Jeonghwan Park

The Soil Moisture Active Passive (SMAP) mission provides global soil moisture (SM) observations with high temporal frequency and broad spatial coverage, valuable for hydrological and agricultural applications. However, its coarse spatial resolution can limit direct use in heterogeneous agricultural landscapes where soil moisture varies over short distances. This study developed a multi-stage machine learning framework to improve the spatial applicability of SMAP Level-3 SM across the conterminous United States (CONUS). SMAP brightness temperatures and nighttime land surface temperature (LST) were used as core retrieval-relevant predictors, while vegetation-related controls and soil–vegetation background interactions were incorporated as supplementary information during downscaling and as regional controls during refinement. The Random Forest (RF) based framework first generated 500 m and 250 m downscaled SMAP soil moisture across the CONUS. A 100 m regionally refined soil moisture field was then produced by combining the downscaled SMAP soil moisture with regional environmental drivers. After aggregation to the 9 km SMAP Level-3 product grid, the 500 m downscaled product showed strong consistency with the corresponding SMAP values, with an r² of 0.880 and an RMSE of 0.0292 m³/m³ across the CONUS. Direct comparison with International Soil Moisture Network (ISMN) in situ observations showed RMSE values of up to 0.099 m³/m³ depending on the site network. Following regional refinement based on hydro-topographic, soil, and vegetation-related controls, the overall five-fold cross-validated RMSE was 0.052 m³/m³, indicating improved predictive refinement within the available ISMN station network. These results indicate that multi-stage machine learning downscaling can enhance spatial interpretability and strengthen the applicability of SMAP SM for agricultural monitoring, while preserving broad consistency with the original passive microwave retrieval and providing a practical baseline for regional refinement.

An analysis of a Level-2 (L2) soil moisture record extending from May 1st, 2021 to January 1st, 2024 derived from Spire, Inc.'s Global Navigation Satellite System Reflectometry (GNSS-R) observatories is presented. The product's sensitivity to large scale soil moisture variability is demonstrated using an example of a 2022 flood in Pakistan. Product consistency among the constellation's multiple satellites is also investigated; no clear evidence of inter-satellite biases is observed. Further comparisons are performed with soil moisture datasets from the SMAP (Soil Moisture Active Passive) and CYGNSS (Cyclone Global Navigation Satellite System) missions, from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5), and from in situ ISMN (International Soil Moisture Network) sites. Although an overall product correlation with SMAP soil moistures of approximately 85% is determined, per-pixel correlations vary significantly and per-pixel Root Mean Square Errors (RMSE) can range from 0.02-0.09 (cm $^{3}$ /cm $^{3}$ ) depending on land class. The importance of applying the product's quality flags is also demonstrated. The influence of other calibration effects and inland water body contamination on these results is also discussed.

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL’s UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire “burn” detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as “burned” compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance.

The NASA ISRO Synthetic Aperture Radar (NISAR) mission scheduled for launch in 2024 will provide global L-band radar observations that can be applied to estimate land surface soil moisture. The mission's soil moisture product will be provided at 200 m resolution with a global revisit frequency of 12 days (or 6 days when considering both ascending and descending observations). A time-series ratio algorithm for soil moisture retrieval has been applied to NISAR simulated datasets from airborne UAVSAR measurements in the SMAPVEX12 field campaign. Soil moisture retrieval performance using the algorithm is encouraging, with a correlation coefficient between retrievals and in situ observations greater than 0.7 and an unbiased Root Mean Squared Error (RMSE) of 0.05 m ³ / m ³ The results suggest that the time series ratio algorithm will provide soil moisture products that meet an accuracy goal of 0.06 m ³ / m ³ unbiased RMSE.

The use of a “time-series ratio” soil moisture retrieval approach is under consideration for the upcoming NISAR mission’s soil moisture product. As such, it is of interest to characterize the algorithm’s anticipated error budget as part of pre-launch activities. This paper develops an error model to estimate retrieval errors for the proposed algorithm. The model accounts for error contributions from speckle and thermal noise as well as uncertainties that arise as part of the retrieval process. Spatial and temporal behaviors of the retrieval errors are determined using a SMAP-based soil moisture climatology. The results show that soil moisture retrieval errors from the time-series ratio method meet the 0.06 m ³ /m ³ unbiased root mean square error (URMSE) performance metric established for the NISAR soil moisture product.

The launch of the CYGNSS constellation in 2016 represents NASA's first mission dedicated to the acquisition of GPS signals reflected off the earth’s surface. The major geophysical measurement objective is ocean surface winds with particular emphasis placed on the improved characterization of tropical cyclones. The conventional method for retrieving ocean surface winds using spaceborne Global Navigation Satellite System Reflectometry systems, of which CYGNSS is one example, is to measure the reflected power from the specular point on the ocean surface. Accurate measurement of surface power is complicated by calibration of the various power levels from the transmitting satellites, geometric effects, receiving system RF chain characteristics, among others. This article applies an alternative trailing edge slope (TES) method, which is more immune to end-to-end calibration uncertainties. It is nonetheless noted that because TES is a fundamentally shape-based measure of the waveform, it is susceptible to factors that may distort the measurements' slopes. This included measurements made with low SNR (on the order of 1.5 dB or less) due to low level of surface scattering or observations within the low gain portion of the receive antenna pattern. For this reason, in this work retrieval attempts are largely limited to those with a receive gain on the order of 10 dB or better. The TES method is based on the slope of the measured signal as a function of delay. Given the need for a delay space that extends beyond the $\approx$ 2.5–3.5 chips provided by CYGNSS's standard Level-1 measurements, its utility is explored using one of the constellation's special “full DDM” downlink modes. The TES method is shown to provide competitive retrievals including measurements from the surface, where the conventional method provides ocean wind speed estimates of lower accuracy. Tradeoffs using the TES method are discussed as well as simple improvements in the method are given. It is also noted that because with TES, measurements' slopes are the fundamental observable no calibration of measurements is required.

A novel approach in addressing cyclone global navigation satellite system (CyGNSS) intersatellite and GPS-related calibration issues is proposed, based on a track-wise $\sigma ^{o}$ bias correction method. This method makes use of both ancillary data from numerical weather prediction models and a semiempirical geophysical model function. Care is taken, so the track-wise $\sigma ^{o}$ bias correction maintains CyGNSS signal sensitivity. Both intersatellite and GPS-related calibration issues are removed after correction. Long-term $\sigma ^{o}$ downward trend, observed throughout the CyGNSS mission, is greatly reduced. Using the corrected $\sigma ^{o}$ measurements, a wind retrieval method is also presented and its product thoroughly assessed for a three-year period against European Centre for Medium-Range Weather Forecasts (ECMWFs), Advanced Scatterometer (ASCAT) A/B, Advanced Microwave Scanning Radiometer (AMSR)-2, GMI, WindSat, hurricane weather research and forecasting (HWRF) model, and the stepped frequency microwave radiometer (SFMR) winds. The overall wind speed bias and standard deviation of the error (stde) against ECMWF are 0.16 and 1.19 m/s, while these are −0.11 and 1.12 m/s against ASCAT A/B, respectively. The same metrics against AMSR-2/GMI/WindSat (combined) are −0.19 and 1.11 m/s, respectively. The bias and stde against soil moisture active passive (SMAP) are −0.38 and 1.90 m/s, respectively. In the tropical cyclone environment, the bias and stde against HWRF are −0.54 and 2.90 m/s, and −4.71 and 5.88 m/s with SFMR. Finally, CyGNSS wind performance is gauged in the presence of rain. Below 10 m/s, the bias between CyGNSS and ECMWF increases as the rain rate increases. Between 10 and 15 m/s, biases are mostly absent. Above 15 m/s, results are inconclusive due to the low number of collocated rain samples. Overall, the presented CyGNSS wind speed product both exhibits consistency and reliability, showing promise of using GNSS-R derived winds for operational purposes.

The Cyclone Global Navigation Satellite System (CYGNSS) is a spaceborne GNSS-reflectometry mission, which was launched on December 15, 2016 for ocean surface wind speed measurement. CYGNSS includes eight small satellites in the same low earth orbit, so that the mission provides wind speed products having unprecedented coverage both in time and space to study multitemporal behaviors of oceanic winds. The nature of CYGNSS coverage results in some locations on earth experiencing multiple wind speed measurements within a short period of time (a “clump” of observations in time) resulting in a “rapid revisit” series of measurements. Such observations seemingly can provide indications of regions experiencing rapid changes in wind speeds, and therefore serve as an indicator of convective activity. An initial investigation of this concept using simulated and on-orbit CYGNSS measurements is provided in this paper. The temporally “clumped” properties of CYGNSS measurements are examined, and the results show that clump durations and spacing vary with latitude. For example, the duration of a clump can extend as long as a few hours at higher latitudes, with gaps between clumps ranging from 6 to as high as 12 h depending on latitude. Initial examples are provided to indicate the potential of changes within a clump to detect convective activity through a comparison with convective activity indicators derived from model datasets. The results at present are limited by the ongoing calibration of CYGNSS wind speed retrievals, so that future work will be required to obtain a more complete assessment, but nevertheless clearly indicate the potential utility of the method for studies of atmospheric convection.