Analyses of spectral-biophysical relationships for a corn canopy

Gilabert, María Amparo; Gandía, S.; Meliá, J.

doi:10.1016/0034-4257(95)00187-5

Cited by 97 publications

(53 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It should be noticed that NDVI values tend to underestimate the green biomass of stands over areas with strong foliage density and high production (Hobbs 1995;Tucker et al 1986;Gilabert et al 1996), causing saturation of NDVI. Despite this caveat, which is expected to cause relatively lower impacts in the assessment of droughts, the time series of MVC-NDVI composites have proven to be a source of valuable information for monitoring surface vegetation dynamics at the global and the regional scales Nemani et al 2003;Gouveia et al 2008).…”

Section: Methodsmentioning

confidence: 99%

The 2009/10 Drought in China: Possible Causes and Impacts on Vegetation

Barriopedro

Gouveia

Trigo

et al. 2012

Journal of Hydrometeorology

215

125

View full text Add to dashboard Cite

Several provinces of China experienced an intense drought episode during 2009 and 2010. The drought was particularly severe in southwestern and northern China, where the accumulated precipitation from May 2009 to April 2010 was about 25% less than normal. The decline of accumulated precipitation over northern China was mostly noticeable during the summer season of 2009 and it was comparable to recent dry episodes. The southwestern China drought resulted from a sequence of dry months from summer 2009 to winter 2010, corresponding to the driest event since at least 1951. The suppression of rainfall in summer over both regions was in agreement with a weakened broad-scale South Asian summer monsoon, possibly influenced by an El Niñ o developing phase, whereas the extremely negative phases of the Arctic Oscillation during the winter of 2010 may have contributed to the persistence of the drought in southwestern China. The assessment of the associated impacts indicates that water reservoirs were severely affected with a ;20% reduction in the nationwide hydroelectrical production during the drought event. Furthermore, an analysis of the normalized difference vegetation index data reveals that large cropland sectors of northern and eastern China experienced up to 8 months of persistently stressed vegetation between May 2009 and July 2010, while southwestern China was relatively less affected. Such different regional vegetative responses are interpreted in terms of the land-cover type, agriculture management, and their dependence on seasonal precipitation and water availability for irrigation.

show abstract

Section: Methodsmentioning

confidence: 99%

The 2009/10 Drought in China: Possible Causes and Impacts on Vegetation

Barriopedro

Gouveia

Trigo

et al. 2012

Journal of Hydrometeorology

215

125

View full text Add to dashboard Cite

show abstract

“…NDVI is related exponentially with Leaf Area Index (LAI) [15]. Is well known that NDVI begins to saturate for a value of LAI equal to 3 reaching a plateau for LAI > 3 (Figure 2 a); 2.…”

Section: Crop Coefficient From Ndvi and Canopy Biophysics Parametersmentioning

confidence: 99%

Remote Sensing Based Crop Coefficients for Water Management in Agriculture

Rocha¹,

Perdigão²,

Melo³

et al. 2012

Sustainable Development - Authoritative and Leading Edge Content for Environmental Management

View full text Add to dashboard Cite

“…These studies have shown narrow-band data to provide additional information over broadband data, enabling significant improvements in quantifying biophysical and biochemical variables of agricultural crops. A number of investigators have studied the relationship between canopy hyperspectral reflectance and canopy properties for major crops [15][16][17]. Hyperspectral studies have been successfully used in assessments of rice yield [13] and chlorophyll content of plants [6,7].…”

mentioning

confidence: 99%

Estimating biophysical parameters of rice with remote sensing data using support vector machines

Yang

Huang

Wu³

et al. 2011

Sci. China Life Sci.

View full text Add to dashboard Cite

Hyperspectral reflectance (350-2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application. Four different transformations of the reflectance data were analyzed for their capability to predict rice biophysical parameters, comprising leaf area index (LAI; m 2 green leaf area m −2 soil) and green leaf chlorophyll density (GLCD; mg chlorophyll m −2 soil), using stepwise multiple regression (SMR) models and support vector machines (SVMs). Four transformations of the rice canopy data were made, comprising reflectances (R), first-order derivative reflectances (D1), second-order derivative reflectances (D2), and logarithm transformation of reflectances (LOG). The polynomial kernel (POLY) of the SVM using R was the best model to predict rice LAI, with a root mean square error (RMSE) of 1.0496 LAI units. The analysis of variance kernel of SVM using LOG was the best model to predict rice GLCD, with an RMSE of 523.0741 mg m −2 . The SVM approach was not only superior to SMR models for predicting the rice biophysical parameters, but also provided a useful exploratory and predictive tool for analyzing different transformations of reflectance data. The assessment of biophysical vegetation properties, such as leaf area index (LAI) and green leaf chlorophyll density (GLCD), is a major goal of remote sensing in agriculture. Remote-sensing-based assessments of these variables are made possible as a result of the strong contrast between spectral reflectances of vegetation and the soil background and the dramatic reflectance changes associated with changing vegetative cover. Based on this contrast, numerous vegetation indices (VIs) have been developed during the past few decades, which are highly correlated with the amount of vegetation. The most common of these indices use the red and near-infrared (NIR) canopy reflectances in the form of ratios, such as the ratio VI [1] and the normalized difference vegetation index [2], and as linear combinations of red and NIR reflectances [3,4]. These indices generally use averaged spectral information over broad bandwidths [5], resulting in the loss of critical information available in specific narrow bands [6], and potentially limiting the accurate estimates of agricultural crop and natural vegetation biophysical and biochemical variables [7,8]. In addition, many of these vegetation indices are strongly influenced by the soil background, resulting in soil-dependent VI-biophysical relationships [9,10]. Further improvements in quantifying vegetation are possible using spectral data from distinct narrow bands, as indicated by numerous hyperspectral studies using field spec-

show abstract

Analyses of spectral-biophysical relationships for a corn canopy

Cited by 97 publications

References 21 publications

The 2009/10 Drought in China: Possible Causes and Impacts on Vegetation

The 2009/10 Drought in China: Possible Causes and Impacts on Vegetation

Remote Sensing Based Crop Coefficients for Water Management in Agriculture

Estimating biophysical parameters of rice with remote sensing data using support vector machines

Contact Info

Product

Resources

About