A study of paddy monitoring system using NOAA and MOS-1 data

A study of paddy monitoring system using NOAA and MOS-1 data

Tsukasa Hosomura, Yoshiaki Matsumae,
Haruhisa Shimoda and Toshibumi Sakata
Tokai University Research & Information Center
Tomigaya 2-28-4, Shibuya-ku, Tokyo 151, Japan

Introduction
This paper describes about the regional paddy monitoring system using remote sensing technology. Target area of this study is large paddy fields spreading over Hua Zhong and Hua Nan area, i.e. basin of Yang Zi river. This area has a regional scale and it is very difficult to get around informations in a timely manner. NOAA AVHRR data is optimal for such monitorings. It is difficult to get the information about aground truth data. In this study, a system in order to obtain the necessary informaions of paddies in this area within certain time internal by using NOAA AVHRR and MOS-1 MESSR images are examined.

This system is composed of the following steps. NOAA AVHRR images are collected in Tokai University Space Infromation Center (TSIC) in Kumamoto, Japan. These data are transported to Tokai University Research & Information Center (TRIC) in Tokyo and processed there. Data processings are composed of three steps. First step is preprocessings which include geometric corrections and radiometric correction. Geometric corrections were conducted to precisely co-register each images. Radiometric distortions mainly caused by incidence angle difference of sun light. In the second step, a cloud free image of the target area was generated by combining images within one or two weeks. In the third step, the information of paddy growing stages were extracted using a combination of band 1 and band 2 of cloud free AVHRR image with the aid of MOS-1 MESSR data as ground truth data. In this step two kinds of processings were examined. One is the false color composite of band 1 and band 2 while the other is the use of vegetation index. In both cases, regional scale paddy conditions have been effectively monitored within reasonable time duration.

Image data used in this study

Test image data
Test image data used in this study are as follows:

Date set 1	NOAA-9 and NOAA-10 1988. May 15 - May . 24	5 images
Data set 2	NOAA-9 and NOAA-10
Data Set 3	NOAA-9 and NOAA-10 1988. June. 24 - June 30	8 images

A NOAA image of the target area is shown in fig 1 which was taken on June 4, 1988. Target area of this study is Hua Zhong and Hua Naan are. i.e basin of Tang Zi river.

Fig. 1 The object area of this study

Images used as ground truth data
Following MOS-1 MESSR images are used as ground truth data.

MOS-1 MESSR. PATH-35 (E), ROW 72, 75, 78

DATA 1 : 1989. MAY. 2

DATA 2 : 1989. JUNE. 5

DATA 3 : 1989.JULY . 2

Preprocessings
A flow chart of the image processings in this study is shown in fig 2. geometric correction an radiometric correction are very important in these processings.

Geometric correction
In this section, geometric correction for AVHRR data are described. In the geometric correction for this study, there are following two problems.
1. Higher geometric accuracy is required to co-register AVHRR data for clouds elimination.
2. Faster processing algorithms for geometric corrections are required to process large quantity of data within a limited duration.
Following processings have been adopted to solve these problems. Faster and accurate geometric corrections were performed by three step processings. At the first step, tangential and each curvature distortions were corrected using table look up algorithm.

In the second step, geometric corrections using orbital elements were accelerated with the aid of scan and pixel functions. One dimensional 3rd and 2nd order polynomials for each function were sufficient to maintain within 1 pixel relative accuracy.

Last step is the co-registration process of images. As most of images are largely covered by clouds, cloud free areas of each image were first selected and a correlation techniques was used to determine control points. Images were then superimposed with the aid of these control points. With these techniques, co-registration of images were achieved within 1 pixel accuracy.

Radiometric distortions mainly caused by incident sun light should be eliminated. In order to eliminate radiometric distortions, sun angle correlations were first applied to geometrically correction images. However, there exists brightness differences between different date images mainly caused by atmospheric conditions. These differences were normalized by histogram normalization process using pixels which can be through to be in the same conditions.

Fig. 2 Flow chart of processing

Clouds elimination
The most primitive idea to generate cloud free image from images is that the channel 1 and 2 values of cloud parts are larger than those of cloud free area. However, the method using this idea also picks up shadows of clouds. In order to avoid this defect, thresholding was introduced to eliminate shadow areas. Thresholding caused another defect that water areas like lakes and rivers were sometimes eliminated as shadows. From these reasons. Therefore, the method using original data values could not applied directly.

In order to eliminate clouds and shadows simultaneously, the method using N.V.I. (normalized vegetation index) was used. NVI can be calculated by the following equation.

N.V.I. = (Channel 2 - channel 1) / )channel 2 + Channel 1)The N.V.I. is then scaled as follows :

Scaled N.V.I. = 240 - (NVI + 0.05) x 350As shown in the above equation, scaled NVI has larger values in cloud of shadow area where the difference of CH. 1 and CH 2 is relatively small. On the contrary, scaled NVI in water area has not so large value because there exist some differences between Ch. 1 and Ch. 2. Cloud free images could be generated by taking the area which have the smallest scaled NVI value.

Monitoring of the paddy fields
Two kinds of cloud free images were generated for monitoring the informations of paddy growing states. One is the false color composite image of band 1 and band 2. The other is the use of normalized vegetation index (N.V.I).

After eliminating the clouds, comparisons of paddy fields in three terms (mid-may, early June and late June) were executed. For the interpretation of these images, MOS-1 data were used as ground truth data. Fig 3 shows an example of MOS-1 MESSR data (1988 June 5, 35-75E).

The image taken on mid-May is shows in Fig4. This is the false color composite (Ch.1-B, Ch.1-G, Ch.2-R) image. In this image, dark tone appears in South area and red tone appears in north area. This shows that rice-planting have started in south area but there are some plants in north area.

Next, the image taken on early, June is shown in Fig 5. In this image, dark tone appears in central area and red tone planting have started in central area and rice grows in south area and there still remains some plants in north area.

At last, the image taken on late June is shown in Fig. 6 in this image, dark tone appears in north area and red tone appears in south and central area. This shows that rice planting have started in north area and rice grows in central and south area.

From these three images, it was clarified that rice-planting moves from south to north day by day. From NVI images calculated for three terms, the same tendency could be recognized as color composite images.

Fig. 3 An example of MOS-1 Messr images
( 1988. June. 5, 35-75E )

Fig. 4 Cloud free color composite image ( Mid-May)

Fig. 5 Cloud free color composite image ( Early June )

Fig. 6 Cloud free color composite image ( Late June )

Conclusions

The regional paddy monitoring system was established
The accuracy of systematic geometric correction was improved by introducing tangential and earth curvature correction.
Radiometric correction were performed by using sun angle corrections and histogram normalized techniques.
Two kinds of cloud free images were generated for monitoring the rice growing stages. One is the false color composite image of band 1 and band 2. the other is the use of normalized vegetation index (NVI). Rice growing stages could be monitored using either images.