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.

This letter aims to develop an inversion model to estimate soil moisture using Navigation with Indian Constellation (NavIC) L-band signal. Several research works suggest that microwave signal property gets affected after reflecting from the soil surface. The nature of the reflected microwave signal depends on the signal’s penetration depth, which is the factor of water content present in the soil surface. NavIC multipath signal can be used for the estimation of soil moisture using this property. The carrier to noise ratio$(C/N_{\mathrm {o}})$of NavIC signal is used for this purpose. The model proposed in the letter is based on the relationship of estimated multipath phase value with the volumetric moisture content present in the soil surface. The output of the developed model is highly encouraging. A linear relation relationship with a high correlation coefficient value of 0.902 and root mean square error (RMSE) of 4.03% is obtained between ground truth soil moisture and retrieved soil moisture from developed algorithm.

For more than four decades, it has been known that the dielectric constant of the lunar surface can be retrieved from the Fresnel reflection coefficients. However, theoretical models have met with limited success in validating laboratory test results from the Apollo missions to date. This paper is the first study to focus on the use of high-resolution full-polarimetric synthetic aperture radar datasets for the retrieval of the dielectric constant of the lunar surface from the Fresnel reflection coefficients. We initially show that it is possible to retrieve the lunar dielectric constant via the classical Freeman-Durden Decomposition (FDD). The performance of the FDD algorithm is found to be unacceptable over regions with surface slopes and craters, and for sub-surface soil samples. Accurate estimation is not possible by simply replacing the volume scattering model in the FDD with popular and widely used volume scattering models. Therefore, a model-based three-component decomposition (TCD) algorithm for a robust retrieval of the lunar dielectric constant is proposed. The proposed TCD algorithm implements an efficient branching condition combined with double unitary matrix rotations and provides exceptionally accurate dielectric constant estimation. The proposed TCD algorithm is validated by using L band full-polarimetric datasets acquired by the Chandrayaan-2 mission over Apollo 12, Apollo 15, and Apollo 17 landing sites. Comparisons are also made with other three-component decomposition algorithms. Excellent agreement between the estimated values by the proposed TCD algorithm and the reference values for the dielectric constant, available from the literature, has been observed.

A new dielectric constant estimation approach for the lunar surface using Mini-RF synthetic aperture radar (SAR) data and Chandrayaan-2 SAR data is presented in this work. Both the SAR systems are based on a hybrid-polarimetry (hybrid-pol) configuration that transmits a circularly polarized wave and coherently measures the backscattered wave by dual orthogonal linearly polarized channels. From the three-component hybrid-pol SAR decomposition technique, the ratio of the Fresnel reflection coefficients for horizontal and vertical polarization transmission can be estimated. This ratio, which is referred to as the co-polarization ratio, is used by the proposed methodology to find the real value of the dielectric constant. For performance validation, the proposed method is implemented on the Mini-RF hybrid-pol SAR data acquired over Apollo 17 landing sites. The values of the real part of dielectric constant are estimated for six different regions covering the collection sites of six Apollo 17 samples: 72 441, 73 241, 74 241, 75 081, 76 001, and 79 135. The results obtained using the proposed method are found to be in good agreement with the laboratory-measured results of the corresponding samples. Furthermore, the proposed methodology is also implemented on the Chandrayaan-2 hybrid-pol SAR data acquired over theBiot craterregion. Various small areas possessing different possible surface characteristics, situated inside and outside theBiot crater, are being analyzed for validation.

The exploitation of GNSS signals for soil moisture as one of the land applications is the current interest of researchers due to its multiple advantages over existing soil moisture retrieval techniques based on traditional radiometer and other datasets. Multipath phase and amplitude of GNSS C/Nodata have been mostly utilized to determine the sensitivity of soil moisture. However, in this work, we have analyzed the multipath peak frequency to determine its sensitivity for field soil moisture. The multipath frequency is used to consider constant when studies are carried out with multipath phase or amplitude. Here, we have demonstrated that the multipath peak frequency is a function of field soil moisture which can be evaluated efficiently with Lomb Scargle Periodogram (LSP). Navigation with Indian Constellation (NavIC) data has been used to determine the correlation between multipath peak frequency and surface soil moisture. The obtained sensitivity results are very optimistic (correlation coefficient = 0.69), which can be further utilized for developing soil moisture estimation model using NavIC data to cater different land applications.

The possibility of using high resolution SCATSAT-1 data for studying ice calving events in Antarctica has been explored in this study. Two recent calving events in the ice shelves of Amery (2019) and Larsen D (2020) have been observed. These gave birth to icebergs D-28 and A-69 respectively. Enhanced resolution level-4 horizontally polarized daily gamma-0 measurements are used. Canny edge detection technique has been employed to observe these events. The results obtained from the edge detection have been compared with Sentinel-1A Level-1 Ground Range Detected datasets and are found to be in good agreement. A mean difference of 0.2 km (± 3.7 km) is obtained between the two ice front edges (derived and actual).

This paper demonstrates machine learning based approach to retrieve soil moisture (SM) and its validation over India using CYGNSS data. CYGNSS mission is mainly designed and dedicated for monitoring the tropical cyclones over ocean.However, recent developments has highlighted the potential of GNSS-Reflectometry for land applications, specially for SM with high spatio-temporal frequency over traditional satellite data sets. It can be directly utilized to retrieve SM as complementary data to fill the spatial and temporal gaps in satellite microwave radiometer derived SM, like from SMAP and SMOS mission to meet the requirements of high spatial and temporal frequency data sets for agricultural applications. In this work, we developed an Artificial Neural Network (ANN) framework to derive SM and validated at selected agricultural sites over India. SMAP derived vegetation and roughness parameters were also used as inputs for training of ANN model to add the effect of vegetation and roughness. Detailed spatial and temporal correlation analyses of CYGNSS SM were performed to test the proposed ANN model using SMAP SM and in-situ observations from hydra probe station data from 2018 to 2019. It was observed from temporal correlation analysis that CYGNSS and SMAP SM follow a good trend with high correlation using in-situ data. Spatial correlation also shows high correlation with Pearson correlation coefficient of 0.69 and RMSD of 0.057 m3/m3during pre-monsoon and 0.65 and 0.053 m3/m3in post monsoon periods, respectively.

Since the 2007 National Academy of Science “Decadal Survey” report “Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond” [1], the National Aeronautics and Space Administration (NASA) has been studying concepts for a Synthetic Aperture Radar (SAR) mission to determine Earth change in three disciplines - ecosystems, solid earth, and cryospheric sciences. One of the most promising and original concepts involves an innovative international partnership between NASA and the Indian Space Research Organization (ISRO). Previous NASA concepts had focused on exploiting an L-band array-fed reflector SAR configuration that enabled > 200 km swath at full SAR resolution and full polarimetry simultaneously in order to meet requirements in all three disciplines [2]. The feed where the electronics are housed in this design is relatively compact compared to a planar phased array antenna with similar azimuth resolution capability. This compactness allows for straightforward addition of feed array elements at other frequencies. As the partnership concept with ISRO developed, it became clear that flying dual L- and S-band SAR capabilities, with L-band electronics supplied by NASA and S-band electronics by ISRO, would satisfy science and application requirements of the US and India. A dual-frequency fully polarimetric SAR with the potential for global coverage every 12 days would offer unprecedented capability that researchers could exploit in new and exciting ways.

The Multifrequency Scanning Microwave Radiometer (MSMR), flown on-board Oceansat-I (IRS-P4), is a four-frequency and dual-polarization sensor for oceanographic applications. An extensive ground calibration experiment was conducted prior to launch to evaluate calibration coefficients. This paper presents an overview of the on-board system, details of ground calibration methodology, and expected calibration performance derived from ground calibration data.