Rice grain protein contents that play an important role in the eating quality of rice can be estimated from leaf color in maturing stage. In order to investigate the distribution of paddy rice grain protein of a wide area, we employed SPOT/HRV data from August to September for successive 4 years, selecting the Naganuma town, Hokkaido as the study area. The relationship between each spectral bands and ground survey data were examined. The result showed that the grain protein contents could be estimated using the normalized difference vegetation index (NDVI) with the absolute root mean square error less than 0.4% under the condition that the time lag between the satellite observation date and the maturing stage was within 20 days. In this period, we would have enough chance to get clear observation data every year under the weather conditions in the study area using the SPOT/HRV sensors that has pointing ability. For major rice varieties cultivated in Hokkaido, the same relationship between NDVI and protein contents was observed. Thus, we conclude that the method proposed in this study is operational in rice production.
Every year, the agricultural statistics section of the Japanese government announces rice planting paddy area and rice yield per hectare (ha). At present, the rice planting paddy area is calculated based on field survey by human power. In future, the Japanese government should like to determine the rice transplanted paddy area using remote sensing. Already, some results have come out using satellite-borne optical sensors. However, Japan has a rainy-season at crop growing time, and therefore it is difficult, under such weather condition, to make accurate and consistent observation of paddy fields every year by optical means. On the other hand, Synthetic Aperture Radar (SAR) is capable of observing the earth's surface without influence of clouds. Making use of this all-weather imaging capability, we are currently developing a method to determine the rice planted paddy area using SAR data acquired by RADARSAT. Paddy fields are filled with water during rice-planting period. When the microwave is incident on the filled paddy fields, it is reflected away from the SAR antenna by the water surface acting like a mirror. This phenomenon is called 'specular reflection'. The microwave backscatter is therefore small from the surface covered with water. Thus, the radar cross section (RCS) is very small from rice paddies at a transplanting period due to the specular reflection, and it increases with the growth of rice plants because of volume scatter by stems and leaves, and also by multiple reflection between the water surface and rice plants. In our study, this characteristic is used to develop methods of estimating rice paddy area. Our study area is the Saga plain in the southeast Japan. First, We determine the threshold of image intensity to separate the land and water areas using the histogram and maps. Next, we develop techniques of classification, utilizing (1) RADARSAT and optical data, (2) two multi-temporal RADARSAT data, (3) RADARSAT and GIS data, and (4) two multi-temporal RADARSAT and GIS data. Comparison is then made not only for the accuracy of each methods but also the accuracy of matching the classified areas with municipalities. As a result, we conclude that the threshold value needs to be compensated by taking into account the presence of scattering objects such as houses and creeks around the rice paddies, and that the most accurate method is to use (4) two multi-temporal RADARSAT and GIS data.
South-east Asia has a rainy-season at the crop growing period, and it is difficult to observe agricultural land in this season using optical remote sensing. Synthetic Aperture Radar (SAR) can observe the earth's surface without being influenced by of clouds. However, it is less useful for observing agricultural land, because satellite SAR has only one data band. Recently, SAR is able to provide multi band and multi polarimetric data. Pi-SAR, an airborne SAR developed by NASDA and CRL, can provide L and X bands and fully polarimetric data. Rice is the main crop in Asia, and we studied the characteristic microwave scatter on rice paddy fields using Pi-SAR data. Our study area was the rice paddy fields in Kojima reclaimed land in Japan. We had two fully polarimetric data sets from 13 July 1999 and 4 October 2000. First, we processed the color polarimetric composite image. Next we calibrated the phase of each polarimetric data using river area by the Kimura method. After that we performed decomposition analysis and drew polarimetric signatures for understanding the status of rice paddy fields. At the rice planting period, rice paddy fields are filled with water and rice plants are very small. The SAR microwave scatters on water surfaces like a mirror, called `mirror (or specular) reflection'. This phenomenon makes backscatter a small value at the water-covered area. The image from July is about one month after trans-planting and rice plants are 20-40 cm in height. X-band microwave scatters on the rice surface, but L-band microwave passes through rice bodies and shows mirror refraction on water surfaces. Some strong backscatter occur on rice paddy fields especially VV polarization because of bragg scattering. The fields where bragg scattering returns strong VV scatter because the space between rice stems cause resonation in the L-band wavelength. We can easily understand bragg scatter by using polarimetric data. Using the image from October at just before harvest, L-band polarimetric data can detect various rice statuses such as standing, inclining, or lying. We conclude that multi band and fully polarimetric SAR data can quantity detect crop growth, as do optical sensors in all weather conditions.
The estimation of the rice-planted acreage is an important subject for agricultural administration in Japan. The rice-planted acreage is estimated by statistical calculation based on field survey work, which is implemented every year and consumes a lot of time, labor and budget. It is desirable to develop a estimation method using satellite data which would save time, labor and budget. The pixel-based method (PBM) is the conventional estimation methods using satellite data. In the pixel-based method, rice-planted fields are discriminated by satellite image pixels and acreage is calculated by totalizing the rice-planted pixels' area. Alternatively, a precise estimation method called "Outline data Referring Method (ORM)" discriminates rice-planted plots using satellite data and agricultural plot vector data. The rice-planted acreage is calculated by totalizing the rice-planted plots' area in ORM. Agricultural plot vector data and each plot's acreage can be provided precisely by existing data. This paper is a case study in which the rice-planted acreage is estimated by ORM using EO-1 Hyperion data. Hyperion data has a ground resolution of thirty (30) meters. In order to evaluate the estimation accuracy, the rice-planted acreage was estimated by pixel-based method using Hyperion data. These rice-planted acreages obtained by ORM and pixel-based method were then compared with the reference data calculated from IKONOS data. The estimation error ratio by ORM and PBM was -0.2% and -3.1%, respectively. As the result of this study, ORM has a possibility that rice-planted acreage can be estimated precisely using satellite data such as LANDSAT TM in a region where the size of the satellite image pixel is comparable to the size of paddy plot. Various kinds of satellite data such as LANDSAT TM data are available to estimate the rice-planted acreage by ORM. Therefore the utilization of satellite data for rice-planted acreage estimation will progress in the future.
Paddy rice is one of the most important crops in Japan and it needs much water to grow, especially for water reserves for paddy irrigation. The test site "Owari Seibu" doesn't have enough irrigation water and the water reserves are rotated for a period of about one month in this area. In order to monitor the water reserves condition, we use four satellite data sets (RADARSAT/SAR-C, Landsat/ETM+) and a digital land use map (10m mesh). The results of detecting transplanted paddy field area accurately correspond to statistical data (r2=0.999) at the local level. The distribution map of water reserves made from this analysis shows a rotation pattern over a wide area.
In this paper, an estimation system of rice plant nitrogen contents distribution in the paddy field is discussed for a precision farming by using the proximal remote sensing images. These images are taken at a panicle formation stage and a reduction division stage from a boundary of the rice field by using RGB digital camera. These bird's-eye view images are transformed to top views of a parallel projection to ease mapping. On parallel projected images, RGB brightness levels have a significant correlation with the nitrogen contents even on different time and different direction. Using satellite images are unsuitable for our objects, because of the rainy season, cloudy summer season, the long interval of satellite image and so on.
The estimation of area size of vegetable fields which are covered by different crop kinds and growth stages, is indispensable to keep supply and demand in balance for Japanese agricultural market. Few studies have been reported for vegetable crops, while several attempts using satellite images have been studied for cereal crops, such as rice, wheat, and corn. These cereal crops are cultivated once or twice a year, and each crop is planted simultaneously. Then, the kind and growth stage estimation of cereal crops using satellite images is practicable only with a crop calendar. On the other hand, vegetable crops are planted many times in a year for constant products supply and seasonal labor dispersion, owing to their short growing period. It causes many different growth stages at a same time even for a single kind of vegetation. Therefore, the estimation of both crop kinds and growth stages are necessary because of the unavailability of crop calendars. We have proposed some methods for vegetable crop classification and growth stage estimation using airborne hyperspectral images which have more bands and higher spatial resolution than satellite images, and obtained good results. In this paper, we demonstrate the ability of vegetable crop classification and growth stage estimation using high resolution satellite images that have less spectral information but have better cost performance than airborne hyperspectral images. Our target vegetable is cabbage in three areas, Choshi, Tsumagoi, and Atsumi, where cabbages are produced most for each harvest season in Japan. The accuracy of cabbage identification in luxuriant growth stages, in which the coverage of cabbage is almost 100%, is more than 90% for each area. When all growth stages are included, the accuracy is 76.5% for Choshi, 80.2% for Tsumagoi, and 57.7% for Atsumi. Most errors are caused in fields where the coverage of vegetation is small. Others are caused by misclassification of cabbage as other vegetables, such as purplish cabbage, celery. Besides, Japanese radish, purplish cabbage, cauliflower, and broccoli are misclassified as cabbage. We examined two kinds of growth stage estimation, one is maximum likelihood method and the other is the method using vegetation index, DVVI (Derivative Value for Vegetation Index). The accuracy of growth stage estimation is 53.0% for Choshi, 81.7% for Tsumagoi, and 85.6% for Atsumi. Our method shows usefulness to estimate the yield of cabbage just before the harvest or after the production season, and to monitor the state of growing.
In this paper, we propose a new method of estimating pure spectra and the mixture ratio by applying Independent Component Analysis (ICA) to the agricultural remote sensing images for recognizing fine structured vegetation change on farmland, where the covering plant is unknown. This technique enables us to separate the change of vegetation into qualitative one due to ecological characteristics such as the chlorophyll quantity, and the quantitative coverage one. In the area of remote sensing, several attempts using ICA have been reported. These methods have defined the spectral reflectance pattern in the wavelength domain, as the independent component (IC), in order to extract pure spectra or only spectral features for the classification. In these cases, it is necessary to provide sufficient spectral bands to ensure the independence of each IC, as, for example, with hyperspectral images. In our technique, we define the periodical spatial distribution of crops along the farmland position as the IC, so that pure spectra of crops are estimated as the mixture ratio of the IC, the coverage, unlike the conventional ones. To the simulated mixed spectra, we demonstrated that this technique is useful even when the mixed spectra include vegetation covering fluctuation, and additive noise such as thermal noise from the sensor and atmospheric noise, which are involved in the real data. In addition, by interpreting the coverage as the IC, it is possible to reduce the number of spectral bands. This means that our method can be applied not only to hyperspectral images but also multispectral images.
When the agricultural area is evaluated by the remote sensing image, crop and soil can be considered taking various coverages within a pixel by the growth stage of crop. If the large deviation to the coverage of crop and soil is seen, it was possible to estimate endmembers by the conventional singular value decomposing method. However, if the large deviation to the coverage of crop and soil is not seen, it is difficult to estimate endmembers by the conventional singular value decomposition method. In this paper, by using not only the singular value decomposition method but also three restricted conditions and maximum-minimum average, it became possible to estimate endmembers and coverages more precisely also in such cases.
Locations of wastelands where had been paddy fields in 1987 and 1990 were distinguished by multitemporal NDVI calculated from SPOT(HRV) data. Seasonal profile of NDVI shows characteristic pattern according to land use such as wastelands, paddy fields, conservation paddy fields, forage crop fields, soybean fields and buckwheat fields. NDVI thresholds for several periods to detect wastelands were determined. Wastelands designation by several periods NDVI data was more accurate than by single one.
Remote sensing is powerful technique for large area observation at once. We developed the monitoring system of near real time for agricultural disaster at coastal zone of China using NOAA/AVHRR data. Current ten-day composite of Normalized Difference Vegetation Index (NDVI) is compared with the average of ten day NDVI composite of past three years. The average NDVI images are created using the ten-day composite data of 1997-1999. To reduce the cloud noise, the maximum value of NDVI is used for ten-day composite. The negative NDVI pixels are eliminated at the calculation of the average, since there are the cloud pixels even in ten-day composite. Then, the difference NDVI values are calculated as current composite NDVI minus the average NDVI images and negative difference (<-0.1) pixels are listed as drought risk area in spring and summer. Using the difference NDVI images of Northeast China, we can realize drought damage on agricultural land and grassland.