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NASA NTRS2022

Remote sensing-based vegetation and soil moisture constraints reduce irrigation estimation uncertainty

Understanding the human water footprint and its impact on the hydrological cycle is essential to inform water management under climate change. Despite efforts in estimating irrigation water withdrawals in earth system models, uncertainties and discrepancies exist within and across modeling systems conditioned by model structure, irrigation parameterization, and the choice of input datasets. Achieving model reliability could be much more challenging for data-sparse regions, given limited access to ground truth for parameterization and validation. Here, we demonstrate the potential of utilizing remotely sensed vegetation and soil moisture observations in constraining irrigation estimation in the Noah-MP land surface model. Results indicate that the two constraints together can effectively reduce model sensitivity to the choice of irrigation parameterization by 7%–43%. It also improves the characterization of the spatial patterns of irrigation and its impact on evapotranspiration and surface soil moisture by correcting for vegetation conditions and irrigation timing. This study highlights the importance of utilizing remotely sensed soil moisture and vegetation measurements in detecting irrigation signals and correcting for vegetation growth. Integrating the two remote sensing datasets into the model provides an effective and less feature engineered approach to constraining the uncertainty of irrigation modeling. Such strategies can be potentially transferred to other modeling systems and applied to regions across the globe.

Wanshu Nie↗
NASA NTRSDate not supplied

Oregon Wildfires: Integrating ECOSTRESS to Map & Analyze Vegetation Moisture for Wildfire Modeling

Wildfire season in the western USA is starting earlier and gaining in intensity. The Bootleg Fire in Southern Oregon began on July 6th, 2021, and burned over 1675 km2 before it was fully contained on August 15th, 2021. Evapotranspiration (ET) is one indicator of vegetation moisture and there is interest in using high-resolution ET products from ECOsystem and Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) in future wildfire modeling. In partnership with the Pacific Northwest National Laboratory and US Forest Service, the team examined ECOSTRESS ET for the two years before the Bootleg Fire and assessed the relationship between ET, topography, and vegetation. Remotely sensed data from Shuttle Radar Topography Mission (SRTM) and Global Ecosystem Dynamics Investigation (GEDI) along with ancillary data from the National Land Cover Database (NLCD) and Landscape Fire Resource Management Planning Tools (LANDFIRE) were incorporated. The team examined data in relation to soil burn severity from the Burned Area Emergency Response (BAER) program. From ET median composites for April 1st – July 5th, 2021 and 2019, the Bootleg Fire area showed a 7 mm/day decrease in ET and a relative 75% decrease in ET between 2019 and 2021. Approximately 6% of the Bootleg Fire area was identified as having a high soil burn severity and these areas were found predominantly in the evergreen forest land cover class and northward facing slopes with a mean ET decrease of 3 mm/day between 2019 and 2021. The team also analyzed ECOSTRESS Water Use Efficiency products as an additional vegetation moisture indicator of pre-fire conditions in the study area. The end products will allow the partners to assess if higher resolution vegetation moisture datasets from ECOSTRESS will improve wildfire modeling for other susceptible areas.

Brenna Hatch↗
NASA NTRS2022

Towards Effective Drought Monitoring in the Middle East and North Africa (Mena) Region: Implications From Assimilating Leaf Area Index and Soil Moisture Into the Noah-Mp Land Surface Model for Morocco

The Middle East and North Africa (MENA) region has experienced more frequent and severe drought events in recent decades, leading to increasingly pressing concerns over already strained food and water security. An effective drought monitoring and early warning system is thus critical to support risk mitigation and management by countries in the region. Here we investigate the potential for assimilation of leaf area index (LAI) and soil moisture observations to improve the representation of the overall hydrological and carbon cycles and drought by an advanced land surface model. The results reveal that assimilating soil moisture does not meaningfully improve model representation of the hydrological and biospheric processes for this region, but instead it degrades the simulation of the interannual variation in evapotranspiration (ET) and carbon fluxes, mainly due to model weaknesses in representing prognostic phenology. However, assimilating LAI leads to greater improvement, especially for transpiration and carbon fluxes, by constraining the timing of simulated vegetation growth response to evolving climate conditions. LAI assimilation also helps to correct for the erroneous interaction between the prognostic phenology and irrigation during summertime, effectively reducing a large positive bias in ET and carbon fluxes. Independently assimilating LAI or soil moisture alters the categorization of drought, with the differences being greater for more severe drought categories. We highlight the vegetation representation in response to changing land use and hydroclimate as one of the key processes to be captured for building a successful drought early warning system for the MENA region.

Wanshu Nie↗
NASA NTRS2022

Time-Series Ratio Algorithm for Nisar Soil Moisture Retrieval

The NASA ISRO Synthetic Aperture Radar (NISAR) mission is currently under development and will provide global L-band radar observations that will be helpful for various soil moisture applications. The final NISAR soil moisture product will have 200m spatial resolution with 12-day exact revisit time. A time-series ratio algorithm was implemented using NISAR simulated UAVSAR data collected during the SMAPVEX12 field experiment. In this paper, the performance of the time series ratio algorithm was assessed using in situ observations. Performance of the soil moisture retrieval algorithm was also assessed for dual polarization and quad-polarization observations modes.

Jeonghwan Park↗
NASA NTRS2022

SMAP Soil Moisture Assimilation To Enhance Streamflow Estimates Across South Asia

Streamflow estimation across areas facing water scarcity is important considering the evolving climatic conditions. In this study, the impact of assimilating Soil Moisture Active Passive (SMAP) soil moisture retrievals on the modeled streamflow across three main river basins in South Asia is explored. Model estimated runoff was hydraulically routed using the HYMAP routing model within the NASA Land Information System (LIS) framework. Streamflow estimates via soil moisture assimilation highlighted the potential improvements in streamflow across low-flow river tributaries in irrigated regions. Limited relative change was noted along high-flow river tributaries. Modeled streamflow tended to underestimate the flow magnitude at upstream stations and overestimated the streamflow at downstream stations within the Indus basin due to missing physics related to reservoir operation. Improvement of the modeling systems representativeness of the ground conditions by the inclusion of water management information would potentially improve the current streamflow modeling capability across South Asia.

Jawairia Ahmad↗
NASA NTRS2022

Correction of Path Integrated Attenuation Estimates Considering the Soil Moisture Effect for the Gpm Dual-Frequency Precipitation Radar

Spaceborne precipitation radars, including the Tropical Rainfall Measuring Mission’s Precipitation Radar (PR) and the Global Precipitation Measurement Mission’s Dual-Frequency Precipitation Radar (DPR), measure not only precipitation echoes but surface echoes as well, the latter of which are used to estimate the path-integrated attenuation (PIA) in the surface reference technique (SRT). In our previous study based on analyzing PR measurements, we found that attenuation-free surface backscattering cross sections (denoted by s0e) over land increased in the presence of precipitation. This behavior, called the soil moisture effect, causes an underestimate of the PIA by the SRT as the method does not explicitly consider this effect. In this study, measurements made by Ku-band Precipitation Radar (KuPR) and Ka-band Precipitation Radar (KaPR), which comprise the DPR, were analyzed to examine whether KuPR and KaPR exhibit similar dependencies on the soil moisture as does the PR. For both KuPR and KaPR, an increase in s0e was observed for a large portion of the land area, except for forests and deserts. Results from the Hitschfeld–Bordan (HB) method suggest that s0e increases with the surface precipitation rate for light precipitation events. Meanwhile, for heavy precipitation, owing to the degradation of the HB method, it is difficult to estimate s0e quantitatively. Thus, a correction method for PIA that considers the soil moisture effect was developed and implemented into the DPR standard algorithm. With this correction, the surface precipitation rate estimates increased by approximately 18%for KuPR and 15%for the normal scan of KaPR over land.

Shinta Seto↗
NASA NTRS2023

Assimilation of Blended Satellite Soil Moisture Data Products to Further Improve Noah-MP Model Skills

Microwave satellite remote sensing has enabled observations of soil moisture (SM) at the global scale, and multiple SM data products have been developed in the past decades. However, single-sensor-based measurements are insufficient for continuous spatiotemporal coverage. In the context of its climate program, the Climate Change Initiative, the European Space Agency (ESA) has developed robust, long term, global scale, multi instrument satellite derived time series of climate data record for key component of the climate system, including soil moisture (CCI), while the Soil Moisture Operational Product System (SMOPS) was specifically developed by National Oceanic and Atmospheric Administration (NOAA) to offer the real time blended SM datasets through merging all available individual products. Before combining, all individual SM data ingested into both SMOPS and CCI blended products are scaled to Global Land Data Assimilation System (GLDAS) 0-10 cm SM climatology. Benefiting from land surface model evolution and the availability of high-quality forcing data, GLDAS has become more comprehensive to track SM changes and dynamic trends. The development of GLDAS and the scaling procedure in CCI and SMOPS leave an open scientific and operational question: do the blended satellite SM data products have added value comparing to the GLDAS product? This study clearly reveals that both CCI and SMOPS can provide the reliable SM observations with independent information, although their climatology matches well with GLDAS. Relative to assimilation of GLDAS 0-10 cm SM data, Noah-MP model can be further improved by assimilating the blended satellite SM observations with respect to the quality-controlled in situ measurements. The strong consistency of results presented in this paper proves that the blended satellite SM data products are more useful than the GLDAS product in terms of improving Noah-MP model performance.

Jifu Yin↗
NASA NTRS2002

On the relationshop between polarimetric parameters and soil moisture

In this paper, we examine the relationship between polarimetric parameters and soil moisture. For example, the co-polarization ratio is independent of surface roughness to the first order of the small perturbation approximation. As surface roughness increases, it can be shown that the co-polarization ratio depends on the surface slope under the tilted Bragg approximation. The coupling of the surface roughness to the co-polarization ratio is theoretically investigated. An algorithm to compensate the surface slope effect will be discussed. We will also study other polarimetric parameters such as the average alpha angle and eigenvalues to understand their relationship with soil moisture.

polarimetric parameters soil moisture↗
NASA NTRS1996

Estimation of Moisture Content of Forest Canopy and Floor from SAR Data Part I: Volume Scattering Case

To understand and predict the functioning of forest biomes, their interaction with the atmosphere, and their growth rates, the knowledge of moisture content of their canopy and the floor soil is essential. The synthetic aperture radar on airborne and spaceborne platforms has proven to be a flexible tool for measuring electromagnetic back- scattering properties of vegetation related to their moisture content.

Forest Canopy moisture content forest floor synthe↗
NASA NTRS1994

Passive Microwave Remote Sensing of Soil Moisture

Microwave remote sensing provides a unique capability for direct observation of soil moisture... This Paper outlines the basic principles of the passive microwave technique for soil moisture sensing, and reviews briefly the status of current retrieval methods.

Microwave sensing soil sensing soil moisture hydro↗
NASA NTRS1994

Retrieval of Soil Moisture Profile by Combined Remote Sensing and Modeling

The objective of this paper is to develop a statistically optimal assimilation algorithm which estimates the entire system state, i.e., vertical soil moisture and temperature profiles, from information which is only available for portion of the system (namely, the near-surface soil moisture and temperature).

soil moisture profiles↗
NASA NTRS2006

Compact, Lightweight Dual-Frequency Microstrip Antenna Feed for Future Soil Moisture and Sea Surface Salinity Missions

The development of a compact, lightweight, dual-frequency antenna feed for future soil moisture and sea surface salinity (SSS) missions is described. The design is based on the microstrip stacked-patch array (MSPA) to be used to feed a large lightweight deployable rotating mesh antenna for spaceborne L-band (approx.1 GHz) passive and active sensing systems. The design features will also enable applications to airborne soil moisture and salinity remote sensing sensors operating on small aircrafts. This paper describes the design of stacked patch elements and 16-element array configuration. The results from the return loss, antenna pattern measurements and sky tests are also described.

microstrip antennas↗
NASA NTRS2002

Moisture Advection and Fresh Water Flux over Oceans

Moisture transport in the atmosphere is one of the most significant components in the hydrological cycle. Under stationary condition, ocean surface fresh water flux, which is the difference between precipitation (P) and evaporation (E), is balanced by the divergence of column-integrated moisture transport (IMT) in the atmosphere. Characterizing accurately a global picture of IMT from observation is a difficult task. It requires measurements of vertical profiles for wind vector and humidity. More specifically, IMT can be defined as the integration in pressure coordinates the product of specific humidity q and wind vector u, where g is the gravitational acceleration, and p, is the atmospheric pressure at ocean surface.In this study, a statistical relationship is derived between u, and u(sub)s using data from numerical weather prediction model. The relationship is then validated using surface and vertical profile from radiosonde data, before applied to spacebased measurements.

scatterometers↗
NASA NTRS2011

The Planned Soil Moisture Active Passive (SMAP) Mission L-Band Radar/Radiometer Instrument

The Soil Moisture Active/Passive (SMAP) mission is a NASA mission identified by the NRC 'decadal survey' to measure both soil moisture and freeze/thaw state from space. The mission will use both active radar and passive radiometer instruments at L-Band. In order to achieve a wide swath at sufficiently high resolution for both active and passive channels, an instrument architecture that uses a large rotating reflector is employed. The instrument system has completed the preliminary design review (PDR) stage, and detailed instrument design has begun. In addition to providing an overview of the instrument design, two recent design modifications are discussed: 1) The addition of active thermal control to the instrument spun side to provide a more stable, settable thermal environment for the radiometer electronics, and 2) A 'sequential transmit' strategy for the two radar polarization channels which allows a single high-power amplifier to be used.

radiometer↗
NASA NTRS2013

Soil Moisture Estimate Under Forest Using a Semi-Empirical Model at P-Band

Here we present the result of a semi-empirical inversion model for soil moisture retrieval using the three backscattering coefficients: sigma(sub HH), sigma(sub VV) and sigma(sub HV). In this paper we focus on the soil moisture estimate and use the biomass as an ancillary parameter estimated automatically from the algorithm and used as a validation parameter, We will first remind the model analytical formulation. Then we will sow some results obtained with real SAR data and compare them to ground estimates.

soil moisture↗
NASA NTRS2015

Variability of Phenology and Fluxes of Water and Carbon with Observed and Simulated Soil Moisture in the Ent Terrestrial Biosphere Model (Ent TBM Version 1.0.1.0.0)

The Ent Terrestrial Biosphere Model (Ent TBM) is a mixed-canopy dynamic global vegetation model developed specifically for coupling with land surface hydrology and general circulation models (GCMs). This study describes the leaf phenology submodel implemented in the Ent TBM version 1.0.1.0.0 coupled to the carbon allocation scheme of the Ecosystem Demography (ED) model. The phenology submodel adopts a combination of responses to temperature (growing degree days and frost hardening), soil moisture (linearity of stress with relative saturation) and radiation (light length). Growth of leaves, sapwood, fine roots, stem wood and coarse roots is updated on a daily basis. We evaluate the performance in reproducing observed leaf seasonal growth as well as water and carbon fluxes for four plant functional types at five Fluxnet sites, with both observed and prognostic hydrology, and observed and prognostic seasonal leaf area index. The phenology submodel is able to capture the timing and magnitude of leaf-out and senescence for temperate broadleaf deciduous forest (Harvard Forest and Morgan- Monroe State Forest, US), C3 annual grassland (Vaira Ranch, US) and California oak savanna (Tonzi Ranch, US). For evergreen needleleaf forest (Hyytiäla, Finland), the phenology submodel captures the effect of frost hardening of photosynthetic capacity on seasonal fluxes and leaf area. We address the importance of customizing parameter sets of vegetation soil moisture stress response to the particular land surface hydrology scheme. We identify model deficiencies that reveal important dynamics and parameter needs.

Soil moisture↗