The Chinese L-band twin-satellite SAR constellation, LuTan-1, that was launched in 2022 became the first spaceborne L-band bistatic InSAR mission. In this work, we will explore this valuable dataset of bistatic InSAR mode to estimate forest height. Since the majority of this mode only acquires single-polarization (HH-pol) data, we use the few-look InSAR phase histogram method developed by our previous work to estimate the digital terrain height (DTM) and InSAR phase center height simultaneously. Then, the HH-pol complex InSAR coherence measurements in combination with the DTM (or phase center height) are used to invert for forest total height using a physical model approach, namely the Random Volume over Ground (RVoG) model. Preliminary inversion results are shown and validated against spaceborne lidar (NASA’s GEDI and ICESat-2/ATLAS) and airborne lidar data over the tropical test site on the Hainan island of China.

This paper describes the Altay 2024 airborne field campaign in support of snow observation retrieved from spaceborne InSAR measurements from the Chinese LuTan-1 (a spaceborne L-band SAR constellation launched in 2022). The airborne and field measurements that are synchronized with LuTan-1 InSAR acquisitions will be conducted in January-February 2024 (snow on) and May-July 2024 (snow off). The remote sensing and in-situ measurements include various in-situ observations and drone-based lidar measurements. We first provide the overview of the Altay 2024 campaign including the choice of the in-situ measurement locations and flight tracks of the drone-based lidar. Then, historical InSAR dataset from all the available L/C-band SAR’s (e.g. JAXA’s ALOS, ESA’s Sentinel-1, China’s LuTan-1) over the study area are used to generate SWE change products, which are further compared against the in-situ measurements when available. This synergetic spaceborne airborne field campaign will directly validate the LuTan-1 derived snow products using the acquired airborne and field dataset, which can also support the design of future spaceborne mission concepts for snow retrieval.

This paper describes a 3-D Pseudospectral Time-Domain full-wave simulator for solving large-scale electromagnetic scattering problems and radar sounding applications. The simulated 3-D backscatter and bistatic scattering radar cross sections are compared with analytical solution of lossless and lossy dielectric spheres. The PSTD solver is applied to simulating the surface scattering of a Martian moon - Phobos using the MARSIS's radar characteristics and viewing geometry. Utilizing GPU parallelization, the simulation is performed on a kilometer-scale domain with enhanced efficiency in both time and memory as well as better accuracy, demonstrating its superiority in large-scale radar sounding simulations by using the full-wave method.

Snow water equivalent (SWE) is a critical component in the global water and energy cycles. This study proposes a SWE inversion method applicable to VV and VH dual-polarization Synthetic Aperture Radar (SAR) data. The method establishes a cost function between simulated and SAR-measured scattering and subsequently solves this function to obtain SWE values. Using the central region of the Sierra Nevada Mountains in the United States as the study area, SWE was retrieved using five Sentinel-1 images acquired from January to February 2021. The verification results show the feasibility of the method, which will help expand the application of Sentinel-1 in snow monitoring.

This paper presents 30 m gridded mosaic of forest canopy height for the New England region of the United States, encompassing Maine, New Hampshire, Vermont, Massachusetts, Connecticut, and Rhode Island, covering a total area of 18 million hectares. The forest height estimates were derived based on ALOS repeat-pass SAR interferometry (InSAR) observations (100 InSAR pairs) and a semi-empirical physical model [1]. sd Further efforts were devoted to automating this approach for generating large-scale forest height products and evaluate the performance of the products at different sites (e.g., flat, hilly area, etc). As validated against NASA’s LVIS airborne LiDAR, this approach presents an accuracy of 3–4 m (RMSE) over flat area and an accuracy of 4-5 m per sub-hectare pixel over hilly area on the order of sub-hectare pixel (0.81 ha). This approach demonstrates promising values in the context of combining low-frequency InSAR observations and LiDAR measurements from existing and future spaceborne missions.

In this work, we introduce a novel InSAR processing routine for handling the low-coherence data from long temporal baseline (~ 4 months) L-band ALOS-2 InSAR pairs in the 2016–2017 snow season over the mountainous regions of Colorado, mostly covered by sparse forest. A physical InSAR scattering model was exploited to simulate the InSAR sensitivity of SWE retrieval for forest-covered sites. InSAR-retrieved SWE results are compared with SNOTEL in-situ measurements with a correlation of 0.5 (p-value of 0.01), however, with a much shorter dynamic range of 80 mm versus the actual SWE range of 400 mm. This paper sheds light on using long temporal baseline repeat-pass L-band InSAR data for forest-covered SWE retrieval.

This paper presents a promising fusion prototype for forest stand height inversion using L-band spaceborne repeat-pass SAR interferometry (InSAR) and spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR measurements. NASA’s GEDI mission provides sparsely but extensively distributed LiDAR measurements which could serve as ample calibration samples to improve the forest height estimates based on InSAR information and semi-empirical scattering model. Based on previous efforts, this paper further removed the assumptions that were made given by the limited availability of calibration samples at that time, and developed a new inversion approach based on a global-to-local two-stage fitting scheme. Making good use of local GEDI samples in this approach allows a finer characterization of temporal decorrelation pattern and thus higher accuracy of forest height inversion. This approach is validated at the Howland Forest in Maine, U.S. by using ALOS InSAR and GEDI LiDAR data, where the estimates achieve a RMSE of 3.8m at a sub-hectare spatial resolution (e.g., 0.81 ha). The above experimental results demonstrates a promising prototype towards a large-scale forest height mapping using existing and future spaceborne L-band InSAR missions (JAXA’s ALOS-1/2, China’s L-SAR, NASA-ISRO’s NISAR), as well as spaceborne LiDAR missions (e.g. NASA’s GEDI, JAXA’s MOLI and China’s TECIS).

In this paper, we refine a previously developed forest height inversion and mosaicking approach that fuses spaceborne repeat-pass L-band Interferometric Synthetic Aperture Radar (InSAR) and limited lidar data that serve as training samples. In particular, with the availability of spaceborne lidar training samples from NASA's GEDI mission, the previous inversion approach has been adapted to use the sparsely distributed but extensive GEDI lidar data as local training dataset, so that the inverted height estimates are better tuned to match the local lidar data without making assumptions such as constant scene-wide mean behavior of temporal changes. The refined inversion approach is validated at the Howland Forest in Central Maine, US by using JAXA's ALOS/ALOS-2 InSAR data combined with NASA's GEDI lidar data, where the inverted height estimates are compared against NASA's LVIS lidar dataset that serve as ground truth. This refined inversion approach is a potential fusion scheme of the future spaceborne repeat-pass L-band InSAR and lidar missions (e.g. NASA's NISAR and GEDI, JAXA's ALOS-4 and MOLI, and China's LT-1 and TECIS-1).

In a recent study, a 2-D pseudospectral time-domain (PSTD) full-wave simulator was developed and demonstrated to be capable of efficiently solving large-scale low-frequency (e.g., HF) electromagnetic scattering problems, for example, on the application of radar sounding simulations of planetary clutter and subsurfaces. In this article, the 2-D PSTD simulator is applied to simulate a domain as large as 4000$\lambda $(along-track)$\times 1666.67\,\,\lambda $(cross-track)$\times 33.33\,\,\lambda $(depth) with$\lambda =15$m at an HF frequency of 20 MHz. To accomplish the goal, the simulator is further improved to efficiently model/simulate large cross-track slices of dielectric scenes by allowing nonuniform grid sampling in horizontal (lateral) and vertical directions, and the cross-track results are then stitched together along the track to form the simulated radargram. By combining the SHAllow RADar (SHARAD) viewing geometry and Mars Orbital Laser Altimeter (MOLA) digital elevation model (DEM), we simulate SHARAD returns at three different sites on Mars: one at the North Pole and two at Oxia Planum. At all three sites, the PSTD simulated radargrams are compared with measured SHARAD radargrams. Through power-level calibration and reference time adjustment, the PSTD simulated power estimates are further validated by comparing with real power observations from SHARAD with a 5-dB uncertainty and Pearson correlation coefficient of 0.3–0.4 (a$p$-value on the order of$10^{-9}$), which justifies the use of the 2-D PSTD simulator for emulating surface clutter in planetary radar sounding. This simulator is open source and can be easily modified to support radar sounding simulations in support of other planetary missions with radar sounding instruments.

In this article, we investigate the potential of using single-pass InSAR model-based approaches to retrieve dry snow parameters. Two InSAR scattering models of dry snow are considered: the dense-medium random volume over ground (RVoG) model and the simple variant of the full penetration (FP) model. A quasi-crystalline approximation (QCA)-based extinction analysis confirms the negligible extinction dependence of the InSAR observables at L/C/X-band for fresh dry snow. The FP models the low-frequency (L/C/X-band) InSAR phase as a single constraint of snow depth and density, which can be supplemented by an extra observation (e.g., InSAR coherence orin situdepth/density). The single-pass InSAR models and inversion approaches were validated using X-band InSAR data collected from a tower-based three-frequency (X/Ku-low/Ku-high) fully polarimetric TomoSAR system, where a multi-frequency polarimetric InSAR analysis and ground-to-volume ratio-based snow condition analysis were conducted. We also analyzed the sensitivity and error propagation of the single-pass InSAR phase and coherence in measuring dry snow depth/density. It was found that the X-band HH-pol FP-modeled single-pass InSAR phase along with RVoG-modeled coherence orin situdepth is capable of measuring snow water equivalent (SWE) with a 23–26 mm uncertainty (13–15%) and a 20–26 mm bias (12–15%) for dry snow SWE of 0.2 m, and with an optimal perpendicular baseline on the order of a tenth of the snow depth (0.8 m) at our test site. This single-pass InSAR approach with the FP model is potentially useful and thus needs further investigation for large-scale dry snow retrieval with a wide range of snow conditions using ground-based/airborne/spaceborne low-frequency (L/C/X-band) InSAR observations.

This paper describes a 2D Pseudospectral Time-Domain (PSTD) full-wave simulator for solving large-scale half-space electromagnetic scattering problems with the application of radar sounding of planetary subsurfaces. New domain designs are developed to efficiently simulate 2D scattering of half-space media for normal and oblique incidence from arbitrary wave sources. The simulated 2D bistatic scattering radar cross width (RCW) is compared with the analytical solutions of random rough surfaces with various choices of grid sampling resolution. The PSTD solver is applied to a passive sounding problem with SAR focusing. An example of using the solver is shown for emulating three-dimensional large-scale radar sounding problems with cross-track surface and subsurface scattering. The PSTD solver is both memory-efficient and accurate for sounding applications, and particularly useful to simulate large-scale radar sounding returns and SAR focused imagery.

In this paper, we present a fast and intelligent routine for dense feature tracking with almost two orders of magnitude runtime improvement over conventional dense cross-correlation techniques. This routine consists of two novel modules: 1) “autoRIFT”, an efficient and intelligent dense cross-correlater with nested grid design, sparse/dense combinative searching strategy and disparity filtering technique; 2) “Geogrid”, the precise geocoding component that supports pointwise mapping between imaging coordinates (pixel location and displacement) and geographic Cartesian coordinates (geolocation and displacement velocity). autoRIFT can run on a grid in the native imaging coordinates (such as radar or map) and, when used in conjunction with the Geogrid module, on a user-defined grid in a geographic Cartesian coordinate system such as Universal Transverse Mercator or Polar Stereographic. Here we demonstrated its application in tracking ice displacement and validated with ESA's Sentinel-1A/B radar and NASA's Landsat-8 optical data.

Spaceborne SAR Interferometry (InSAR) has the sensitivity to measure canopy height as well as the underlying topography. However, these measurements are limited by attenuation of the radar wave. In this paper, we refine an interferometric approach that exploits few-look averaged interferograms, a coherent electromagnetic simulator and field inventory data. Both the underlying topography as well as the canopy height (forest mean height, canopy top height) are estimated through a statistical method that relates the true ground position to the statistics of few-look InSAR phase-heights that are simulated using the simulator and field data. Using DLR's TanDEM-X InSAR data, we validate the approach over a well-studied Brazilian tropical forest with both field inventory and lidar data. As validated against lidar data, the estimated underlying topography has an accuracy of 3 m, while the forest mean height and canopy top height have an accuracy of 3 m and 4-5 m, respectively, at the resolution of one hectare forest stands. Given the global data availability of TanDEM-X and the future TanDEM-L, this approach has the potential of wall-to-wall mapping of the forest height as well as underlying topography using single polarization/baseline.

This article discusses the implementation of a 2-D pseudospectral time-domain (PSTD) full-wave simulator for solving large-scale low-frequency (e.g., HF) electromagnetic (EM) scattering problems with the application of radar sounding of planetary subsurfaces. Compared to other computational EM algorithms, the PSTD solver is both memory-efficient and accurate for sounding applications. New domain designs are developed to efficiently simulate 2-D scattering of half-space media for normal and oblique incidence from arbitrary wave sources. As a validation of the PSTD simulator, the simulated 2-D scattering radar cross width (RCW) is compared with the analytical solutions of both point targets (dielectric cylinders) and distributed targets (random rough surfaces), for the first time, where the frequency and angular (bistatic scattering) dependence are studied with various choices of grid sampling resolution. Furthermore, the PSTD solver is applied to passive synthetic aperture radar (SAR) sounding problems (single transmitter and several receivers), for the first time, where various scenarios (e.g., cylinder, surface, and volume) are demonstrated and the targets are correctly resolved after focusing, indicating an accurate simulation of the phase history. Finally, an example of using the solver is shown for emulating 3-D large-scale radar sounding problems with cross-track surface and subsurface scattering. This is particularly useful to simulate radar sounding returns and SAR-focused imagery of large-scale subsurface structures to better support planetary missions with radar sounding instruments.

This paper provides an overview of the scattering model, inversion approach, and validation of the application results for creating large-scale moderate-resolution (hectare-level) mosaics of forest height through using spaceborne repeat-pass SAR interferometry and lidar. By incorporating several improvements to the forest height inversion and mosaicking approach, the height estimation accuracy along with the robustness of this approach have been considerably enhanced from its originally reported accuracy of RMSE of 3-4 m at a 20-hectare aggregated pixel size to RMSE of 3-4 m on the order of 3-6 hectares. Furthermore, practical data processing schemes are provided in detail. Extensive validation results are demonstrated which include: 1) a forest height mosaic (total area of 11.6 million hectares) is generated for the U.S. states of Maine and New Hampshire using Japanese Aerospace Exploration Agency's (JAXA) ALOS-1 InSAR correlation data and a small airborne lidar strip (44 000 hectares); 2) the mosaic height estimates are further compared with the available airborne lidar data and field measurements over both flat and mountainous areas; and 3) feasibility of using modern repeat-pass InSAR satellites with short repeat interval is also examined by using JAXA's ALOS-2 data. This simple and efficient approach is a potential observational prototype with much smaller error budget for the future spaceborne repeat-pass L-band InSAR systems with small spatial baseline and moderate/large temporal baseline (such as NISAR) in combination with lidar (such as GEDI) on the application of large-scale forest height/biomass mapping. It also serves as a complementary tool to the spaceborne single-pass InSAR systems using InSAR/PolInSAR methods when full-pol data are not available and/or when the underlying topography slope causes problems for these approaches.

SnowEx is a multi-year airborne snow campaign led by NASA. The purpose of SnowEx is to figure out how much water is stored in Earth's terrestrial snow-covered regions. As part of the 2017 NASA SnowEx campaign, we deployed a portable triple-frequency (9.6GHz, 13.5GHz and 17.2GHz) and fully polarimetric frequency-modulated continuous-wave (FMCW) radar at Fraser, Colorado. The radar was installed on a 60cmx60cm frame to enable a full reconstruction of the three-dimensional variability per each radar channel. The tomography technique uses the radar echo from the multiple viewing positions and provides a unique access to the vertical structure of the snow layer. With current setup, the range resolution is 30cm. In this paper, we will review the radar design and signalprocessing algorithm - time domain back projection. The generated vertical images show the snow stratigraphy, which is consistent with ground snow pit measurement. The continuous operation demonstrates diurnal thawing and refreezing process. The snow density is retrieved by comparing to the snow free image.

Focusing on open forests and woodlands within the Injune Landscape Collaborative Project research area in central southeast Queensland, Australia, and using dual-pol (HH and HV) ALOS PALSAR repeat-pass InSAR data (temporal baseline of 92 days), this paper explores the detection of forest disturbance from the spaceborne repeat-pass InSAR correlation magnitude by developing a simple and efficient forest disturbance detection approach. In particular, a generic physical InSAR scattering model is derived by accounting for the forest disturbance information as well as the normal temporal decorrelation effects that are later compensated for using the modified Random Volume over Ground model. Based on the generic model, a quantitative indicator of forest disturbance is retrieved, namely, disturbance index that varies from 0 (no disturbance) to 1 (complete deforestation). This index is compared with that identified using a time series of Landsat sensor data over a selective logging area and has a relative root mean square error of 13% at a spatial resolution of 0.8 ha. This paper highlights the use of the co-pol InSAR correlation magnitude for forest disturbance detection, which serves as a complimentary application to using the cross-pol counterpart for forest height inversion in a companion work. Given the global availability of this type of data (e.g., Japanese Aerospace Exploration Agency's ALOS-1/2 and NASA-ISRO's NISAR), the method is anticipated to contribute to the range of tools being developed for large-scale forest disturbance assessment and monitoring.

Spaceborne SAR interferometry (InSAR) has the potential of mapping the forest height on a global scale and a monthly/weekly basis, which can improve our understanding of the global carbon dynamics. In previous work, repeat-pass SAR interferometry from spaceborne sensors is utilized to create large-scale forest height maps based on a newly developed approach. This paper thus serves as a summary paper and also sheds light on the future directions with improved results. In particular, it will be shown that repeat-pass SAR interferometry is able to create a large-scale (11.6 million hectares) forest height mosaic product with RMSE ≤ 4 m for forest stands on the order of 6 hectares over both the flat and mountainous areas in New England, US through using the past and current spaceborne repeat-pass InSAR observations (i.e. JAXA's ALOS-1 and ALOS-2) combined with sparse airborne lidar training samples (44,000 hectares). Moreover, the results and performance of this approach can be remarkably improved with several enhancement techniques that can be easily satisfied with the use of future spaceborne repeat-pass InSAR and lidar missions (e.g. NASA-ISRO's NISAR and NASA's GEDI). The methodology described in this paper can be considered as a complimentary tool to the existing PolInSAR technique when single-pass full/dual-pol data are not available and/or the underlying topography is complicated.

Spaceborne SAR interferometry (InSAR) has the potential of detecting forest change on a global scale with fine (meter-level) spatial resolution as well as on a monthly/weekly basis regardless of day or night. This is significant to characterize the land-use change and its impact on climate change. In this paper, both single-pass and repeat-pass SAR interferometry from spaceborne sensors are combined in order to detect and quantify (with Normalized RMSE ≤ 30%) forest disturbance at a large scale (dozens of kilometers) however with a fine spatial resolution (< 1 hectare) based on two newly developed approaches. The single-pass InSAR approach is not only able to detect forest disturbance but also capable of characterizing meter (or even sub-meter) level change of forest phase-center (mean) height due to forest growth and/or degradation. These methods are extensively validated with the past and current spaceborne single-pass and repeat-pass InSAR missions (i.e. JAXA's ALOS-1, ALOS-2 and DLR's TanDEM-X) over subtropical forests in Australia as well as tropical forests in Brazil. Such techniques also serve as observing prototypes for the fusion of the future spaceborne InSAR missions (such as NASA-ISRO's NISAR and DLR's TanDEM-L).

This paper applies the forest height inversion approach developed in [1] along with the automatic mosaicking algorithm described in [2], and generates a state mosaic map for the US state of New Hampshire (NH) by utilizing the spaceborne repeat-pass ALOS/PALSAR InSAR correlation magnitude data and the airborne lidar data from NH GRANIT database over the White Mountain National Forest (WMNF). Since the forest height map for the US state of Maine (ME) has already been generated using ALOS/PALSAR InSAR data and a small strip of LVIS lidar data over the Howland forest in central Maine, a two-state mosaic, i.e., ME+NH mosaic, can be generated using the LVIS lidar strip with the GRANIT lidar data serving as a separate validation site so that the quality (e.g. error propagation) of the ME+NH mosaic map can be determined. In addition to providing the mosaic map of forest height, this forest height inversion approach is also modified to generate a forest disturbance map over the ground validation site at WMNF where ground truth (such as GRANIT lidar) data is available. The approaches along with the analysis described in this paper will serve as observing prototypes for the data fusion of future spaceborne repeat-pass InSAR missions (e.g. NASA's NISAR [3]) and lidar missions (e.g. NASA's GEDI [4]) in characterizing the large-scale forest height as well as disturbance events (e.g. selective logging and/or forest degradation).

Snow characteristics, such as Snow Water Equivalent (SWE) and snow grain size, are essential to monitor the global hydrological cycle and thus an indicator of climate change. This paper demonstrates an InSAR scattering model for dense medium such as snow considering the multiple scattering effect through the use of Quasi-Crystalline Approximation (QCA) and Percus-Yevick pair distribution function. Based on the simplified versions of the model, simulated results are shown for the problem of snow characteristics retrieval. First, it is noticed that the Ka-band InSAR phase has better sensitivity to the snow grain size, while the L-band InSAR phase has better sensitivity to the snow depth. Then, the Ka- and L-band InSAR correlation measurements are utilized to retrieve the snow volume parameters along with the ground parameters simultaneously. The InSAR scattering model and the retrieval approach proposed in this paper can be a complimentary tool for other techniques of retrieving snow characteristics.

This paper describes the algorithm and results for an automatic mosaicking method in generating a large-scale forest stand height (FSH) map utilizing spaceborne repeat-pass HV-pol InSAR coherence data. This technique demonstrates the capability for creating FSH metrics that can cover large areas. By using repeat-pass InSAR correlation measurements that are dominated by temporal decorrelation and scene-wide fitting parameters (two) that depend on the mean random motion and dielectric changes of the volume scatterers within the scene, it can be shown that a height sensitive measure can be created and validated over the whole scene. In order to combine these single-scene results into a mosaic, a matrix formulation is used with nonlinear least squares (NLS) and observations in adjacent-scene overlap areas to create a self-consistent estimate of FSH over the larger region. This mosaicking algorithm is validated over the US state of Maine by comparing the inverted FSH with Laser Vegetation Imaging Sensor (LVIS) height and National Biomass and Carbon Dataset (NBCD) Basal Area Weighted (BAW) height.

Knowledge of the global biomass distribution is essential in monitoring the carbon cycle budget and the climate change. In addition to limited field inventory data, researchers have been developing remote sensing techniques (e.g., LiDAR, SAR backscatter, InSAR phase and/or correlation magnitude) to derive biomass maps. Most of the techniques seek for empirical relationships between biomass and remote sensing measures; however, lack of a direct physical interpretation of the measurement constrains the utility and understanding of remote sensing data sensitivity to the forest characteristics of interest. In this paper, we explore the use of InSAR correlation magnitude to invert for the tree height through use of a physical scattering model [1]. The inversion algorithm, along with the estimates of the tree heights, will be cross-compared with itself over our test area: the Injune region (ILCP) in Australia.