Two separate periods of the LAI–VIs relationships using in situ measurements in a deciduous broadleaf forest

Potithep, Supannika; Nagai, Shin; Nasahara, Kenlo Nishida; Muraoka, Hiroyuki; Suzuki, Rikie

doi:10.1016/j.agrformet.2012.09.003

Cited by 70 publications

(42 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As formulated for plant canopies, LAI normally replaces N in the Beer-Lambert equation as an estimate of density of light absorbing particles, and in our application EVI replaces LAI, assuming a linear relationship between EVI and LAI over LAI values from 0 to ca. 4 as demonstrated in [17,33]. The coefficient c accounts for the fact that EVI is not zero at zero ET a since bare soil has a low but positive EVI.…”

Section: Form Of the Algorithm For Et Amentioning

confidence: 99%

Estimating Riparian and Agricultural Actual Evapotranspiration by Reference Evapotranspiration and MODIS Enhanced Vegetation Index

et al. 2013

View full text Add to dashboard Cite

Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor water use by both crops and natural vegetation in irrigation districts. We developed an algorithm for estimating actual evapotranspiration (ET a ) based on the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the EOS-1 Terra satellite and locally-derived measurements of reference crop ET (ET o ). The algorithm was calibrated with five years of ET a data from three eddy covariance flux towers set in riparian plant associations on the upper San Pedro River, Arizona, supplemented with ET a data for alfalfa and cotton from the literature. measured results in each case, with a non-significant (P = 0.89) difference between mean measured and modeled ET a of 5.4% over all validation sites. Validation and calibration data sets were combined to present a final predictive equation for application across crops and riparian plant associations for monitoring individual irrigation districts or for conducting global water use assessments of mixed agricultural and riparian biomes.

show abstract

Section: Form Of the Algorithm For Et Amentioning

confidence: 99%

Estimating Riparian and Agricultural Actual Evapotranspiration by Reference Evapotranspiration and MODIS Enhanced Vegetation Index

et al. 2013

View full text Add to dashboard Cite

show abstract

“…At the canopy scale, previous research has reported that the LAI for wheat can be accurately estimated by field spectral measurements [20,26,27]. However, SI-based empirical regression models have often been growth-stage-specific or year-specific [9,21,28,29]. Therefore, quantitative assessments of LAI remain uncertain.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Viña et al [10] found that chlorophyll indices (the red-edge chlorophyll index [CIred-edge] and the green chlorophyll index [CIgreen]), devised for chlorophyll assessment at the leaf scale [18], are more accurate for LAI assessments of maize and soybean crops than for the above-mentioned SIs. These SI-based studies have successfully predicted LAI at various spatial scales using commercially available digital cameras, field spectroradiometers, or airborne and satellite-borne sensors [8,11,[19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

et al. 2015

View full text Add to dashboard Cite

Timely and nondestructive monitoring of leaf area index (LAI) using remote sensing techniques is crucial for precise and efficient management of crops. In this paper, a new spectral index (SI) for estimating LAI of winter wheat (Triticum aestivum L.) is proposed on the basis of field hyperspectral measurements. A simple index based on the empirical relationships between LAIs and SIs of all available two-waveband combinations from hyperspectral data is developed by considering the difference between reflectance values at 760 and 739 nm (DSIR760-R739 = R760 -R739). Among published and newly developed SIs, DSIR760-R739 exhibited a significant and strong linear relationship with LAI OPEN ACCESSRemote Sens. 2015, 7 5330 and showed outstanding performance in LAI assessments. The permissible bandwidths for broad-band DSIR760-R739 investigated using simulated reflectance were 5 nm for both 760 and 739 nm center wavelengths. The results indicate that the linear regression model based on the narrow-band and broad-band DSIR760-R739 is a simple but accurate method for timely and nondestructive monitoring of LAI.

show abstract

“…Many methods have been developed to retrieve LAI from remote-sensing data. In general, methods to estimate LAI from satellite-sensor data can be classified into empirical methods [2,3] and physical methods [4][5][6]. Empirical methods are based mainly on constructing statistical relationships between LAI and a variety of vegetation indices.…”

Section: Introductionmentioning

confidence: 99%

Sequential Method with Incremental Analysis Update to Retrieve Leaf Area Index from Time Series MODIS Reflectance Data

et al. 2014

View full text Add to dashboard Cite

High-quality leaf area index (LAI) products retrieved from satellite observations are urgently needed for crop growth monitoring and yield estimation, land-surface process simulation and global change studies. In recent years, sequential assimilation methods have been increasingly used to retrieve LAI from time series remote-sensing data. However, the inherent characteristics of these sequential assimilation methods result in temporal discontinuities in the retrieved LAI profiles. In this study, a sequential assimilation method with incremental analysis update (IAU) was developed to jointly update model states and parameters and to retrieve temporally continuous LAI profiles from time series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data. Based on the existing multi-year Global Land Surface Satellite (GLASS) LAI product, a dynamic model was constructed to evolve LAI anomalies over time.

show abstract

Two separate periods of the LAI–VIs relationships using in situ measurements in a deciduous broadleaf forest

Cited by 70 publications

References 48 publications

Estimating Riparian and Agricultural Actual Evapotranspiration by Reference Evapotranspiration and MODIS Enhanced Vegetation Index

Estimating Riparian and Agricultural Actual Evapotranspiration by Reference Evapotranspiration and MODIS Enhanced Vegetation Index

Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

Sequential Method with Incremental Analysis Update to Retrieve Leaf Area Index from Time Series MODIS Reflectance Data

Contact Info

Product

Resources

About