Mapping soil moisture in the central Ebro river valley (northeast Spain) with Landsat and NOAA satellite imagery: a comparison with meteorological data

Vicente‐Serrano, Sergio M.; Pons, Xavier; Prats, José María Cuadrat

doi:10.1080/01431160410001712990

Cited by 50 publications

(25 citation statements)

References 68 publications

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“…(7)), it is important to note that the length scales of variability in the fluxes need not be the same as those of the input fields (Brunsell & Gillies, 2003a). The fluxes are the result of non-linear interactions between vegetation and soil moisture, where the near surface soil moisture variability is captured via the variability in radiometric surface temperature McVicar & Jupp, 2002;Vicente-Serrano et al, 2004).…”

Section: Wavelet Multi-resolution Analysismentioning

confidence: 99%

Assessing the multi-resolution information content of remotely sensed variables and elevation for evapotranspiration in a tall-grass prairie environment

Brunsell¹,

Ham²,

Owensby³

2008

Remote Sensing of Environment

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Section: Wavelet Multi-resolution Analysismentioning

confidence: 99%

Assessing the multi-resolution information content of remotely sensed variables and elevation for evapotranspiration in a tall-grass prairie environment

Brunsell¹,

Ham²,

Owensby³

2008

Remote Sensing of Environment

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“…Extensive efforts have been carried out over the past decades to estimate soil moisture using satellite images. Vicente-Serrano et al (2004) showed that, using remote sensing, it was possible to estimate aridity intensity with a high reliability. Much other work has been carried out to investigate the suitability of remote sensing in soil moisture estimation (Goward et al 1985, Price 1989, Carlson et al 1990, Gillies et al 1997, Tugrul Yilmaz et al 2008.…”

Section: Introductionmentioning

confidence: 98%

Multi-index-based soil moisture estimation using MODIS images

Hosseini

Saradjian

2011

International Journal of Remote Sensing

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The suitability of using Moderate Resolution Imaging Spectroradiometer (MODIS) images for surface soil moisture estimation to investigate the importance of soil moisture in different applications, such as agriculture, hydrology, meteorology and natural disaster management, is evaluated in this study. Soil moisture field measurements and MODIS images of relevant dates have been acquired. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) are calculated from MODIS images. In addition, MODIS Land Surface Temperature (LST) data (MOD11A1) are used in this analysis. Four different soil moisture estimation models, which are based on NDVI-LST, EVI-LST, NDVI-LST-NDWI and EVI-LST-NDWI, are developed and their accuracies are assessed. Statistical analysis shows that replacing EVI with NDVI in the model that is based on LST and NDVI increases the accuracy of soil moisture estimation. Accuracy evaluation of soil moisture estimation using check points shows that the model based on LST, EVI and NDWI values gives a higher accuracy than that based on LST and EVI values. It is concluded that the model based on the three indices is a suitable model to estimate soil moisture through MODIS imagery.

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“…Besides studies based on laboratory results, approaches exist to quantify surface soil moisture from broadband optical sensors such as Landsat Thematic Mapper (Landsat ETM+), Ad-vanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) [9,[19][20][21][22] or simulated Satellite Pour l'Observation de la Terre (SPOT) channels [23]. Due to the relatively broad spectral resolution of these sensors, none of the models developed under laboratory conditions can be applied here without restrictions.…”

Section: Discovered In Theirmentioning

confidence: 99%

Surface soil moisture quantification and validation based on hyperspectral data and field measurements

Haubrock

2008

J. Appl. Remote Sens

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Abstract. Surface soil moisture information is needed for monitoring and modeling surface processes at various spatial scales. While many reflectance based soil moisture quantification models have been developed and validated in laboratories, only few were applied from remote sensing platforms and thoroughly validated in the field. This paper addresses the issues of a) quantifying surface soil moisture with very high resolution spectral measurements from remote sensors in a landscape with sandy substrates and low vegetation cover as well as b) comprehensively validating these results in the field. For this purpose, the recently developed Normalized Soil Moisture Index (NSMI) has been analyzed for its applicability to airborne hyperspectral remote sensing data. Three HyMap scenes from 2004 and 2005 were collected from a lignite mining area in southern Brandenburg, Germany. An NSMI model was calibrated (R 2 =0.92) and surface soil moisture maps were calculated based on this model. An in-situ surface soil moisture map based on a combination of Frequency Domain Reflectometry (FDR) and gravimetric data allowed for validating each image pixel (R 2 =0.82). In addition, a qualitative multitemporal comparison between two consecutive NSMI datasets from 2004 was performed and validated, showing an increase in estimated surface soil moisture corresponding with field measurements and precipitation data. The study shows that the NSMI is appropriate for modeling surface soil moisture from high spectral-resolution remote sensing data. The index leads to valid estimations of soil moisture values below field capacity in an area with sandy substrates and low vegetation cover (NDVI < 0.3). Further studies will analyze the validity of the NSMI for surface soil moisture estimation from spaceborne hyperspectral sensors like the Environmental Mapping and Analysis Program (EnMap) in different landscapes.

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Mapping soil moisture in the central Ebro river valley (northeast Spain) with Landsat and NOAA satellite imagery: a comparison with meteorological data

Cited by 50 publications

References 68 publications

Assessing the multi-resolution information content of remotely sensed variables and elevation for evapotranspiration in a tall-grass prairie environment

Assessing the multi-resolution information content of remotely sensed variables and elevation for evapotranspiration in a tall-grass prairie environment

Multi-index-based soil moisture estimation using MODIS images

Surface soil moisture quantification and validation based on hyperspectral data and field measurements

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