This study provides a theoretical/physical framework to quantify the vegetation scattering effects on radiometric microwave measurements of soil moisture. The model development and analysis is ...presented to assess the limitations of the existing τ−ω (tau-omega) model with respect to vegetated landscapes and thus to extend the usefulness of the τ−ω model to a wider range of vegetation conditions. An explicit expression is driven for an effective albedo of vegetated terrain from the zero- and multiple-order radiative transfer solutions. The formulation establishes a direct physical link between the effective vegetation parameterization and the theoretical description of absorption and scattering within the canopy. Evaluation of the derived albedo for corn canopies (stem-dominated vegetation) with data taken during the Huntsville 1998 field experiment (Hsv98) are shown and discussed. The simulation results are in good agreement with the data and show that the effective albedo values are significantly smaller than the single-scattering albedo values and increase monotonically as soil moisture increases. The model is also used to simulate effective albedo from a soybean canopy (leaf dominated vegetation) at L-band. Both results illustrate that the fitted albedo values, which are found in the literature, represent effective albedo values rather than the single-scattering albedo values.
► Assessing limitations of the tau-omega model with respect to vegetation scattering. ► Relating effective albedo to single-scattering albedo explicitly. ► Demonstrating soil moisture dependence of effective albedo.
This paper presents a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The goal of the proposed novel ...method is to advance CYGNSS-based SM estimations, exploiting the spatio-temporal resolution of the GNSS reflectometry (GNSS-R) signals to its highest potential within a machine learning framework. The methodology employs a fully connected Artificial Neural Network (ANN) regression model to perform SM predictions through learning the nonlinear relations of SM and other land geophysical parameters to the CYGNSS observables. In situ SM measurements from several International SM Network (ISMN) sites are used as reference labels; CYGNSS incidence angles, derived reflectivity and trailing edge slope (TES) values, as well as ancillary data, are exploited as input features for training and validation of the ANN model. In particular, the utilized ancillary data consist of normalized difference vegetation index (NDVI), vegetation water content (VWC), terrain elevation, terrain slope, and h-parameter (surface roughness). Land cover classification and inland water body masks are also used for the intermediate derivations and quality control purposes. The proposed algorithm assumes uniform SM over a 0.0833 ∘ × 0.0833 ∘ (approximately 9 km × 9 km around the equator) lat/lon grid for any CYGNSS observation that falls within this window. The proposed technique is capable of generating sub-daily and high-resolution SM predictions as it does not rely on time-series or spatial averaging of the CYGNSS observations. Once trained on the data from ISMN sites, the model is independent from other SM sources for retrieval. The estimation results obtained over unseen test data are promising: SM predictions with an unbiased root mean squared error of 0.0544 cm 3 /cm 3 and Pearson correlation coefficient of 0.9009 are reported for 2017 and 2018.
Soil moisture (SM) derived from satellite-based remote sensing measurements plays a vital role for understanding Earth’s land and near-surface atmosphere interactions. Bistatic Global Navigation ...Satellite System (GNSS) Reflectometry (GNSS-R) has emerged in recent years as a new domain of microwave remote sensing with great potential for SM retrievals, particularly at high spatio-temporal resolutions. In this work, a machine learning (ML)-based framework is presented for obtaining SM data products over the International Soil Moisture Network (ISMN) sites in the Continental United States (CONUS) by leveraging spaceborne GNSS-R observations provided by NASA’s Cyclone GNSS (CYGNSS) constellation alongside remotely sensed geophysical data products. Three widely-used ML approaches—artificial neural network (ANN), random forest (RF), and support vector machine (SVM)—are compared and analyzed for the SM retrieval through utilizing multiple validation strategies. Specifically, using a 5-fold cross-validation method, overall RMSE values of 0.052, 0.061, and 0.065 cm3/cm3 are achieved for the RF, ANN, and SVM techniques, respectively. In addition, both a site-independent and a year-based validation techniques demonstrate satisfactory accuracy of the proposed ML model, suggesting that this SM approach can be generalized in space and time domains. Moreover, the achieved accuracy can be further improved when the model is trained and tested over individual SM networks as opposed to combining all available SM networks. Additionally, factors including soil type and land cover are analyzed with respect to their impacts on the accuracy of SM retrievals. Overall, the results demonstrated here indicate that the proposed technique can confidently provide SM estimates over lightly-vegetated areas with vegetation water content (VWC) less than 5 kg/m2 and relatively low spatial heterogeneity.
This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high ...spatio-temporal resolution observations over the tropics (within ±38° latitudes) at L-band. The learning model is trained by using in-situ SM data from the International Soil Moisture Network (ISMN) sites and various space-borne ancillary data. The approach produces daily SM retrievals that are gridded to 3 km and 9 km within the CYGNSS spatial coverage. The performance of the model is independently evaluated at various temporal scales (daily, 3-day, weekly, and monthly) against Soil Moisture Active Passive (SMAP) mission’s enhanced SM products at a resolution of 9 km × 9 km. The mean unbiased root-mean-square difference (ubRMSD) between concurrent (same calendar day) CYGNSS and SMAP SM retrievals for about three years (from 2017 to 2019) is 0.044 cm3 cm−3 with a correlation coefficient of 0.66 over SMAP recommended grids. The performance gradually improves with temporal averaging and degrades over regions regularly flagged by SMAP such as dense forest, high topography, and coastlines. Furthermore, CYGNSS and SMAP retrievals are evaluated against 170 ISMN in-situ observations that result in mean unbiased root-mean-square errors (ubRMSE) of 0.055 cm3 cm−3 and 0.054 cm3 cm−3, respectively, and a higher correlation coefficient with CYGNSS retrievals. It is important to note that the proposed approach is trained over limited in-situ observations and is independent of SMAP observations in its training. The retrieval performance indicates current applicability and future growth potential of GNSS-R-based, directly measured spaceborne SM products that can provide improved spatio-temporal resolution than currently available datasets.
Global Navigation Satellite Systems (GNSS) Reflectometry (GNSS-R) has gained significant attention in retrieving geophysical parameters of the Earth's surface using ground, airborne, and spaceborne ...systems in the past decade. Such studies have mainly been investigated through custom-built systems or networks of geodetic receivers and antennas. For the broader adaptation of such an approach in precision agriculture or small-scale experiments, we have recently conjectured that a smartphone's built-in GNSS chip and antenna mounted on a small Unmanned Aircraft System (UAS) platform could be used to estimate the reflectivity of the soil surface using reflected GNSS signals. The main barrier to using a smartphone as a ubiquitous GNSS-R receiver is the built-in antenna's irregular radiation pattern that makes the measurement signal highly angular dependent. This study provides a unique and practical solution to lessen the impact of antenna radiation patterns on reflectivity estimation by spinning two smartphones mounted on two separate ground plate and taking the logarithmic difference of such simultaneous measurements. In this proposed configuration, a down-facing spinning smartphone on a UAS platform collects reflected signals. At the same time, another identical spinning smartphone is located on the ground, providing reference data in an open area. We compared the results from measurements with the spinning smartphone on a small UAS and the ground. We also discuss the trade-offs involved in rotation and flight dynamics. Our findings show that a ubiquitous GNSS-R system based on spinning smartphones operating from a small UAS platform can estimate surface reflectivity at the sub-field scale.
In this study, we conducted an empirical investigation on mobile global navigation satellite system (GNSS) transmissometry (GNSS-T) measurements to explore vegetation optical depth (VOD). Our ...approach involved using a dual-receiver setup, with one receiver located in open terrain to capture direct signals as a reference and another deployed on an unmanned ground vehicle (UGV) to sample vegetation across expansive forested regions. Noteworthy findings reveal the negligible influence of ground multipath effects within these forested terrains, effectively resulting in sampling the forest canopy rather than the ground itself as the receiver moves. The UGV-based method also uncovers VOD fluctuations inside the forest, offering insights into spatial distribution and the influence of satellite position on VOD measurements. The study further examines the effect of tree heterogeneity and seasonal dynamics on the VOD estimates. This empirical study contributes to our understanding of the VOD mapping capabilities of the mobile GNSS-T approach and can potentially lead to nonintrusive quantification of vegetation water content at a landscape scale in forest terrains. These results are significant for advancing our knowledge of forest ecosystem dynamics and sustainable resource management.
National Aeronautics and Space Administration's Cyclone Global Navigation Satellite System (CYGNSS) mission has gained significant attention within the land remote sensing community for estimating ...soil moisture (SM) by using the Global Navigation System Reflectometry (GNSS-R) technique. CYGNSS constellation generates Delay-Doppler Maps (DDMs), containing important Earth surface information from GNSS reflection measurements. Previous studies considered only designed features from CYGNSS DDM, whereas the whole DDM image is affected by SM, inundation, and vegetation. This paper presents a deep learning (DL) based framework for estimating SM in the Continental United States by leveraging spaceborne GNSS-R DDM observations provided by the CYGNSS constellation along with remotely sensed geophysical data. A data-driven approach utilizing convolutional neural networks (CNNs) is developed to determine complex relationships between the reflected measurements and surface parameters which can provide improved SM estimation. The model is trained jointly with three types of processed DDM images of analog power, effective scattering area, and bistatic radar cross-section with other auxiliary geophysical information such as elevation, soil properties, and vegetation water content (VWC). The model is trained and evaluated using the Soil Moisture Active Passive (SMAP) mission's enhanced SM products at a 9 km resolution with VWC less than 5 kg/m 2 . The mean unbiased root-mean-square difference between concurrent CYGNSS and SMAP SM retrievals from 2017 to 2020 is 0.0366 m 3 /m 3 with a correlation coefficient of 0.93 over fivefold cross-validation and 0.0333 m 3 /m 3 with a correlation coefficient of 0.94 over year-based cross-validation at spatial resolution of 9 km and temporal resolution similar to CYGNSS mission.
The accuracy of geophysical retrievals from radiometers relies on calibration quality, encompassing both absolute radiometric accuracy and spectral consistency. Radiometers have employed various ...calibration techniques, including external targets, vicarious sources, and internal calibrators like noise diodes or matched reference loads. Calibration techniques face challenges like frequency dependence, instrumental effects, environmental influences, drift, aging, and radio frequency interference. Recent hardware advancements enable radiometers to collect raw samples containing both temporal and spectral information. Leveraging advanced modeling techniques like deep learning (DL) enables detecting subtle correlations, non-linear dependencies, and higher-order interactions within the data extracting valuable information that may have been challenging with conventional methods. This study utilizes NASA's Soil Moisture Active Passive (SMAP) satellite's level 1A and level 1B data products to develop a DL-based radiometer calibrator to estimate antenna temperature. Spectrograms of second raw moments equivalent to power carrying the 2-D spectral features serve as primary input in a supervised convolutional neural network-based architecture. DL-based calibrator has demonstrated high correlation and low root mean square error when incorporating spectral information from both reference and noise diodes and when not considering this information. Findings suggest that the ancillary features such as internal thermistor temperature and loss elements exhibit sufficient accuracy in estimating antenna temperature to compensate for variations in receiver noise temperature and short-term gain fluctuations in the absence of the reference load and noise diode power. The proposed calibration technique with reduced reference information might enable radiometers for a higher number of antenna scene observations within a footprint.
Passive remote sensing is a crucial technology for climate studies and Earth science. NASA's soil moisture active passive (SMAP) is a remote sensing observatory that uses passive microwave radiometer ...measurements to estimate soil moisture and detect the freeze or thaw state. Despite operating in the protected band of the radio spectrum (1400-1427 MHz), the radiometer's measurements are nonetheless tainted by radio frequency interference (RFI). An increasing number of radio-frequency transmissions such as those from air surveillance radars, 5G wireless communications, and unmanned aerial vehicles are contributing to RFI through either out-of-band emissions or operating in-band illegally. Physical modeling to detect RFI globally might prove to be challenging as RFI can be generated from single as well as multiple sources and these can be divided as pulsed or continuous wave RFI. In this study, a deep learning (DL) based RFI detection method is proposed with a novel convolutional neural network framework that can detect different types of RFI on a global scale. This is a data-driven approach where the detection framework learns directly from the SMAP data products to make a decision whether a certain footprint is RFI contaminated or not. SMAP's level 1A data products containing antenna counts of different raw moments along with Stokes parameters are used in this study to produce spectrograms and level 1B data products containing the quality flags are used to dynamically label those spectrograms. This study's robust DL framework provided the highest accuracy with the raw moments of horizontal polarization (99.99%) to detect RFI globally.
We investigate the feasibility of using built-in GNSS sensors within ubiquitous smartphone devices from a small UAS for the purpose of land remote sensing. We summarize the experimental findings and ...challenges that need to be resolved in order to perform the GNSS reflectometry (GNSS-R) technique via smartphones. In late 2018, a series of experiments were conducted and designed by integrating two smartphones into a multicopter UAS by attaching them to ground plates to isolate and record both direct and reflected GNSS carrier-to-noise density ratio (C/N_0) separately. It was demonstrated that, first, fluctuations of moving GNSS specular reflections are correlated with spatial ground features with appreciable dynamic range and second, radiation pattern of the smartphone's inbuilt antenna has a significant effect on the received signal strength. In 2020, more experiments were conducted to examine the quality of in-built chip and antenna of a smartphone with regard to the GNSS-R approach as well as the consistency of measurements. These follow-up experiments involved, first, placement of the smartphone on a pan-tilt mechanism on a tripod, second, formation flights with smartphone on a gimbal and a high-quality custom-built dual-channel GNSS-R receiver, and, third, flying the UAS at different times of the day on two consecutive days. It was demonstrated that, first, the radiation pattern of the smartphone's GNSS antenna are observed to be highly irregular, but time-invariant, and, second, internal GNSS chip produces observables of sufficient quality, and, third, the fluctuations of the reflected signal are repeatable under the same configuration at different times.
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