SLAPex Freeze/Thaw 2015: The First Dedicated Soil Freeze/Thaw Airborne Campaign

Soil freezing and thawing is an important process in the terrestrial water, energy, and carbon cycles, marking the change between two very different hydraulic, thermal, and biological regimes. NASA's Soil Moisture Active/Passive (SMAP) mission includes a binary freeze/thaw data product. While there have been ground-based remote sensing field measurements observing soil freeze/thaw at the point scale, and airborne campaigns that observed some frozen soil areas (e.g., BOREAS), the recently-completed SLAPex Freeze/Thaw (F/T) campaign is the first airborne campaign dedicated solely to observing frozen/thawed soil with both passive and active microwave sensors and dedicated ground truth, in order to enable detailed process-level exploration of the remote sensing signatures and in situ soil conditions. SLAPex F/T utilized the Scanning L-band Active/Passive (SLAP) instrument, an airborne simulator of SMAP developed at NASA's Goddard Space Flight Center, and was conducted near Winnipeg, Manitoba, Canada, in October/November, 2015. Future soil moisture missions are also expected to include soil freeze/thaw products, and the loss of the radar on SMAP means that airborne radar-radiometer observations like those that SLAP provides are unique assets for freeze/thaw algorithm development. This paper will present an overview of SLAPex F/T, including descriptions of the site, airborne and ground-based remote sensing, ground truth, as well as preliminary results

Empirical fitting of forward backscattering models for multitemporal retrieval of soil moisture from radar data at L-band

A multitemporal algorithm, originally conceived for the C-band radar aboard the Sentinel-1 satellite, has been updated to retrieve soil moisture from L-band radar data, such as those provided by the National Aeronautics and Space Administration Soil Moisture Active/Passive (SMAP) mission. This type of algorithm may deliver more accurate soil moisture maps that mitigate the effect of roughness and vegetation changes. Within the multitemporal inversion scheme based on the Bayesian maximum a posteriori probability (MAP) criterion, a dense time series of radar measurements is integrated to invert a forward backscattering model. The model calibration and validation tasks have been accomplished using the data collected during the SMAP validation experiment 12 spanning several soil conditions (pasture, wheat, corn, and soybean). The data have been used to update the forward model for bare soil scattering at L-band and to tune a simple vegetation scattering model considering two different classes of vegetation: those producing mainly single scattering effects and those characterized by a significant multiple scattering involving terrain surface and vegetation elements interaction. The algorithm retrievals showed a root mean square difference (RMSD) around 5% over bare soil, soybean, and cornfields. As for wheat, a bias was observed; when removed, the RMSD went down from 7.7% to 5%

A Method for Upscaling In Situ Soil Moisture Measurements to Satellite Footprint Scale Using Random Forests

Geophysical products generated from remotely sensed data require validation to evaluate their accuracy. Typically in situ measurements are used for validation, as is the case for satellite-derived soil moisture products. However, a large disparity in scales often exists between in situ measurements (covering meters to 10 s of meters) and satellite footprints (often hundreds of meters to several kilometers), making direct comparison difficult. Before using in situ measurements for validation, they must be “upscaled” to provide the mean soil moisture within the satellite footprint. There are a number of existing upscaling methods previously applied to soil moisture measurements, but many place strict requirements on the number and spatial distribution of soil moisture sensors difficult to achieve with permanent/semipermanent ground networks necessary for long-term validation efforts. A new method for upscaling is presented here, using Random Forests to fit a model between in situ measurements and a number of landscape parameters and variables impacting the spatial and temporal distributions of soil moisture. The method is specifically intended for validation of the NASA soil moisture active passive (SMAP) products at 36-, 9-, and 3-km scales. The method was applied to in situ data from the SoilSCAPE network in California, validated with data from the SMAPVEX12 campaign in Manitoba, Canada with additional verification from the TxSON network in Texas. For the SMAPVEX12 site, the proposed method was compared to extensive field measurements and was able to predict mean soil moisture over a large area more accurately than other upscaling approaches

Connecting NASA Science and Engineering with Earth Science Applications

ABSTRACT The National Research Council (NRC) recently highlighted the dual role of NASA to support both science and applications in planning Earth observations. This article reports the efforts of the NASA Applied Sciences Program and NASA Soil Moisture Active Passive (SMAP) mission to integrate applications with science and engineering in prelaunch planning. The SMAP Early Adopter program supported the prelaunch applied research that comprises the SMAP Special Collection of the Journal of Hydrometeorology. This research, in turn, has resulted in unprecedented prelaunch preparation for SMAP applications and critical feedback to the mission to improve product specifications and distribution for postlaunch applications. These efforts have been a learning experience that should provide direction for upcoming missions and set some context for the next NRC decadal survey

High-Resolution Soil-Moisture Maps Over Landslide Regions in Northern California Grassland Derived From SAR Backscattering Coefficients

Slow-moving landslides are destabilized by accumulated precipitation and consequent soil moisture. Yet, the continuous high-resolution soil-moisture measurements needed to aid the understanding of landslide processes are generally absent in steep terrain. Here, we produce soil-moisture time-series maps for a seasonally active grassland landslide in the northern California coast ranges, USA, using backscattering coefficients from NASA's uninhabited aerial vehicle synthetic aperture radar at 6-m resolution. A physically based radar scattering model is used to retrieve the near-surface (5-cm depth) soil moisture for the landslide. Both forward modeling (backscattering estimation) and the retrieval (soil-moisture validation) show good agreement. The root-mean-square errors (RMSE) for vertical transmit vertical receive (VV) and horizontal transmit horizontal receive (HH) polarizations in forward model comparison are 1.93 dB and 1.88 dB, respectively. The soil-moisture retrieval shows unbiased RMSE of 0.054 m³/m³. Our successful retrieval benefits from the surface and double-bounce scattering, which is common in grasslands. The retrieved maps show saturated wetness conditions within the active landslide boundaries. We also performed sensitivity tests for incidence angle and found that the retrieval is weakly dependent on the angle, especially while using copolarized HH and VV together. Using the two copolarized inputs, the retrieval is also not sensitive to the change of orientation angles of grass cylinders. The physical model inversion presented here can be generally applied for soil-moisture retrieval in areas with the same vegetation cover types in California

Understanding and Improving the Soil Moisture Retrieval Algorithm under Space, Time and Heterogeneity

The spatial and temporal monitoring of soil moisture from remote sensing platforms plays a pivotal role in predicting the future food and water security. That is, improving soil moisture estimation at remote sensing platforms has remarkable impacts in the fields of meteorology, hydrology, agriculture, and global climate change. However, remote sensing of soil moisture for long is hindered by spatial heterogeneity in land surface variables (soil, biomass, topography, and temperature) which cause systematic and random errors in soil moisture retrievals. Most soil moisture improvement methods to date focused on the downscaling of either coarse resolution soil moisture or brightness temperature based on fine scale ancillary information of land surface variables. Comparatively little work has been done on improving the parameterization of most sensitive variables to radiative transfer model that impact soil moisture retrieval accuracy. In addition, the classic radiative transfer model assumes the vegetation and surface roughness parameters, as constant with space and time which undermines the retrieval accuracy. Also, it is largely elusive so far the discussion on the non-linearity of microwave radiative transfer model and its relationship with energy and water fluxes. In order to address the above mentioned limitations, this dissertation aims to develop and validate a soil moisture modeling framework with associated improved parameterizations for surface roughness and vegetation optical depth (VOD) in the homogeneous and heterogeneous environments. To this end, the following research work is specifically conducted: (a) conduct comprehensive sensitivity analysis on radiative transfer model with space, time and hydroclimates; (b) develop multi-scale surface roughness model which incorporates small (soil) and large (topography) surface undulations to improve soil moisture retrievals; (c) improve the parameterization of vegetation topical depth (VOD) using within-pixel biomass heterogeneity to improved soil moisture accuracy; (d) investigate the non-linearity in microwave radiative transfer model, and its association with thermal energy fluxes. The results of this study showed that: (a) the total (linear + non-linear) sensitivity of soil, temperature and biomass variables varied with spatial scale (support), time, and hydro climates, with higher non-linearity observed for dense biomass regions. This non-linearity is also governed by soil moisture availability and temperature. Among these variables, surface roughness and vegetation optical depth are most sensitive variables to radiative transfer model (RTM); (b) considering the spatial and temporal variability in parameterization of surface roughness and VOD has improved soil moisture retrieval accuracy, importantly in cropland and forest environments; and (c) the soil moisture estimated through evaporative fraction (EF) correlates higher with VOD corrected soil moisture

Estimation of Indonesian Peat Forest Carbon Emissions Based on Soil Moisture Active Passive (SMAP) Satellite Image

Calculation of carbon emission in tropical peatland forest still unsolved problem. In this paper, we propose a method to calculate carbon emission by using Soil Moisture Active Passive (SMAP) satellite. The SMAP images on January 2018 enhanced 1.3 radiometer Global Daily 9 km, EASE grid overlayed with peat map. The water table mapping obtained by using empirical relation between soil moisture and water table in January 2018 shows a pattern according to some observation. The carbon emission map on January 2018 shows the average is about 280 gC km-2

L-band SAR co-polarized phase difference modeling for corn fields

This research aims at modeling the microwave backscatter of corn fields by coupling an incoherent, interaction-based scattering model with a semi-empirical bulk vegetation dielectric model. The scattering model is fitted to co-polarized phase difference measurements over several corn fields imaged with fully polarimetric synthetic aperture radar (SAR) images with incidence angles ranging from 20° to 60°. The dataset comprised two field campaigns, one over Canada with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR, 1.258 GHz) and the other one over Argentina with Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) (ALOS-2/PALSAR-2, 1.236 GHz), totaling 60 data measurements over 28 grown corn fields at peak biomass with stalk gravimetric moisture larger than 0.8 g/g. Co-polarized phase differences were computed using a maximum likelihood estimation technique from each field’s measured speckled sample histograms. After minimizing the difference between the model and data measurements for varying incidence angles by a nonlinear least-squares fitting, well agreement was found with a root mean squared error of 24.3° for co-polarized phase difference measurements in the range of -170.3° to -19.13°. Model parameterization by stalk gravimetric moisture instead of its complex dielectric constant is also addressed. Further validation was undertaken for the UAVSAR dataset on earlier corn stages, where overall sensitivity to stalk height, stalk gravimetric moisture, and stalk area density agreed with ground data, with the sensitivity to stalk diameter being the weakest. This study provides a new perspective on the use of co-polarized phase differences in retrieving corn stalk features through inverse modeling techniques from space.This research was partially funded by the Argentinean National Scientific and Technical Research Council (CONICET, project PICT 2015 N◦3689), by the Spanish Ministry of Science and Innovation (project CICYT RTI2018-099008-B-C21/AEI/10.13039/501100011033 “Sensing with pioneering opportunistic techniques”) and by the grant to “CommSensLab-UPC” Excellence Research Unit Maria de Maeztu (MINECO grant).Peer ReviewedPostprint (published version

A 3-D Full-Wave Model to Study the Impact of Soybean Components and Structure on L-Band Backscatter

Microwave remote sensing offers a powerful tool for monitoring the growth of short, dense vegetation like soybean. As the plants mature, changes in their biomass and 3-D structure impact the electromagnetic (EM) backscatter signal. This backscatter information holds valuable insights into crop health and yield, prompting the need for a comprehensive understanding of how structural and biophysical properties of soybeans as well as soil characteristics contribute to the overall backscatter signature. In this study, a full-wave model is developed for simulating L-band backscatter from soybean fields. Leveraging the ANSYS High-Frequency Structure Simulator (HFSS) framework, the model solves for the scattering of EM waves from realistic 3-D structural models of soybean, explicitly incorporating the interplant scattering effects. The model estimates of backscatter match well with the field observations from the SMAPVEX16-MicroWEX and SMAPVEX12, with average differences of 1-2 dB for co-pol and less than 4 dB for cross-pol. Furthermore, the model effectively replicates the temporal dynamics of crop backscatter throughout the growing season. The HFSS analysis revealed that the stems and pods are the primary contributors to HH-pol backscatter, while the branches contribute to VV-pol, and leaves impact the cross-pol signatures. In addition, a sensitivity study with 3-D bare soil surface resulted in an average variation of 8 dB in co- and cross-pol, even when the root mean square height and correlation length were held constant