A5100322106- Soil Moisture and Remote Sensing
- Precipitation Measurement and Analysis
- Cryospheric studies and observations
- Hydrology and Watershed Management Studies
- Climate change and permafrost
- Soil and Unsaturated Flow
- Hydrological Forecasting Using AI
- Meteorological Phenomena and Simulations
- Landslides and related hazards
- Grey System Theory Applications
- Stochastic Gradient Optimization Techniques
- earthquake and tectonic studies
- Statistical Methods and Inference
- Electromagnetic Simulation and Numerical Methods
- Transportation Planning and Optimization
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Urban Transport Systems Analysis
- Arctic and Antarctic ice dynamics
- Remote Sensing and LiDAR Applications
- Geophysical Methods and Applications
- Lattice Boltzmann Simulation Studies
- Markov Chains and Monte Carlo Methods
- Environmental Impact and Sustainability
- Resource-Constrained Project Scheduling
- Climate variability and models
Agricultural Research Service
2012-2024
Science Systems and Applications (United States)
2011-2024
China University of Mining and Technology
2023
Qingdao University of Technology
2022
Beltsville Agricultural Research Center
2017-2021
United States Department of Agriculture
2016-2021
University of Nottingham
2019-2020
United States Department of State
2018-2019
The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. observatory developed to provide global mapping of high-resolution soil moisture freeze-thaw state every two three days using an L-band (active) radar (passive) radiometer. After irrecoverable hardware failure the July 7, 2015, radiometer-only product became only operational for SMAP. provides estimates posted a 36 km Earth-fixed grid produced...
The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, defined a set criteria for core validation sites (CVS) that enable testing key SM accuracy requirement (unbiased root-mean-square error <0.04 m<sup>3</sup>/m<sup>3</sup>). approach also includes other (“sparse network”) <i>in situ</i> measurements,...
The validation of the soil moisture retrievals from recently launched National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) satellite is important prior to their full public release. Uncertainty in attempts characterize footprint-scale surface-layer using point-scale ground observations has generally limited past remotely sensed products densely instrumented sites covering an area approximating footprint. However, by leveraging independent information...
The soil moisture active passive (SMAP) mission was designed to acquire L-band radiometer measurements for the estimation of (SM) with an average ubRMSD not more than 0.04 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m volumetric accuracy in top 5 cm vegetation a water content less kg/m xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Single-channel algorithm (SCA) and dual-channel (DCA) are implemented processing SMAP data....
Abstract Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating strength relationship between storm‐scale runoff ratio (i.e., total streamflow divided rainfall accumulation depth units) prestorm surface estimates from a range products. Results demonstrate that both satellite‐based, L band microwave radiometry...
Accurate partitioning of precipitation into infiltration and runoff is a fundamental objective land surface models tasked with characterizing the water energy balance. Temporal variability in this due, part, to changes pre-storm soil moisture, which determine capacity unsaturated storage. Utilizing NASA Soil Moisture Active Passive Level-4 moisture product combination streamflow observations, we demonstrate that (LSMs) generally underestimate strength positive rank correlation between event...
Abstract Uncertainties in precipitation forcing and prestorm soil moisture states represent important sources of error streamflow predictions obtained from a hydrologic model. An earlier synthetic twin experiment has demonstrated that both antecedent rainfall can be filtered by assimilating remotely sensed surface retrievals. This opens up the possibility applying satellite estimates to address key model predictions. Here, an attempt extend analysis into real-data environment, two...
Data assimilation is the application of Bayes' theorem to condition states a dynamical systems model on observations. Any real-world approximate, and therefore we cannot expect that data will preserve all information available from models We outline framework for measuring in models, observations, evaluation way allows us quantify loss during (necessarily imperfect) assimilation. This facilitates quantitative analysis tradeoffs between improving (usually expensive) remote sensing observing...
Abstract Despite extensive efforts to maximize ground coverage and improve upscaling functions within core validation sites (CVS) of the NASA Soil Moisture Active Passive (SMAP) mission, spatial averages point-scale soil moisture observations often fail accurately capture true average reference pixels. Therefore, some level pixel-scale sampling error from in situ must be considered during SMAP retrievals. Here, uncertainties site (CSASM) due errors are examined their impact on CSASM-based...
In August 2020, soil moisture active passive (SMAP) released a new version of its and vegetation optical depth (VOD) retrieval products. this article, we review the methodology followed by SMAP regularized dual-channel algorithm. We show that implementation generates SM retrievals not only satisfy accuracy requirements, but also performance comparable to single-channel algorithm uses <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i>...
Abstract The physical parameterization of key processes in land surface models (LSMs) remains uncertain, and new techniques are required to evaluate LSMs accuracy over large spatial scales. Given the role soil moisture partitioning water fluxes (between infiltration, runoff, evapotranspiration), (SSM) estimates represent an important observational benchmark for such evaluations. Here, we apply SSM from NASA Soil Moisture Active Passive Level‐4 product (SMAP_L4) diagnose bias correlation...
Abstract Mass balance analysis of ice sheets is a key component to understand the effects global warming. A significant sheet and shelf mass iceberg calving, which can generate large tsunamis endangering human beings coastal infrastructure. Such iceberg-tsunamis have reached amplitudes 50 m destroyed harbours. Calving icebergs interact with surrounding water through different mechanisms we investigate five; A: capsizing, B: gravity-dominated fall, C: buoyancy-dominated D: overturning E:...
Abstract Satellite-based precipitation estimates (SPEs) are generally validated using ground-based rain gauge or radar observations. However, in poorly instrumented regions, uncertainty these references can lead to biased assessments of SPE accuracy. As a result, at regional continental scales, an objective basis evaluate SPEs is currently lacking. Here, we the potential for large-scale, spatially continuous evaluation over land via application collocation-based techniques [i.e., triple...
Using historical satellite surface soil moisture products, the Soil Moisture Analysis Rainfall Tool (SMART) is applied to improve submonthly scale accuracy of a multi-decadal global daily rainfall product that has been bias-corrected match monthly totals available rain gauge observations. In order adapt irregular retrieval frequency heritage new variable correction window method developed allows for better efficiency in leveraging temporally sparse retrievals. Results confirm advantage using...
NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days using L-band (active) radar (passive) radiometer. The SMAP radiometer began acquiring routine science data March continues operate nominally. SMAP's radiometer-derived product (L2_SM_P) provides estimates posted 36 km fixed Earth grid brightness temperature...
Since the beginning of its routine science operation in March 2015, NASA SMAP observatory has been returning interference-mitigated brightness temperature observations at L-band (1.41 GHz) frequency from space. The resulting data enable frequent global mapping soil moisture with a retrieval uncertainty below 0.040 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m 36 km spatial scale. This paper describes development and validation an...
The Soil Moisture Active Passive (SMAP) mission is designed to acquire L-band radiometer measurements for the estimation of soil moisture with 0.04 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m volumetric accuracy in top 5 cm vegetation water content less than kg/m xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . In regions near coast or inland bodies water, signal measured by SMAP contains emissions from land and resulting...