Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach

Chauhan, N.S.; Miller, Shawn W.; Ardanuy, Philip E.

doi:10.1080/0143116031000156837

Cited by 287 publications

(207 citation statements)

References 30 publications

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“…According to the studies of Carlson, there is a relationship among soil moisture, NDVI, and LST, which can be expressed through a regression formula [65]. It is possible to downscale AMSR-E soil moisture from 25 to 1 km based on this relationship [66][67][68].…”

Section: The Estimation Of Smmentioning

confidence: 99%

A Global Grassland Drought Index (GDI) Product: Algorithm and Validation

Liao

Quan

et al. 2015

Remote Sensing

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Existing drought indices have been widely used to monitor meteorological drought and agricultural drought; however, few of them are focus on drought monitoring for grassland regions. This study presented a new drought index, the Grassland Drought Index (GDI), for monitoring drought conditions in global grassland regions. These regions are vital for the environment and human society but susceptible to drought. The GDI was constructed based on three measures of water content: precipitation, soil moisture (SM), and canopy water content (CWC). The precipitation information was extracted from the available precipitation datasets, and SM was estimated by downscaling exiting soil moisture data to a 1 km resolution, and CWC was retrieved based on the PROSAIL (PROSPECT + SAIL) model. Each variable was scaled from 0 to 1 for each pixel based on absolute minimum and maximum values over time, and these scaled variables were combined with the selected weights to construct the GDI. According to validation at the regional scale, the GDI was correlated with the Standardized Precipitation Index (SPI) to some extent, and captured most of the drought area identified by the United States Drought Monitor (USDM) maps. In addition, the global GDI product at a 1 km spatial resolution substantially agreed with the global Standardized Precipitation Evapotranspiration Index (SPEI) product throughout the period 2005-2010, and it provided detailed and accurate information about the location and the duration of drought based on the evaluation using the known drought events. OPEN ACCESSRemote Sens. 2015, 7 12705

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Section: The Estimation Of Smmentioning

confidence: 99%

A Global Grassland Drought Index (GDI) Product: Algorithm and Validation

Liao

Quan

et al. 2015

Remote Sensing

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“…To solve this problem one has to consider additional observations. Chauhan et al [11] suggested adding surface albedo. When introducing the albedo, it is expected to separate areas according to the absorbed solar radiation.…”

Section: Addition Of a Third Optical Parameter To T-vimentioning

confidence: 99%

“…Soil moisture M was evaluated from a relation proposed by Chauhan in [11] where albedo is replaced by the emissivity ε :…”

Section: Distribution Of Temperature Vegetation Index and Repectivmentioning

confidence: 99%

Combined airborne thermography and visible-to-near infrared reflectance measurement for soil moisture mapping

Krapez¹,

Chatelard²,

J.-F.³

et al. 2012

Proceedings of the 2012 International Conference on Quantitative InfraRed Thermography

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We first present a review of the so-called triangle/trapezoid method for soil moisture evaluation from optical remote sensing data obtained in the red, near infrared and thermal infrared bands. Recent improvements based on additional vegetation indexes obtained for example from multiwavelength signals in SWIR band will also be presented. In a second part we describe the results we obtained for this purpose by using a microbolometer camera onboard of a motorglider. Signal processing for atmosphere correction and orthorectification was specifically developed for this application. First results on the path for identification of soil water content with this approach will be presented.

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“…In this context, a number of downscaling strategies of the surface soil moisture derived from microwave data have been imagined. They vary with respect to input ancillary data (e.g., optical data [8], radar data [9], topography and soil depth [10]), the nature (physical, semi-empirical, empirical) of scale change modeling and the underlying physical assumptions (i.e., how soil moisture is linked to available fine-scale modeled or observational information). It is worth noting that the principle of SMAP is based on the disaggregation of L-band brightness temperatures using higher resolution radar backscatter data [11].…”

Section: Introductionmentioning

confidence: 99%

Performance Metrics for Soil Moisture Downscaling Methods: Application to DISPATCH Data in Central Morocco

et al. 2015

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Data disaggregation (or downscaling) is becoming a recognized modeling framework to improve the spatial resolution of available surface soil moisture satellite products. However, depending on the quality of the scale change modeling and on the uncertainty in its input data, disaggregation may improve or degrade soil moisture information at high resolution. Hence, defining a relevant metric for evaluating such methodologies is crucial before disaggregated data can be eventually used in fine-scale studies. In this paper, a new metric, named G DOWN , is proposed to assess the potential gain provided by disaggregation relative to the non-disaggregation case. The performance metric is tested during a four-year period by comparing 1-km resolution disaggregation based on physical and theoretical scale change (DISPATCH) data with the soil moisture measurements collected by six stations in central Morocco. DISPATCH data are obtained every 2-3 days from 40-km resolution SMOS (Soil Moisture Ocean Salinity) and 1-km resolution optical MODIS (Moderate Resolution Imaging Spectroradiometer) data. The correlation coefficient between G DOWN and the disaggregation gain in time series correlation, mean bias and bias in the slope of the linear fit ranges from 0.5 to 0.8. The new metric is found to be a good Remote Sens. 2015, 7 3784 indicator of the overall performance of DISPATCH. Especially, the sign of G DOWN (positive in the case of effective disaggregation and negative in the opposite case) is independent of the uncertainties in SMOS data and of the representativeness of localized in situ measurements at the downscaling (1 km) resolution. In contrast, the traditional root mean square difference between disaggregation output and in situ measurements is poorly correlated (correlation coefficient of about 0.0) with the disaggregation gain in terms of both time series correlation and bias in the slope of the linear fit. The G DOWN approach is generic and thus could help test a range of downscaling methods dedicated to soil moisture and to other geophysical variables.

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Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach

Cited by 287 publications

References 30 publications

A Global Grassland Drought Index (GDI) Product: Algorithm and Validation

A Global Grassland Drought Index (GDI) Product: Algorithm and Validation

Combined airborne thermography and visible-to-near infrared reflectance measurement for soil moisture mapping

Performance Metrics for Soil Moisture Downscaling Methods: Application to DISPATCH Data in Central Morocco

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