Modeling rice growth from characteristics of
Reflectance spectra
Chwen-Ming Yang* and Muh-Rong
Su
Agronomist and Assistant, Department of Agronomy
Taiwan Agricultural Research Institute, Council of Agriculture
189 Chung-Cheng Road, Wufeng, Taichung Hsien,
Taiwan 41301, ROC
Tel: (886)-4-330-2301 ext.135, Fax: (886)-4-330-2806
E-mail: cmyang@wufeng.tari.gov.tw
Keywords: Modeling, Rice Growth, Reflectance Spectra, Vegetation index, Satellite
Abstract
A three-year (1996-1998) experiment was conducted at Taiwan Agricultural
Research Institute (TARI) Experimental Farm, Wufeng to modeling of rice growth from
characteristics of reflectance spectra. The growth-waveband relationships were analyzed by
different reflectance transformation techniques and were compared among several satellite
sensors. The narrow-band ground remotely sensed spectral data were acquired by a portable
spectroradiometer and the broad-band multispectral satellite inputs were simulated from the
ground measurements. Physical growth characters leaf number (LN), plant height (PH), leaf
area index (LAI), leaf dry weight (LDW), and aboveground dry weight (ADW) were measured
periodically. Results indicate a diverse correlation coefficients between growth characters and
different vegetation indices and the wavebands set in satellite sensors. For linear relationship,
correlation with vegetation index (VI) was generally higher than with single-waveband of
satellites sensors. In a curvilinear relationship, correlation between growth character (e.g. LAI)
and VI (e.g. NDVI) was greatly improved, and growth character was also better estimated from
reflectance of multi-waveband of satellite data. Results suggest that rice growth may be
reasonably assessed and monitored, either from the ground or satellites, from characteristics of
reflectance spectra when proper wavebands are selected.
Introduction
Remote sensing technology (RS) is currently an effective tool widely adopted in various aspects
of exploitation and management of natural resources. It provides a timely detailed spatially
distributed information allowing one to measure, monitor, and analyze the time sequence
changes of target(s) possible. In agriculture, RS has been employed to the development of
precision farming for the better of crop production and environment protection. Nevertheless,
the surface reflectance spectra over a wide range of objects and conditions should be identified
and interpreted into meaningful outputs prior to decision-making and applications. For such
purposes, data bank including spectral properties of physical characteristics of crops and land
cover and the corresponding mathematical models have to be established based on ground truth.
The in situ remotely sensed datasets from aircraft or satellite can therefore be reasonably
resolved and reconstructed to the original state.
Yang and Su (1997) simplified the relationship between LAI and the near-infrared reflectance
(756 nm) of rice canopy to the reverse of the Mitscherlich function. Yang and Su (1998a, 1998b)
found a curvilinear link between growth characters (LDW and LAI) and normalized difference
vegetation index (NDVI) in rice crops from the ground spectral measurements. Su and Yang
(1999) further supported the fact by suggesting exponential formulae for estimating the
advancement of growth characters from NDVI. However, it would be invaluable to learn
whether the narrow-band models be applicable to the broad-band measures of satellite sensors
in the real world applications.
The present study is to modeling of rice growth from spectral characteristics of vegetative cover.
The growth-waveband relationships were analyzed and compared by different reflectance
transformation techniques among a number of satellite sensors. The broad-band multispectral
satellite inputs (Landsat-TM, Landsat-MSS, and Spot-HRV) were simulated from the
narrow-band ground measurements and rice growth was assessed from physical growth
characters investigations.
Materials and Methods
Rice (Oryza sativa L. cv. Tainung 67) were grown in the experimental farm of TARI, Wufeng
(24° 02’ N, 120° 40’ E, elevation of 85 m) on a loam soil during the 1
st and the 2
nd crops in
1996-1998. Seedlings were machine-transplanted to north-south rows with planting density of
1.92 × 10
5 hills ha
-1 , in 1996 and 1997, and 2.22 × 10
5 hills ha
-1 , in 1998, respectively. Each
crop had 3 plots (replicates) with plot size of 18 m × 11 m. Cultivation and fertilization
followed local cultural practices with furrow irrigation. Pest and weed control was applied as
needed to avoid pest and weed interference. Growth characters (GC) of LN, PH, LAI, LDW,
and ADW were measured periodically. PH was taken by a ruler. LN was counted at the time of
area measurements, which was determined by a portable area meter (model LI-3000A, LI-COR
Inc., USA). LAI was calculated by the area of green leaves over unit area of land. LDW and
ADW were weighted after oven-dried at 80°C for 72 h.
Radiance from the incident solar radiation and vegetative cover were acquired under the same
sun conditions to calculate reflectance spectra of rice periodically during the growing periods of
1996-1998. The reflectance (%) of individual wavelength was calculated by dividing the
vegetation radiance measurements with the corresponding incident solar radiation
measurements (Yang and Su, 1997, 1998a; Su and Yang, 1999). A LI-COR model-1800
portable spectroradiometer, with 2-nm bandpasses in the range of 350-1100 nm, was used for
the ground measurements. It was connected to a quartz fiber-optic probe (LI-1800-10) and a
remote cosine receptor pointed downward in a nadir-viewing about 1.0 m above rice vegetative
surface. This distance was adjusted to plant height, by a tripod, to scan the upward reflected
radiation of canopy. Measurements were made on clear or near cloudless days within
11:00-12:00 local standard time and the average values were used. Characteristics wavelengths
of reflectance spectra were determined by using the first order differentiation in cope with
valley and peak observations of spectral waves over six cropping seasons (Su and Yang, 1999).
Wavelengths at 554, 674, and 754 nm were selected as the characteristics wavelengths (CW).
They coincide with the absorption minimum and maximum of chlorophyll and the near-infrared
boundary of the chlorophyll red-edge, respectively. The corresponding wavebands of
LANDSAT-TM, LANDSAT-MSS, and SPOT-HRV sensors to CW are listed in Table 1.
Table 1. Comparisons of the spectral characteristics of different satellite sensors with the characteristics wavelengths (CW)
determined from reflectance spectra of the ground measurements.
|
| Landsat-TM | Landsat-MSS | Spot-HRV | CW |
|
| | -------------------- nm -------------------- | |
| 450-520 (TM1) | | | |
| 520-600 (TM2) | 500-600(MSS1) | 500-590(HRV1) | 554(GREEN) |
| 630-690 (TM3) | 600-700(MSS2) | 610-680(HRV2) | 674(RED) |
| 760-900 (TM4) | 700-800(MSS3) | 790-890(HRV3) | 754(NIR) |
| 800-1100(MSS4) | | |
|
Six VIs were selected from the literature (Elvidge and Chen, 1995; Kanemasu, 1974; Rouse et
al., 1974; Tucker, 1979) defined as the followings: (1) sum vegetation index (SVI): NIR+RED;
(2) difference vegetation index (DVI): NIR-RED; (3) ratio vegetation index (RVI): NIR/ RED;
(4) green-red ratio vegetation index (GRVI): GREEN/RED; (5) normalized difference
vegetation index (NDVI): (NIR-RED)/(NIR+RED); and (6) soil-adjusted vegetation index
(SAVI): (NIR-RED)× 1.5/ (NIR+RED+0.5). The broad-band inputs of satellite sensors were
simulated from the ground narrow-band reflectance (Table 1). Correlation matrices were
applied for GC and wavebands and vegetation indices comparisons. Regression analyses were
performed to generate fitting-curves and equations in order to monitoring the comparative
changes of GC and wavebands and vegetation indices as plants aged.
Table 2. Correlation coefficients for growth characters of rice and vegetation indices (VI) calculated from characteristics
wavelengths (CW) and the simulated wavebands of different satellite sensors.
| VI | Leaf Number |
Plant Height | Leaf area index |
Leaf dry weight | Aboveground dry weight |
| Landsat-TM | NDVI | 0.791 |
0.372 | 0.833 |
0.731 | 0.188 |
| SAVI | 0.790 |
0.374 | 0.835 |
0.734 | 0.192 |
| RVI | 0.815 |
0.187 | 0.881 |
0.744 | 0.071 |
| DVI | 0.598 |
0.535 | 0.871 |
0.818 | 0.430 |
| SVI | 0.284 |
0.648 | 0.683 |
0.699 | 0.609 |
| GRVI | 0.830 |
0.024 | 0.800 |
0.631 | -0.048 |
| Landsat-MSS | NDVI |
0.819 | 0.318 | 0.844 |
0.730 | 0.137 |
| SAVI | 0.817 |
0.322 | 0.846 |
0.733 | 0.143 |
| RVI | 0.829 |
0.181 | 0.873 |
0.734 | 0.054 |
| DVI | 0.596 |
0.510 | 0.853 |
0.795 | 0.415 |
| SVI | 0.053 |
0.623 | 0.470 |
0.522 | 0.645 |
| GRVI | 0.854 |
-0.063 | 0.756 |
0.575 | -0.142 |
| Spot-HRV | NDVI |
0.791 | 0.380 |
0.835 | 0.735 |
0.193 |
| SAVI | 0.786 |
0.389 | 0.839 |
0.740 | 0.202 |
| RVI | 0.817 |
0.200 | 0.881 |
0.747 | 0.073 |
| DVI | 0.591 |
0.546 | 0.870 |
0.820 | 0.439 |
| SVI | 0.279 |
0.654 | 0.682 |
0.700 | 0.616 |
| GRVI | 0.854 |
-0.022 | 0.780 |
0.605 | -0.107 |
| CW | NDVI |
0.800 | 0.329 |
0.825 | 0.717 |
0.155 |
| SAVI | 0.799 |
0.333 | 0.827 |
0.718 | 0.159 |
| RVI | 0.798 |
0.161 | 0.874 |
0.733 | 0.071 |
| DVI | 0.630 |
0.475 | 0.865 |
0.798 | 0.379 |
| SVI | 0.306 |
0.584 | 0.667 |
0.670 | 0.558 |
| GRVI | 0.783 |
0.069 | 0.810 |
0.653 | 0.016 |
