Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements

Shibayama, Michio; Akiyama, Tsuyoshi

doi:10.1016/0034-4257(91)90029-6

Cited by 80 publications

(38 citation statements)

References 15 publications

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“…The spectral reflectance of a standard white board, spectralon (LAB SPHER, CITY NH) , was measured for the calibration of spectral reflection coefficient at X nm, Rx defined as (canopy output)/ (white board output) . LRx was defined as Log ( 1/ Rx), which corresponds to absorption (Card et al, 1988;Shibayama and Akiyama, 1991). About 35 seconds was required to complete one scan, and the time lag between measurement of the canopy and white board was about five minutes at most.…”

Section: Plant Materials and Measurementmentioning

confidence: 99%

Statistical Models for Prediction of Dry Weight and Nitrogen Accumulation Based on Visible and Near-Infrared Hyper-Spectral Reflectance of Rice Canopies

Takahashi

Kawaguchi

et al. 2000

Plant Production Science

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Section: Plant Materials and Measurementmentioning

confidence: 99%

Statistical Models for Prediction of Dry Weight and Nitrogen Accumulation Based on Visible and Near-Infrared Hyper-Spectral Reflectance of Rice Canopies

Takahashi

Kawaguchi

et al. 2000

Plant Production Science

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“…Further improvements in quantifying vegetation are possible using spectral data from distinct narrow bands, as indicated by numerous hyperspectral studies using field spec-troradiometers [11][12][13][14]. 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.…”

mentioning

confidence: 99%

“…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%

“…Some researchers have shown that stepwise multiple regression (SMR) models performed on discrete narrow bands provide flexibility in choosing the bands that provide maximum information at a given period of crop growth [29,30]. As crop conditions vary due to factors such as management conditions, soil characteristics, climatic conditions, and cultural practices, different band combinations can be used [13,30]. Recent studies have also successfully used SMR to select the optimal wavelengths that correlate best with leaf biochemistry [31].…”

mentioning

confidence: 99%

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

Yang

Huang

Wu³

et al. 2011

Sci. China Life Sci.

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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-

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“…In comparison to Landsat Enhanced Thematic Mapper data and other broadband sensors, these hyperspectral approaches increased accuracy for crop classification from 9% to 43%. Recent studies observed using field spectroradiometers for assessment of crop biophysical characteristics [15][16][17][18]. These studies have shown that the narrow bands of hyperspectral data provide additional information with significant improvements over broad bands in quantifying biophysical characteristics of agricultural crops.…”

Section: Introductionmentioning

confidence: 99%

Crop Discrimination Using Field Hyper Spectral Remotely Sensed Data

Arafat¹,

Aboelghar²,

Ahmed³

2013

ARS

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Crop discrimination through satellite imagery is still problematic. Accuracy of crop classification for high spatial resolution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still low. Therefore, the main objective of this research is to determine the optimal hyperspectral wavebands in the spectral range of (400 -2500 nm) to discriminate between two winter crops (Wheat and Clover) and two summer crops (Maize and Rice). This is considered as a first step to improve crop classification through satellite imagery in the intensively cultivated areas in Egypt. Hyperspectral ground measurements of ASD field Spec3 spectroradiometer was used to monitor the spectral reflectance profile during the period of the maximum growth stage of the four crops. 1-nm-wide was aggregated to 10-nm-wide bandwidths. After accounting for atmospheric windows and/or areas of significant noise, a total of 2150 narrow bands in 400 -2500 nm were used in the analysis. Spectral reflectance was divided into six spectral zones: blue, green, red, near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA and Tukey's HSD post hoc analysis was performed to choose the optimal spectral zone that could be used to differentiate the different crops. Then, linear regression discrimination (LDA) was used to identify the specific optimal wavebands in the spectral zones in which each crop could be spectrally identified. The results of Tukey's HSD showed that blue, NIR, SWIR-1 and SWIR-2 spectral zones are more sufficient in the discrimination between wheat and clover than green and red spectral zones. At the same time, all spectral zones were quite sufficient to discriminate between rice and maize. The results of (LDA) showed that the wavelength zone (727:1299 nm) was the optimal to identify clover crop while three zones (350:712, 1451:1562, 1951:2349 nm) could be used to identify wheat crop. The spectral zone (730:1299 nm) was the optimal to identify maize crop while three spectral zones were the best to identify rice crop (350:713, 1451:1532, 1951:2349 nm). An average of thirty measurements for each crop was considered in the process. These results will be used in machine learning process to improve the performance of the existing remote sensing software's to isolate the different crops in intensive cultivated lands. The study was carried out in Damietta governorate of Egypt.

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Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements

Cited by 80 publications

References 15 publications

Statistical Models for Prediction of Dry Weight and Nitrogen Accumulation Based on Visible and Near-Infrared Hyper-Spectral Reflectance of Rice Canopies

Statistical Models for Prediction of Dry Weight and Nitrogen Accumulation Based on Visible and Near-Infrared Hyper-Spectral Reflectance of Rice Canopies

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

Crop Discrimination Using Field Hyper Spectral Remotely Sensed Data

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