Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA☆

Marco, Trombetti; Riaño, David; Rubio, Miguel A.; Cheng, Yi‐Feng; Ustin, Susan L.

doi:10.1016/j.rse.2007.04.013

Cited by 142 publications

(96 citation statements)

References 65 publications

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“…In order to compare performance with the empirical derived models, three RTM-based models were used to estimate CWC (Trombetti et al, 2008) and FMC (Yebra et al, 2008b;Jurdao et al, 2013). As for the empirical models, the spectral information used to run the RTMs was the one obtained using proximal sensing and MODIS data.…”

Section: Rtm-based Water Metrics Estimatesmentioning

confidence: 99%

“…CWC was estimated in the study site following Trombetti et al (2008). This method uses PROSAILH RTM (Jacquemoud and Baret, 1990;Jacquemoud et al, 1995) and Artificial Neural Networks (ANN) to estimate CWC.…”

Section: Cwcmentioning

confidence: 99%

“…Finally, another metric is the canopy water content (CWC), the mass of water in the canopy per ground area Trombetti et al, 2008). CWC represent the product of EWT and leaf area index (LAI), offering information on vegetation water content at canopy level and can be expressed as:…”

Section: Introductionmentioning

confidence: 99%

“…They can be applied to different locations to estimate different vegetation parameters, as long as the RTM is a true representation of the vegetation canopy. For example, Trombetti et al (2008) predicted CWC for the continental US using RTM PROSAILH (Jacquemoud et al, 1995) simulations. Their model was calibrated with CWC from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral water absorption bands.…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, the performance statistics of the fitting equations were evaluated to select the most accurate empirical models. Finally, these models were compared to three RTM based estimates, proposed in the literature to derive two FMC Yebra et al, 2008b) and the other for CWC (Trombetti et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

et al. 2015

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Abstract. This study evaluates three different metrics of water content of an herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel moisture content (FMC), equivalent water thickness (EWT) and canopy water content (CWC) were estimated from proximal sensing and MODIS satellite imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics and were also analyzed. Metrics were derived from field sampling of grass cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and MODIS images were simultaneously acquired and predictive empirical models were parametrized. Two methods of estimating FMC and CWC using different field protocols were tested in order to evaluate the consistency of the metrics and the relationships with the predictive empirical models. In addition, radiative transfer models (RTM) were used to produce estimates of CWC and FMC, which were compared with the empirical ones.Results revealed that, for all metrics spatial variability was significantly lower than temporal. Thus we concluded that experimental design should prioritize sampling frequency rather than sample size. Dm variability was high which demonstrates that a constant annual Dm value should not be used to predict EWT from FMC as other previous studies did. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. Visible Atmospherically Resistant Index (VARI) provided the lowest explicative power in all cases. For proximal sensing, Global Environment Monitoring Index (GEMI) showed higher statistical relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 % respectively). When MODIS data were used, results showed an increase in R 2 and Enhanced Vegetation Index (EVI) as the best predictor for FMC (RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for EWT (RRMSE = 24.6 %). Differences in the viewing geometry of the platforms can explain these differences as the portion of vegetation observed by MODIS is larger than when using proximal sensing including the spectral response from scattered trees and its shadows. CWC was better predicted than the other two water content metrics, probably because CWC depends on LAI, that shows a notable seasonal variation in this ecosystem. Strong statistical relationship was found between empirical models using indices sensible to chlorophyll activity (NDVI or EVI which are not directly related to water content) due to the close relationship between LAI, water content and chlorophyll activity in grassland cover, which is not true for other types of vegetation such as forest or shrubs. The empirical methods tested outperformed FMC and CWC products based on radiative transfer model inversion.

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Section: Rtm-based Water Metrics Estimatesmentioning

confidence: 99%

Section: Cwcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

et al. 2015

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Contributions of imaging spectroscopy to improve estimates of evapotranspiration

Rodriguez

Ustin

Riaño

2011

Hydrological Processes

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Abstract:Improved estimates of evapotranspiration (ET) are needed for water resource management and irrigation scheduling. We review the use of imaging spectroscopy to capture estimates of water vapour flux and biophysical components of ET. Remote sensing has long attempted to quantify and predict ET, with most applications relying only on green vegetation indexes from multispectral imagers combined with thermal radiance and weather data. In contrast, imaging spectrometry is an advanced remote sensing technology that measures hundreds of spectral bands in the solar spectrum. Plant pigments, water, and dry matter have unique spectral signatures that can advance estimates of ET and detection of drought stress. This allows analyses based on the physics of spectroscopy and avoids a requirement for continual empirical calibration. These spectral components provide unprecedented information about plant physiological processes, which improve understanding of the regulation of water fluxes and the energy budget. Laboratory, field, and airborne studies of spectral properties in the near-and shortwave-infrared region show strong relationships with plant water relations like water content, relative water content, and water potential. Because water absorption features are spectrally independent of pigment absorptions in the visible region, they provide a new source of information about environmental conditions. These new observations from imaging spectroscopy will lead to better understanding of ecological and hydrological processes.

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Soil Moisture Estimation by Combining L-Band Brightness Temperature and Vegetation Related Information

Zhao

Yang

et al. 2019

Computer and Computing Technologies in Agriculture XI

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Passive radiometry at L-band has been widely accepted as one of the most promising techniques for monitoring soil moisture content (SMC). However, with vegetation cover, the scatter and attenuation of microwave signals by vegetation make the discrimination of SMC related signal complicated. To improve SMC estimate, this study proposed the combined use of L-band brightness temperature (TB) and optical remote sensing data to take into account the effect of vegetation. The normalized difference infrared index (NDII) and enhanced vegetation index (EVI) were used as proxy for including the effect of vegetation water content and structure. Considering viewing angle effects, TB data were normalized to three different angles (7°, 21.5°, and 38.5°). The model based on the combination of NDII and horizontally polarized TB normalized to 7° produced the best result (R 2 =0.678, RMSE=0.026 m 3 /m 3 ). It suggests that involving NDII into the model could significantly improve pasture covered SMC estimation accuracy.

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Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA☆

Cited by 142 publications

References 65 publications

Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

Contributions of imaging spectroscopy to improve estimates of evapotranspiration

Soil Moisture Estimation by Combining L-Band Brightness Temperature and Vegetation Related Information

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