as soil ice/liquid water content, suggesting that there is an advantage offered by the synergistic use of passive and active measurements. Overall, the findings show that future studies can take advantage of remotely sensed microwave passive and active measurements from present and upcoming satellites such as Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), Soil Moisture Active Passive, and COld REgion Hydrology High-resolution Observatory (CoReH2O) for monitoring SWE and the underlying soil freeze-thaw state.

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 two and 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.

Northern peatlands are estimated to hold about 30 % of the total global pool of soil carbon or 13 % of the total terrestrial carbon in the biosphere [1]. The warmer, drier conditions being experienced throughout the Arctic appear to be accelerating both aerobic and anaerobic decomposition of northern peatland soils, thereby increasing emissions of methane (CH4) and carbon dioxide (CO2) [2]. If continued, this trend could cause northern peatlands to become major sources of atmospheric carbon, with existing models predicting large increases in CH4emissions as CO2levels continue to rise [3]. To better understand sources, sinks, and net fluxes of atmospheric CO2and CH4validated high-resolution maps of the extent and distribution of northern wetlands are needed [4].

Seasonal terrestrial Freeze/Thaw cycle in Northern Quebec Tundra (Nunavik) determined and evaluated with Passive and Active Microwave Observations. SMOS time series data were analyzed to examine seasonal variations of soil freezing, and to assess the impact of snow cover and land cover on freeze-thaw cycle. Further, the soil freezing maps derived from SMOS observations compared to microwave active images in the region near Umiujaq and Field survey data. The objective is to develop algorithms to follow the seasonal cycle of freezing and thawing of the soil in the Tundra and Boreal forest. Field data shows that freezing and thawing dates vary much spatially at the local scale in the Boreal Forest and the Tundra. Therefore, the field validation of the F/T state maps at the regional scale will be very important. Agreement Factor derived from comparison of SMOS FT maps with daily in-situ data shows low values which does not seems to be acceptable. New parameters such as lake and pond as well as vegetation type and height present on surface have to be introduced in the algorithm to obtain more realistic estimations.

The annual freeze/thaw (FT) cycle determines the potential growing season in boreal landscapes and is a major factor determining ecosystem productivity and associated exchange of trace gases (CO2, H2O) with the atmosphere. Accurate characterization of these processes can improve regional assessment of seasonal carbon dynamics and climate feedbacks. FT process variations are spatially and temporally complex due to topography, snow depth and wetness, land cover, or local climatic conditions. In this paper, we perform a landscape analysis of multifrequency and multitemporal satellite microwave remote sensing measurements at L-band (JERS-1), C-band (ERS), and Ku-band (QuikSCAT) for characterizing FT dynamics. We first analyze backscatter sensitivity of the three frequencies to FT conditions over selected Alaska temperature sites. We then apply an FT classifier over two study areas (wetland complex and moderate topography) and examine differences in FT timing according to vegetation, elevation, and north/south facing slope. Results show that L-, C-, and Ku-band backscatter are sensitive to landscape FT state transitions, with higher backscatter for nonfrozen than frozen conditions at C- and L-bands but the opposite response at Ku-band. We applied a change detection algorithm to the C-band and L-band data over both study areas and analyzed the FT classifications with land cover information. These results resolve characteristic patterns of earlier spring thawing for south facing slopes, lower elevations, and coniferous vegetation. Our results also inform similar FT algorithm development for the NASA Soil Moisture Active Passive mission by documenting L-band FT sensitivity and heterogeneity over a boreal landscape.

An ensemble-based data assimilation approach is developed to characterize the snow water equivalent (SWE) and underlying soil freeze–thaw state (including the soil surface temperature and both soil ice and liquid water content) using multifrequency passive and active microwave remote-sensing measurements. Its feasibility was examined using a synthetic test where passive microwave (1.4, 18.7, and 36.5 GHz) and active microwave [L-band (1.4 GHz), C-band (5.4 GHz), and Ku-band (12 GHz)] measurements at the point scale were individually and simultaneously assimilated to estimate the SWE and soil freeze–thaw state using an Ensemble Batch Smoother framework. The contribution of each channel in retrieving the true SWE, soil surface temperature, soil liquid water and ice content was investigated at the local-scale observation site of the National Aeronautics and Space Administration Cold Land Processes Experiments Field Campaign in northern Colorado during both the snow accumulation (Fall 2002–Winter 2003) and melt (Spring 2003) periods. All of the utilized passive and active measurements were found to contain valuable and complementary information for characterizing the SWE and freeze–thaw state of the underlying soil. L-band measurements were most effective for soil freeze–thaw state estimation, whereas higher frequencies were more effective at SWE characterization. In addition, results from the simultaneous assimilation of passive and active microwave data were compared to those from a modeling approach without assimilating microwave data (open loop). It was found that assimilating both passive and active microwave data decreased the errors that are associated with the open-loop approach. Finally, passive and active measurements were undersampled as expected from the overpasses of current and future satellite platforms. It was observed that the developed method can reliably estimate the soil freeze–thaw state and SWE, even with measurement sequences anticipated from the temporal frequency of existing and future satellites such as the Special Sensor Microwave/Imager, Soil Moisture Active Passive Mission, and Cold Regions Hydrology High-Resolution Observatory.

This study is conducted in the framework of the NASA SMAP mission. The frozen soil mapping would certainly be improved by using the future NASA SMAP instruments which include both a Radiometer and a SAR operating at L-band. SMAP will have the ability to sense the soil conditions through moderate land cover. The radiometric accuracy, the better spatial resolution 40 km passive and 3 km active, and the global coverage of SMAP will make possible the monitoring of the seasonal F/T cycle at a regional scale. The objective of this study is to develop algorithms to track the seasonally F/T over the Tundra and the Boreal Forest. The experimental site is located in Northern Quebec (Nunavik) in Canada. We use available SMOS data from Environment Canada and in situ temperature and soil moisture data measured in different environments of the tundra and taiga.

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.

The calibration and validation of the freeze/thaw product of NASA's proposed L-band SMAP (Soil Moisture Active and Passive) radar and radiometer mission requires execution of a strategy for characterization of thermal regime of the relevant landscape elements in terms of freeze/thaw state and the associated relationship to the microwave remote sensing signature. The goal of this study is to improve the understanding of the L-band radar backscatter processes over boreal landscapes by comparing ALOS PALSAR high resolution L-band backscatter images with Ku-band backscatter from the SeaWinds QuikSCAT scatterometer and C-, Xand Ka-band brightness temperatures from the Aqua AMSR-E radiometer. The results show that landscape elements driving the L-band backscatter are different from those at higher (Ku-band) frequencies and establishment of an optimal validation strategy for SMAP requires investigation of L-band measurements at spatial scales and temporal fidelity commensurate with landscape freeze/thaw variability.

The landscape freeze-thaw (F/T) state parameter derived from satellite microwave remote sensing is closely linked to the surface energy budget, hydrological activity, vegetation growing season dynamics, terrestrial carbon budgets, and land-atmosphere trace gas exchange. Satellite microwave remote sensing is well suited for global F/T monitoring due to its insensitivity to atmospheric contamination and solar illumination effects, and its strong sensitivity to the relationship between landscape dielectric properties and predominantly frozen and thawed conditions. We investigated the utility of multifrequency and dual polarization brightness temperature$(T_{b})$measurements from the Special Sensor Microwave Imager (SSM/I) to map global patterns and daily variations in terrestrial F/T cycles. We defined a global F/T classification domain by examining biophysical cold temperature constraints to vegetation growing seasons. We applied a temporal change classification algorithm based on a seasonal thresholding scheme to classify daily F/T states from time series$T_{b}$measurements. The SSM/I F/T classification accuracy was assessed using in situ air temperature measurements from the global WMO weather station network. A single-channel classification of 37 GHz, V-polarization$T_{b}$time series provided generally improved performance over other SSM/I frequencies, polarizations and channel combinations. Mean annual F/T classification accuracies were 92.2$\pm$0.8 [SD] % and 85.0$\pm$0.7 [SD] % for respective SSM/I time series of p.m. and a.m. orbital nodes over the global domain and a 20-year (1988–2007) satellite record. The resulting database provides a continuous and relatively long-term record of daily F/T dynamics for the global biosphere with well-defined accuracy.

The mapping of freeze/thaw state of the landscape is one of the main objectives of NASA's upcoming SMAP (Soil Moisture Active and Passive) mission. This study applies ALECTRA (Alaska Ecological Transect) biophysical network and QuikSCAT scatterometer data to evaluate some of the validation issues regarding the SMAP freeze/thaw measurements. Although the QuikSCAT data is at Ku-band frequency, rather than the L-band of the SMAP instrument, the data is utilized due to its uniquely high temporal resolution over the ALECTRA sites. The results show that multiple temperature measurements representative of individual landscape (soil, snow cover, vegetation and atmosphere) elements and spatial heterogeneity within the satellite field-of-view are important for understanding the radar backscatter process and aggregate freeze/thaw signal. The backscatter temporal dynamics and relative contribution of these landscape elements to the freeze-thaw signal varies with land cover type, seasonal weather and climate conditions.

We have been developing high-resolution thematic maps of wetlands throughout the North American boreal regions. We assemble a wetlands map for each region based on data collected during the late 1990s, then construct a second map based on data collected during the late 2000s. Comparison of the two maps then makes it possible to assess changes that have occurred over the course of the intervening decade..

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.

We developed an algorithm to estimate surface soil moisture, vegetation optical depth and fractional open water cover using satellite microwave radiometry. Soil moisture results compare favorably with a simple antecedent site precipitation index, and respond rapidly to precipitation events indicated by TRMM. High optical depth reduces soil moisture sensitivity in forests and croplands during peak biomass, although tundra locations maintain soil moisture sensitivity despite high optical depth. Optical depth varies with characteristic seasonality across vegetation cover types and tracks measures of vegetation canopy cover from MODIS. The algorithm developed in this study is able to monitor the daily variability of several important land surface state variables.

Previously, we have developed a robust algorithm for mapping boreal wetlands using L-band satellite radar imagery, and in particular have used the method to produce a complete vegetated wetlands map of Alaska using the JERS radar data. In this work, we apply this algorithm to produce a static map of Canadian wetlands from the 1997-98 era JERS radar data at 100-m resolution, to be followed in the future by 2007-era ALOS/PALSAR maps.

We have been developing a continental-scale map of the North American boreal wetlands based on L-Band SAR imagery collected in 1997-1998 by the Japanese Earth Resources Satellite (JERS). The map currently covers the entire state of Alaska, identifying up to nine wetlands classes and two uplands classes. We have also recently obtained and classified a region of L-Band SAR imagery collected in 2007 by the Advanced Land Observing Satellite (ALOS) Phased Array L-Band SAR (PALSAR). Herein, we compare the results of the PALSAR classification to those of the JERS classification in order to detect changes in wetlands type or extent during the decade-long interval between the two sets of SAR imagery.

Northern ecosystems are a major sink for atmospheric$\hbox{CO}_{2}$and contain much of the world's soil organic carbon (SOC) that is potentially reactive to near-term climate change. We introduce a simple terrestrial carbon flux (TCF) model driven by satellite remote sensing inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to estimate surface ($≪ 10$-cm depth) SOC stocks, daily respiration, and net ecosystem carbon exchange (NEE). Soil temperature and moisture information from AMSR-E provide environmental constraints to soil heterotrophic respiration$(R_{h})$, while gross primary production (GPP) information from MODIS provides estimates of the total photosynthesis and autotrophic respiration. The model results were evaluated across a North American network of boreal forest, grassland, and tundra monitoring sites using alternative carbon measures derived from tower$\hbox{CO}_{2}$flux measurements and BIOME-BGC model simulations. Root-mean-square-error (rmse) differences between TCF model estimates and tower observations were 1.2, 0.7, and 1.2$\hbox{g} \cdot \hbox{C} \cdot \hbox{m}^{-2} \cdot \hbox{day}^{-1}$for GPP, ecosystem respiration$({\rm R}_{\rm tot})$and NEE, while mean residual differences were 43% of the rmse. Similar accuracies were observed for both TCF and BIOME-BGC model simulations relative to tower results. TCF-model-derived SOC was in general agreement with soil inventory data and indicates that the dominant SOC source for$R_{h}$has a mean residence time of less than five years, while$R_{h}$is approximately 43% and 55% of$R_{\rm tot}$for respective summer and annual fluxes. An error sensitivity analysis determined that meaningful flux estimates could be derived under prevailing climatic conditions at the study locations, given documented error levels in the remote sensing inputs.

Field measurements of dielectric properties of hypersaline deposits were realized over an arid site located in Death Valley, CA. The dielectric constant of salt and water mixtures is usually high but can show large variations, depending on the considered salt. We confirmed values observed on the field with laboratory measurements and used these results to model both the amplitude and phase behaviors of the synthetic aperture radar (SAR) signal at C- and L-bands. Our analytical simulations allow reproducing specific copolar signatures observed in both Airborne SAR (AIRSAR) and Spaceborne Imaging Radar (SIR-C) data, corresponding to the saltpan of the Cottonball Basin. More precisely, the main objective of the present paper is to understand the influence of soil salinity as a function of soil moisture on the dielectric constant of soils and then on the backscattering coefficients recorded by airborne and spaceborne SAR systems. We also propose the copolarized backscattering ratio and phase difference as indicators of moistened and salt-affected soils. More precisely, we show that these copolar indicators should allow monitoring of the seasonal variations of the dielectric properties of saline deposits at both C- and L-bands. Because of the frequency dependence of the ionic conductivity, we also show that L-band SAR systems should be efficient tools for detecting both soil moisture and salinity, while C-band SAR systems are more suitable for the monitoring of soil moisture only. Through the study of terrestrial evaporitic environments by means of spaceborne SAR systems, our results could also be of great interest for defining future planetary missions, particularly for the exploration of Mars.

We consider the exploitation of dielectric properties of saline deposits for the detection and mapping of moisture in arid regions on both Earth and Mars. We present simulated and experimental study in order to assess the effect of salinity on the complex permittivity of geological materials and, therefore, on the radar backscattering coefficient in the [1–7 GHz] frequency range. Laboratory measurements are performed on sand/sodium chloride aqueous mixtures using a vectorial network analyzer coupled to an open-ended coaxial dielectric probe. We aim at calibrating and validating semiempirical dielectric mixing models. In particular, we evaluated the dependence of the real and imaginary parts of complex permittivity on the microwave frequency, water content, and salinity. Our results confirm that if the real part is mainly affected by the moisture content, the imaginary part is more sensitive to salinity. In addition to the classic formulas of mixing models, the ionic-conductivity losses, which are due to mobile ions in the saline solution, are taken into account in order to better assess the effect of salinity on the dielectric properties of mixtures. Dielectric mixing models are then used as input parameters for the simulation of the radar backscattering coefficients by means of an analytical model: the integral equation model. Simulation results show that salinity should have a significant impact on the radar backscattering recorded in synthetic aperture radar data in terms of the magnitude of the backscattering coefficient. Moreover, our results suggest that VV polarization provides a greater sensitivity to salinity than HH polarization.

This paper deals with the exploitation of dielectric properties of saline deposits for the detection and mapping of moisture in arid regions on both Earth and Mars. We then present a simulation and experimental study in order to assess the effect of salinity on the permittivity of geological materials and therefore on the radar backscattering coefficient in the [1-7 GHz] frequency range. Dielectric mixing models were first calibrated by means of experimental measurements before being used as input parameters of analytical scattering models (IEM, SPM). Simulation results will finally be compared to field measurements (Pyla dune, Death Valley, Mojave Desert) and will be used for the interpretation of SAR data (AIRSAR, PALSAR).

We have used two seasons of L-band SAR imagery to produce a thematic map of wetlands throughout Alaska. The classification was developed using the Random Forests statistical decision tree algorithm. Input data included mosaics of summer and winter JERS-1 SAR imagery with associated image collection dates, summer and winter SAR backscatter texture, elevation, slope, proximity to water, and geographic latitude. The accuracy of the resulting thematic map was quantified using extensive ground reference data. The overall aggregate accuracy calculated based on all classified pixels was 89.5%, with individual per-tile aggregate accuracies ranging from 80% to 97%. As the first high-resolution large-scale synoptic wetlands map of Alaska, this product provides the basis for improved characterization of land- atmosphere CH4and CO2fluxes and climate change impacts associated with thawing soils and changes in extent and drying of wetland ecosystems.

Methods are developed and evaluated to retrieve surface soil temperature information for the advanced microwave scanning radiometer on earth observing system for seven boreal forest and Arctic tundra biophysical monitoring sites across Alaska and Northern Canada. A multiple-band iterative radiative transfer process-based method producing dynamic vegetation and snow cover correction quantities and an empirical multiple regression method using several frequencies are employed. The seasonal pattern of microwave emission and relative accuracy of the soil temperature retrievals are influenced strongly by landscape properties, including the presence of open water, vegetation type and seasonal phenology, snow cover, and freeze-thaw transitions. The retrieval of soil temperature is similar for the two methods with an overall root-mean-square error of 3.1-3.9 K during summer thawed conditions, with a larger error occurring in winter during periods of dynamic snow cover and freeze-thaw state. These results indicate that at high latitudes, the influence of the atmosphere may be less important than that of surface conditions in determining the relative accuracy of the estimated soil temperature. Impacts of surface conditions on surface emissivity, observed brightness temperature, and estimated soil temperature are discussed.

An accurate measurement of snow-layer thickness on the ground is a critical process for estimating the equivalent water content of snow. Previous studies were mainly based on the analysis of the backscattering cross-section of two co-polarized signals at different frequencies. The snow thickness was inferred by comparison with the analytical model. Recently, we have applied the angular and frequency correlation functions (ACF/FCF) for the estimation of sea-ice thickness. One of the advantages of the ACF/FCF method is suppression of the interfering volume scattering, which results in better accuracy and reliability of thickness estimation. We apply a simplified 1D three-layer model for the analysis. The layers are air, snow, and ground. The interfaces between layers are modeled as rough surfaces. Within the layers, there are small inclusions which introduce the volume scattering. Rough surfaces are modeled by the Kirchhoff approximation methods. The volume scattering is calculated using the quasi-crystalline approximation with coherent potential approximation (QCA-CP) for small particles. The ACF/FCF works by correlating two signals with different frequencies and/or incident angles. Using this model, we can determine the behavior of ACF/FCF and use it for snow thickness retrieval.

Boreal ecosystems play an essential role in global climate regulation. Forests constitute pools of terrestrial carbon and are generally considered as global sinks of atmospheric CO/sub 2/, contributing to attenuating the greenhouse effect. Large amounts of carbon are also stored in boreal lakes, bogs and wetlands, partially released as CH/sub 4/ and other trace gases to the atmosphere during the spring and summer months. Human activities in the forest zone are however reducing the size of the carbon pool and climate change is triggering shorter winters and earlier thaw onset, changing the natural equilibrium. Given its global importance, there is a need to map and monitor the boreal zone, and as the changes occur on all from local, regional to global scales, fine resolution information over vast areas is required. The Global Boreal Forest Mapping (GBFM) project is an international collaborative undertaking initiated by NASDA in 1996, as a follow-on to the tropical-focused Global Rain Forest Mapping (GRFM) project [A. Rosenqvist et al., (2000)]. Utilising the L-band Synthetic Aperture Radar (SAR) on the Japanese Earth Resources Satellite (JERS-1). one of the main objectives of the GBFM project is the generation of extensive, pan-boreaL SAR image mosaics, to provide snap-shots of the forest wetland and open water status in the mid-1990's. Mosaics over Canada, Alaska. Siberia and Europe have been generated, available on the Internet and on DVD free of charge for research and educational purposes. The GBFM project also entails research activities in North America, Siberia and northern Europe, aimed at advancing scientific applications of L-band SAR data in the boreal zone.

The Hydrosphere State Mission (Hydros) is a pathfinder mission in the National Aeronautics and Space Administration (NASA) Earth System Science Pathfinder Program (ESSP). The objective of the mission is to provide exploratory global measurements of the earth's soil moisture at 10-km resolution with two- to three-days revisit and land-surface freeze/thaw conditions at 3-km resolution with one- to two-days revisit. The mission builds on the heritage of ground-based and airborne passive and active low-frequency microwave measurements that have demonstrated and validated the effectiveness of the measurements and associated algorithms for estimating the amount and phase (frozen or thawed) of surface soil moisture. The mission data will enable advances in weather and climate prediction and in mapping processes that link the water, energy, and carbon cycles. The Hydros instrument is a combined radar and radiometer system operating at 1.26 GHz (with VV, HH, and HV polarizations) and 1.41 GHz (with H, V, and U polarizations), respectively. The radar and the radiometer share the aperture of a 6-m antenna with a look-angle of 39/spl deg/ with respect to nadir. The lightweight deployable mesh antenna is rotated at 14.6 rpm to provide a constant look-angle scan across a swath width of 1000 km. The wide swath provides global coverage that meet the revisit requirements. The radiometer measurements allow retrieval of soil moisture in diverse (nonforested) landscapes with a resolution of 40 km. The radar measurements allow the retrieval of soil moisture at relatively high resolution (3 km). The mission includes combined radar/radiometer data products that will use the synergy of the two sensors to deliver enhanced-quality 10-km resolution soil moisture estimates. In this paper, the science requirements and their traceability to the instrument design are outlined. A review of the underlying measurement physics and key instrument performance parameters are also presented.

Spatial and temporal variations in vegetation dielectric properties strongly influence the microwave backscatter characteristics of forested landscapes. This paper examines the relationship between xylem tissue dielectric constant, xylem sap flux density, and xylem sap chemical composition as measured in the stems of two Norway Spruce (Picea abies [L.] Karst.) trees in the Fichtelgebirge region of Northern Bavaria, Germany. Dielectric constant and xylem sap flux were monitored continuously from June through October 1995, at several heights along the tree trunks. At the end of the measurement series, each tree was harvested, and its xylem sap extracted and analyzed to determine the concentrations of amino acids and cations. Results show that the sap flux density was correlated with vapor pressure deficit (VPD) at all heights in the stem. In contrast, the xylem tissue dielectric constant is influenced by VPD but can exhibit a significant temporal lag relative to changes in VPD. This lag varies with position along the tree trunk. The temporal variability of the dielectric constant is compared with both trees at several positions along the tree trunks. Results of xylem sap chemical analysis are presented. We show that spatial and temporal variability in the xylem tissue dielectric constant is influenced not only by water content, but by variations in xylem sap chemistry as well. This has important implications for microwave remote sensing of forested landscapes, as useful information may be acquired regarding stand physiology and water relations and where variations in dielectric properties within individual trees and across geographic areas can be significant error sources for forest inventory mapping.

The design and implementation of a system for the automated and continuous in situ monitoring of the dielectric constant of woody vegetation tissue are presented. The implementation of both single-channel and multichannel systems is discussed. These systems permit unsupervised continuous and long-term monitoring of vegetation canopy dielectric behavior in remote field sites. Utilizing open-ended coaxial lines, the real and imaginary parts of the microwave dielectric constant of woody plant tissue are inferred from direct measurement of the magnitude and phase of the microwave reflection coefficient. Samples of in situ data from forests in contrasting ecological environments are presented. Measurements obtained with the authors' systems allow new insight into the dielectric behavior of vegetation with respect to the physiological and hydraulic function of trees. The observations provide a significant advance in our ability to link canopy physiological and hydraulic behavior to radar remote-sensing observations.

In modeling forest canopies, several scattering mechanisms are taken into account, (1) volume scattering; (2) surface-volume interaction; (3) surface scattering from forest floor. Depending on the structural and dielectric characteristics of forest canopies, the relative contribution of each mechanism in the total backscatter signal of an imaging radar can vary. In this paper, two commonly used first-order discrete scattering models, distorted Born approximation (DBA) and radiative transfer (RT) are used to simulate the backscattered power received by polarimetric radars at P-, L-, and C-bands over coniferous and deciduous forests. The difference between the two models resides on the coherent effect in the surface-volume interaction terms. To demonstrate this point, the models are first compared based on their underlying theoretical assumptions and then according to simulation results over coniferous and deciduous forests. It is shown that by using the same scattering functions for various components of trees (i.e. leaf, branch, stem), the radiative transfer and distorted Born models are equivalent, except in low frequencies, where surface-volume interaction terms may become important, and the coherent contribution may be significant. In this case, the difference between the two models can reach up to 3 dB in both co-polarized and cross-polarized channels, which can influence the performance of retrieval algorithms.

Changes in the seasonal CO/sub 2/ flux of the boreal forests may result from increased atmospheric CO/sub 2/ concentrations and associated global warming patterns. To monitor this potential change, a combination of information derived from remote sensing data, including forest type and growing season length, and ecophysiological models which predict the CO/sub 2/ flux and its seasonal amplitude based on meteorological data, are required. The authors address the use of synthetic aperture radar (SAR) to map forest type and monitor canopy and soil freeze/thaw, which define the growing season for conifers, and leaf on/off, which defines the growing season for deciduous species. Aircraft SAR (AIRSAR) data collected in March 1988 during a freeze/thaw event are used to generate species maps and to determine the sensitivity of SAR to canopy freeze/thaw transitions. These data are also used to validate a microwave scattering model which is then used to determine the sensitivity of SAR to leaf on/off transitions and soil freeze/thaw. Finally, a CO/sub 2/ flux algorithm is presented which utilizes SAR data and an ecophysiological model to estimate CO/sub 2/ flux. CO/sub 2/ flux maps are generated, from which areal estimates of CO/sub 2/ flux are derived. >

The Michigan Microwave Canopy Scattering Model (MIMICS) is used to model scatterometer data that were obtained during the August 1987 EOS (Earth Observing System) synergism study. During this experiment, truck-based scatterometers were used to measure radar backscatter from a walnut orchard in Fresno County, California. Multipolarized L- and X-band data were recorded for orchard plots for which dielectric and evapotranspiration characteristics were monitored. MIMICS is used to model a multiangle data set in which a single orchard plot was observed at varying impedance angles and a series of diurnal measurements in which backscatter from this same plot was measured continuously over several 24-h periods. MIMICS accounts for variations in canopy backscatter driven by changes in canopy state that occur diurnally as well as on longer time scales. L-band backscatter is dependent not only on properties of the vegetation but also on properties of the underlying soil surface. The behavior of the X-band backscatter is dominated by properties of the tree crowns.

The EOS (Earth Observing System) Synergism Study examined the temporal variability of the optical and microwave backscatter due to diurnal change in canopy properties of interest to ecosystem modelers. The experiment was designed to address diurnal changes in canopy water status that relate to transpiration. Multispectral optical and multifrequency, multipolarization microwave measurements were acquired using boom-truck-based systems over a two-week period. Sensor and canopy properties were collected around the clock. The canopy studied was a walnut orchard in the San Joaquin Valley of California. The results demonstrate a large diurnal variation in the dielectric properties of the tree that in turn produces significant diurnal changes in the microwave backscatter. The results suggest that permanently orbiting spaceborne sensors such as those on EOS should be placed in orbits that are optimized for the individual sensor and need not be tied together by a tight simultaneity requirement on the order of minutes to hours for the purpose of monitoring ecosystem properties.