A linear physically-based model for remote sensing of soil moisture using short wave infrared bands

Sadeghi, Morteza; Jones, Scott B.; Philpot, William

doi:10.1016/j.rse.2015.04.007

Cited by 229 publications

(163 citation statements)

References 53 publications

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“…However, this dataset has been shown to considerably underestimate GPP over semi-arid Sahelian ecosystems [21,30]. Recent work by Sadeghi et al [31] found that the shortwave infrared spectral range of 2105-2155 nm, corresponding to band 7 in the moderate-resolution imaging spectroradiometer (MODIS) satellite sensor, provides optimal sensitivity to variations in soil moisture. Furthermore, aerosol effects are negligible because most aerosol particulates are smaller than that wavelength range [32].…”

Section: Field Sitesmentioning

confidence: 98%

“…The Collection 5.1 MCD43A4 NBAR Level 3 and MOD17 8-day GPP product (MOD17A2) products were downloaded for each site from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL-DAAC, daac.ornl.gov/modisglobal). Band 7 of the MCD43A4 NBAR product (henceforth, MODIS NBAR Band 7 or simply Band 7) covers the shortwave infrared wavelength range of 2105-2155 nm that is crucial for capturing sensitivity to variations in soil moisture [31]. These data are provided as ASCII files containing observations within a specified radius around a particular point of interest.…”

Section: Earth Observation Datamentioning

confidence: 99%

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Evaluating Water Controls on Vegetation Growth in the Semi-Arid Sahel Using Field and Earth Observation Data

et al. 2017

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Water loss is a crucial factor for vegetation in the semi-arid Sahel region of Africa. Global satellite-driven estimates of plant CO 2 uptake (gross primary productivity, GPP) have been found to not accurately account for Sahelian conditions, particularly the impact of canopy water stress. Here, we identify the main biophysical limitations that induce canopy water stress in Sahelian vegetation and evaluate the relationships between field data and Earth observation-derived spectral products for up-scaling GPP. We find that plant-available water and vapor pressure deficit together control the GPP of Sahelian vegetation through their impact on the greening and browning phases. Our results show that a multiple linear regression (MLR) GPP model that combines the enhanced vegetation index, land surface temperature, and the short-wave infrared reflectance (Band 7, 2105-2155 nm) of the moderate-resolution imaging spectroradiometer satellite sensor was able to explain between 88% and 96% of the variability of eddy covariance flux tower GPP at three Sahelian sites (overall = 89%). The MLR GPP model presented here is potentially scalable at a relatively high spatial and temporal resolution. Given the scarcity of field data on CO 2 fluxes in the Sahel, this scalability is important due to the low number of flux towers in the region.

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Section: Field Sitesmentioning

confidence: 98%

Section: Earth Observation Datamentioning

confidence: 99%

Evaluating Water Controls on Vegetation Growth in the Semi-Arid Sahel Using Field and Earth Observation Data

et al. 2017

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“…Although L-band microwave soil moisture products can partially overcome the influence of dense vegetation, optical remote sensing has its advantages in exemption from complicated polarization information exploration or exhaustive field observations on soil surface roughness. Thus, many soil moisture remote sensing achievements have been made on optical soil moisture remote sensing [22][23][24][25][26]. Nevertheless, clouds, thick fogs, mists, darkness, absence of revisiting and many other factors have prevented optical sensors from operating over a required location at the required moment.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Based Reconstruction Method for Satellite Remote Sensing of Soil Moisture Images with In Situ Observations

et al. 2017

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Surface soil moisture is an important environment variable that is dominant in a variety of research and application areas. Acquiring spatiotemporal continuous soil moisture observations is therefore of great importance. Weather conditions can contaminate optical remote sensing observations on soil moisture, and the absence of remote sensors causes gaps in regional soil moisture observation time series. Therefore, reconstruction is highly motivated to overcome such contamination and to fill in such gaps. In this paper, we propose a novel image reconstruction algorithm that improved upon the Satellite and In situ sensor Collaborated Reconstruction (SICR) algorithm provided by our previous publication. Taking artificial neural networks as a model, complex and highly variable relationships between in situ observations and remote sensing soil moisture is better projected. With historical data for the network training, feedforward neural networks (FNNs) project in situ soil moisture to remote sensing soil moisture at better performances than conventional models. Consequently, regional soil moisture observations can be reconstructed under full cloud contamination or under a total absence of remote sensors. Experiments confirmed better reconstruction accuracy and precision with this improvement than with SICR. The new algorithm enhances the temporal resolution of high spatial resolution remote sensing regional soil moisture observations with good quality and can benefit multiple soil moisture-based applications and research.

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“…Remote sensing (RS) has demonstrated great potential for large-scale monitoring of soil moisture utilizing various frequency bands of the electromagnetic spectrum such as shortwave infrared [6,7], thermal infrared [8][9][10], or microwave radiation [11,12]. Microwave RS techniques have shown greater promise for global monitoring of soil moisture variations [13], because in contrast to thermal and…”

Section: Introductionmentioning

confidence: 99%

Advancing NASA’s AirMOSS P-Band Radar Root Zone Soil Moisture Retrieval Algorithm via Incorporation of Richards’ Equation

et al. 2016

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P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar observations, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For this reason, an empirical quadratic function (second order polynomial) is currently applied in the AirMOSS inversion algorithm to retrieve the SMP. The three free parameters of the polynomial are retrieved for each AirMOSS pixel using three backscatter observations (i.e., one frequency at three polarizations of Horizontal-Horizontal, Vertical-Vertical and Horizontal-Vertical). In this paper, a more realistic, physically-based SMP model containing three free parameters is derived, based on a solution to Richards' equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy.

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A linear physically-based model for remote sensing of soil moisture using short wave infrared bands

Cited by 229 publications

References 53 publications

Evaluating Water Controls on Vegetation Growth in the Semi-Arid Sahel Using Field and Earth Observation Data

Evaluating Water Controls on Vegetation Growth in the Semi-Arid Sahel Using Field and Earth Observation Data

A Machine Learning Based Reconstruction Method for Satellite Remote Sensing of Soil Moisture Images with In Situ Observations

Advancing NASA’s AirMOSS P-Band Radar Root Zone Soil Moisture Retrieval Algorithm via Incorporation of Richards’ Equation

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