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..

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.

This letter describes a model-based algorithm for estimating tree height and other bio-physical land parameters from time series of synthetic aperture radar (SAR) interferometric coherence and backscatter supported by sparse lidar data. The random-motion-over-ground model (RMoG) is extended to time series and revisited to capture the short- and long-term temporal coherence variability caused by motion of the scatterers and changes in the soil and canopy backscatter. The proposed retrieval algorithm estimates first the spatially slow-varying RMoG model parameters using sparse lidar data, and subsequently the spatially fast-varying model parameters such as tree height. The recently published global Sentinel-1 (S-1) interferometric coherence and backscatter data set and sparse spaceborne GEDI lidar data are used to illustrate the algorithm. Results obtained for a small region over Spain show that the temporal coherence and backscatter time series have the potential to be used for global, model-based land parameter estimation.

In this paper, we assess the estimation of bio-physical land parameters from time-series of interferometric SAR coherence supported by a physical model. The random-motion-over-ground model (RMoG) is revisited to partially capture the short- and long-term temporal variability of the coherence caused by motion of the scatterers and changes in their dielectric properties. The recently-published global Sentinel-1 interferometric coherence dataset is used to compare model predictions with observations and evaluate the need for additional model assumptions or ancillary data sets. Space-borne lidar data acquired by GEDI are also considered to further constrain the parameter estimation. This work is particularly relevant to upcoming SAR missions such as NISAR and ROSE-L that will generate global and dense time-series of interferometric temporal coherence at L-band.

The NASA ISRO Synthetic Aperture Radar (NISAR) Mission plans to generate >40TB of raw data daily to support open access and operational L-band science. This includes Ecosystems applications for agriculture. To further prepare, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) platform was used to observe cropland sites across the southern United States to support the development of L-band prototype science products. Major crops include corn, cotton, pasture, peanut, rice, and soybean. A suite of cropland classification experiments applied a set of strategic algorithms to synergistically assess performance, scattering mechanisms, and limitations. SAR terms with sensitivity to volume scattering performed well and consistently across mapping experiments achieving accuracy greater than 80% for cropland vs not cropland. Volume scattering and cross-pol terms were most useful across the different ML techniques with overall accuracy and Kappa consistently over 90% and. 85, respectively, for crop type by late growth stages for both L-band observations.

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.

The ecosystem science requirements for the NASA ISRO Synthetic Aperture Radar (NISAR) will need to be validated after its launch in 2021 [1]. Out of all disciplines that are encompassed by the NISAR mission, ecosystems are the one in most need of pre-launch proxy data, consisting of repeated L-band observations over an extended period of time. The solid earth, cryosphere, and hazards research communities have been able to use historical and contemporary spaceborne data available from ERS-1/2, Radarsat, TerraSAR, Sentinel-1, and others for developing and evaluating products similar to what NISAR would be able to provide. This has been possible, in part, because of the focus of these disciplines on sparsely vegetated surfaces and the fairly straight-forward correspondence of surface scattering properties at both L- and C-band (wavelength of 24 cm and 5 cm respectively). Time series data from L-band sensors of value for ecosystem science disciplines, in contrast, have been sporadic and irregular. Ecosystems targets are almost always vegetated, with the scattering components and volume scattering nature of the target giving different scattering responses at the different wavelength regimes. For this reason, the use of C-band observations as a proxy for NISAR's L-band, as is often done for other disciplines, is not possible for the development and testing of NISAR algorithms.In 2018, a plan was developed for a field/airborne/spaceborne campaign to acquire data in 2019 for NISAR pre-launch ecosystem algorithm development and for evaluation of NISAR ecosystem Cal/Val protocols. This plan includes the acquisition of not just L-band SAR data from the NASA/JPL UAVSAR airborne SAR, but also the acquisition of spaceborne data, airborne data, and field measurements to fully exercise the NISAR protocols for validation of its ecosystem science measurement requirements. Included in the plan is the processing of the field, airborne, and spaceborne data into validation products and the generation of NISAR-like level 3 science products.

We present an error model for forest cover mapping and change detection with L-band synthetic aperture radar (SAR), which considers measurement noise, forest height, number of images available, and imaging conditions. When applied to a multiseasonal set of Advanced Land Observing Satellite Phased-Array type L-band SAR images acquired over a forest site in southern Sweden, the error model, which is founded on a semiempirical model, suggests that a bitemporal set of cross-polarized L-band backscatter observations is sufficient to detect a forest cover loss of 50% at hectare scale for mature forests. The error probability increases when using co-polarization images, images acquired under adverse imaging conditions, or when detecting forest cover change in a forest of low height. The availability of multitemporal L-band observations is expected to improve forest cover retrieval and change detection, albeit highly correlated forest cover retrieval errors between images acquired within narrow time intervals (e.g., months) pose a limit on the improvements that can be achieved.

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.

A study was conducted to determine the extent to which data from the 2000 Shuttle Radar Topography Mission (SRTM) can be used to estimate vegetation canopy height in conjunction with an existing bald Earth DEM as provided by the National Elevation Dataset (NED). Intensively managed slash pine stands with canopy heights ranging from 11 to 21 m were biometrically surveyed within the general mission timeframe in early 2000. Results indicate that SRTM data can be successfully correlated via linear regression modeling with ground-measured mean vegetation canopy height at the stand level when mean SRTM-NED height difference measures are extracted by averaging pixels within the stands. Regression analysis using 20 and 50 pixels as stand size thresholds yielded adjusted r2values of 0.79 and 0.86 with rms errors of 1.1 m and 1.0 m respectively. Thus a minimum SRTM mapping unit of approximately 1.8 hectares can be postulated which allows vegetation canopy height retrieval at the stand scale

A study was conducted to determine the extent to which data from the 2000 Shuttle Radar Topography Mission (SRTM) could be used to estimate vegetation canopy height in conjunction with an existing bald-Earth DEM provided by the National Elevation Dataset (NED). A densely forested study site with maximum canopy heights reaching 70+ m in the central Sierra Nevada of California was identified based on the availability of suitable field data from within the general mission timeframe. Preliminary work has been conducted to compare C-band SRTM and TOPSAR digital elevation products with canopy height estimates obtained from ground measurements. Results indicate that SRTM data can be successfully correlated via linear regression modeling with ground-measured metrics of vegetation canopy height including median, mean, and maximum height. Maximum canopy height was predicted from SRTM data with an RMSE of 4.9 meters when a minimum of 50 SRTM pixels w as available for averaging This study confirms previous findings, which suggest that after averaging a minimum of 20 SRTM-NED difference pixels to reduce phase noise errors, stable estimates of interferometric mean scattering phase center height can be extracted.

Recent advancements in object oriented image classification provide possibilities to investigate new approaches for inversion techniques for synthetic aperture radar (SAR) images to derive bio-/geophysical parameters, like forest biomass. A study was performed on ERS and JERS SAR data in the Raco test site in Michigan. Both data sets were acquired within 10 days during summer 1992. Ground reference data were available from 80 forest stands with biomass ranges from early regrowth to mature stands for various pine species. Ecognition software was used to perform image segmentation. It was found that the software generated excellent image objects which correlate spatially well with existing stand boundaries and ecological units. However, the SAR data needed to be pre-filtered to reduce the influence of speckle to achieve better segmentation results. Also, improved segmentation was found when ERS and JERS data were used jointly in the segmentation process. Mean backscatter values of the 4 hectare test stands were compared with the mean backscatter of the larger image objects which contain the test stands. A comparison of the 4 ha test stands with the image objects containing these stands showed a signal correlation with an r/sup 2/ of 0.89. The derivation of biomass was then compared using the stand data only or the image objects only. While the r/sup 2/ values were about 0.1 higher for the stand derived regression equations, virtually the same model coefficients (slope, intercept) were achived with the biomass regression with stand data and image object data. This shows, that models which are developed on carefully selected stand data can be transferred to image objects which resulted from prior segmentation of the SAR data.

Synthetic Aperture Radar (SAR) is known to have a response that is directly related to the amount of living material that it interacts with. It is this property that our research seeks to exploit in order to better understand carbon dynamics in the Amazon. The vegetation density causes the radar response to saturate such that vegetation that is more dense than some threshold is indistinguishable from each other. However, the areas of regrowth are likely to have a low enough biomass during the first 10 years of regrowth to be accurately assessed using radar. Our efforts involve obtaining appropriate pairs of radar images at L and C bands from different sites and for both seasons. These data are then orthorectified to allow accurate calibration and incidence angle correction. The seasonality of the data is used to deal with the moisture sensitivity of the data, and the different frequency data is used to help classify the data into several classes for use in class-specific biomass estimates. We have chosen 2 sites in Brazil for our study. we use the JERS-1 (L-band) and RADARSAT (C-band) data to create a 2-channel composite. These data are then classified into the following classes: flat area (water, bare soil), short vegetation, regrowth, and trees. We report on the accuracy of both our classification and biomass estimation efforts.

In this paper, we present a novel approach to continuous remote sensing of Earth. The proposed concept "GLORIA" drastically enhances the ability of scientists to study the Earth in a manner not possible before. The proposed system is based on a constellation of few geostationary, radar transmitter satellites and several low Earth orbiting synthetic aperture radar receiver satellites. Just as the sun is the radiation source for optical remote sensing, transmitters of microwave energy in a geostationary orbit provide the energy for radar remote sensing. Advantages of such a constellation lie in (1) a much larger number of observables, due to multi-static measurements, which significantly enhances the accuracy of retrieval algorithms, 2) the distribution of failure risk is by eliminating total system failure if a single satellite stops operating, 3) simple modular system design of small satellites through separation of transmitters and receivers (reducing weight, cost and power consumption by each satellite), and 4) flexibility in operation, that is, the receivers can be configured for different modes.

During an EOCAP-SAR project, Airsar P-, L-, and C-band data were used to test the capability of synthetic aperture radar (SAR) to predict biometric parameters which are frequently used by timber managers as inputs to growth, harvest, and yield models. Test site was a commercially managed area in Jesup, southeastern Georgia, with stands owned by Plum Creek Timber Company. A biometric survey data set from 118 cluster plots in two thinned and two unthinned slash pine stands was used to determine the correlation with the Airsar data at the plot level. A statistical model was used to test all possible scenarios of polarimetric and frequency combinations. Using a weighted least squares linear regression model, P/sub HV/ proved to be the single most significant band/polarization combination to correlate with dominant height, basal area, and volume at adjusted squared correlation coefficients of 0.70, 0.80, and 0.84, respectively.

During an EOCAP-SAR project, Airsar P-, L- and C-Band data were used to test the capability of SAR to predict biometric parameters which are frequently used by timber managers as inputs to growth, harvest and yield models. The test site was a commercially managed area in Jesup, south-east Georgia, with stands owned by The Timber Company (TTC). Field campaigns provided an extensive plot-level georeferenced ground data set of four slash pine stands which was used to determine the correlation with the Airsar data. Additionally, 18 stands were surveyed and stand level summaries of biometric variables height, basal area, and volume were obtained. A statistical model was used to test all possible scenarios of polarimetric and frequency combinations. The best models were chosen to develop inversion models for basal area. These model results were applied to stands which are owned by a different timber company (Rayonier), and which were located in the Airsar strip. Within the TTC land holdings, R/sup 2/ correlation coefficients for volume prediction from SAR were 0.85. The correlation between predicted basal area and stand age on 65 Rayonier stands resulted in the an adjusted R/sup 2/ of 0.86.

A study was conducted to assess the potential of combined imagery from the existing European and Japanese orbitar synthetic aperture radar (SAR) systems, ERS-1 (C-hand, VV-polarization) and JERS-1 (L-band, HH-palarization), for regional-to-global-scale vegetation classification. For seven test sites from various ecoregions in North and South America, ERS-1/JERS-1 composites were generated using high-resolution digital elevation model (DEM) data for terrain correction of geometric and radiometric distortions. An edge-preserving speckle reduction process was applied to reduce the fading variance and prepare the data for an unsupervised clustering of the two-dimensional (2D) SAR feature space. Signature-based classification of the clusters was performed for all test sites with the same set of radar backscatter signatures, which were measured from well-defined polygons throughout all test sites. While trained on one-half of the polygons, the classification result was tested against the other half of the total sample population. The multisite study was followed by a multitemporal study in one test site, clearly showing the necessity of including multitemporal data beyond a level 1 (woody, herbaceous, mixed) vegetation characterization. Finally, classifications with simulation of backscatter variations shows the dependence of the classification results on calibration accuracy and on naturally occurring backscatter changes of natural surfaces. Overall, it is demonstrated that the combination of existing orbital L- and C-band SAR data is quite powerful for structural vegetation characterization.

A three-step process is presented for estimation of forest biophysical properties from orbital polarimetric SAR data. Simple direct retrieval of total aboveground biomass is shown to be ill-posed unless the effects of forest structure are explicitly taken into account. The process first involves classification by (1) using SAR data to classify terrain on the basis of structural categories or (2) a priori classification of vegetation type on some other basis. Next, polarimetric SAR data at L- and C-bands are used to estimate basal area, height and dry crown biomass for forested areas. The estimation algorithms are empirically determined and are specific to each structural class. The last step uses a simple biophysical model to combine the estimates of basal area and height with ancillary information on trunk taper factor and wood density to estimate trunk biomass. Total biomass is estimated as the sum of crown and trunk biomass. The methodology is tested using SIR-C data obtained from the Raco Supersite in Northern Michigan on Apr. 15, 1994. This site is located at the ecotone between the boreal forest and northern temperate forests, and includes forest communities common to both. The results show that for the forest communities examined, biophysical attributes can be estimated with relatively small rms errors: (1) height (0-23 m) with rms error of 2.4 m, (2) basal area (0-72 m/sup 2//ha) with rms error of 3.5 m/sup 2//ha, (3) dry trunk biomass (0-19 kg/m/sup 2/) with rms error of 1.1 kg/m/sup 2/, (4) dry crown biomass (0-6 kg/m/sup 2/) with rms error of 0.5 kg/m/sup 2/, and (5) total aboveground biomass (0-25 kg/m/sup 2/) with rms error of 1.4 kg/m/sup 2/. The addition of X-SAR data to SIR-C was found to yield substantial further improvement in estimates of crown biomass in particular. However, due to a small sample size resulting from antenna misalignment between SIR-C and X-SAR, the statistical significance of this improvement cannot be reliably established until further data are analyzed. Finally, the results reported are for a small subset of the data acquired by SIR-C/X-SAR.>