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This study aims to detect flooded rice paddies in Indonesia using remotely sensed data from a relatively extensive flood that occurred in the Tegalluar area of Bojongsoang in the spring of 2018, which was observed by the Sentinel-1 and PlanetScope satellites. We propose an automatic thresholding method for the detection of flooded areas in rice paddy fields using Sentinel-1 C-band synthetic aperture radar (SAR) data acquisitions from before and during flooding. The flood-detection accuracy was verified using visible and near-infrared images acquired by the PlanetScope satellites. The proposed method showed that the VV (transmit V and receive V polarizations) data outperformed the VH (transmit V and receive H polarizations) data in terms of correlation ratio and discriminant accuracy. The overall classification accuracy of the nonflooded and flooded areas reached 84.7% with the VV data and 80.6% with the VH data, including the error that resulted from the time difference in the data acquired by Sentinel-1 and PlanetScope. Utilizing speckle-reducing filters with SAR data was found to improve the overall classification accuracy by 5%.

It was reported that 326 Japanese Encephalitis (JE) cases in Indonesia in 2016, majority cases (69.3%) occurred in Bali. It shows that Bali is a prone-area to JE incidence. Previous studies noted that JE is closely related to rural and suburban areas where rice culture and pig farming coexist. This study aims at i) determining knowledge and preventive practices of JE by farmer households; (ii) observing types of mosquitos around farmer households; and (iii) mapping the potency of JE spread using geo-spatial information. Result from this research shows that farmer households have limited knowledge and preventive actions to the incidence of Japanese encephalitis. Preventive actions carried out by respondents were not for JE incidence as such. Nonetheless, farmer’s response to source of vectors and cleanliness are good preventive actions not just to JE incidence but also for other diseases. In this research, setting up mosquito’s light traps nearby pig pens and rice fields has been successful. The result shows that Culex tritaeniorhynchus was dominant type of mosquito trapped. This is an indication that the selected areas are susceptible to the incidence of JE as the Culex sp was reported as the most competent of JE vector in Asia. Maps of the JE spread in Badung regency also coincidence with the places of Culex sp trapped and the dense of rice field. Based on the limitation of knowledge and preventive actions carried out by farmer households, it is important for the stakeholders in the regency including Regional Health Office and health care workers to socialize the occurrence of JE in the community and how to prevent against the disease.

Rice growth monitoring through remote sensing devices, such as Sentinel-2 imagery has been widely used, yet the accuracy level is different due to variations in altitude and object reflectance. Proximal sensing provides site (ground-level) monitoring via high-resolution sensors. Spectroradiometer is a proximal sensing device that can continuously record the spectral pattern of an object. This research aims to examine the spectral pattern characteristic of Grendel sticky and Ciherang rice varieties based on proximal sensing and to investigate the relationship between the reflectance value of spectroradiometer measurements and Sentinel-2 image extraction. Based on the spectral pattern analysis, the Ciherang variety reached the maximum reflectance value for red-edge and NIR wavelength at 56 DAP (reproductive stage) and then decreased at 81 DAP (ripening stage). In comparison, the Grendel variety reached 81 DAP (reproductive stage) and dropped to 86 DAP (ripening stage). Based on linear regression, at 56, 81, 86, and 90, DAP indicated the reflectance values of spectroradiometer measurement are equivalent to the value of sentinel-2 image extraction with an average of R2 above 86%.

This research evaluates damaged rice paddy fields by using remotely sensed data for the agricultural insurance recently launched in Indonesia. The relatively extensive flooding had occurred in the Tegalluar area in Bojongsoang in the 2018 spring. The Sentinel-1 and the PlanetScope satellites observed the Tegalluar area during the flooding period. We propose an automatic thresholding method to detect flood areas in rice paddy fields using two Sentinel-1 C-band SAR data acquisitions pre- and post-flooding. We confirmed the flood detection accuracy using the visible and near-infrared images acquired by the PlanetScope satellites. Our proposed method showed that the VV data outperformed the VH data in correlation ratio and discriminant accuracy. The overall classification accuracy of non-flood and flood areas reached 84.7% with VV data and 80.6% with VH data, including the error caused by the time difference between Sentinel-1 and PlanetScope data acquisitions. Utilizing the speckle reducing filters with SAR data improved the overall classification accuracy by 5 %.

Understanding the temporal and spatial variability in crop yield is considered as one of the key steps in agricultural risk assessment. Therefore, a study of an irrigated area in Cihea, West Java, Indonesia, was conducted to assess rice yield per field using SENTINEL-2 imagery and yield observation data in 2018 and 2019. The study area is located in the Citarum River basin. SENTINEL-2 images were used to derive paddy rice’s growth curve and estimate rice growth stages based on the normalized difference vegetation index. Using these results, the regression model formula using Band 4 (665 nm) and the normalized difference water index in the ripening stage was created (R2 = 0.40, RMSE = 1.21 t/ha). The results from this model were used to generate yield maps, which illustrated a distinct spatial variation in rice yield, such as the average rice productivity in the study area was relatively high, however, the difference between years tended to be small in the upper stream area. The results of this study show that this method is effective in this area to monitor rice yield condition and distribution.Understanding the temporal and spatial variability in crop yield is considered as one of the key steps in agricultural risk assessment. Therefore, a study of an irrigated area in Cihea, West Java, Indonesia, was conducted to assess rice yield per field using SENTINEL-2 imagery and yield observation data in 2018 and 2019. The study area is located in the Citarum River basin. SENTINEL-2 images were used to derive paddy rice’s growth curve and estimate rice growth stages based on the normalized difference vegetation index. Using these results, the regression model formula using Band 4 (665 nm) and the normalized difference water index in the ripening stage was created (R2 = 0.40, RMSE = 1.21 t/ha). The results from this model were used to generate yield maps, which illustrated a distinct spatial variation in rice yield, such as the average rice productivity in the study area was relatively high, however, the difference between years tended to be small in the upper stream area. The results of this study show that this method is effective in this area to monitor rice yield condition and distribution.

Bacterial Leaf Blight (BLB) is one of the main diseases in Indonesia that causes a 90% reduction in grain weight. Multispectral imaging may be used as a quick and effective method for damage assessment and is expected to utilize on agricultural insurance in Indonesia. Data were collected at the rainy season and dry season 2018 in Farmers rice field at Bali Province. Vegetation indices (NDVI, GNDVI, and VARIred-edge) was analyzed using QGIS 2.18 from multispectral images. Some vegetation index shows positive correlation with SPAD and negative correlation with DSI (%). VARIred-edgehas a higher relationship with DSI (R2: 0.8443) than NDVI (R2: 0.8291) and GNDVI (R2: 0.5463) at the average value on each location, but the relation seems to be affected by that relation between SPAD and LAI. Further data and analysis are required.

In Indonesia, there is a need to improve the efficiency of damage assessments of the agricultural insurance system for paddy rice producers affected by floods, droughts, pests, and diseases. In this study, we develop a method to estimate the transplanting date required for damage assessments of paddy rice fields. The study area is the Cihea irrigation district in West Java, Republic of Indonesia. Backscattering coefficients of VH polarization measured by a synthetic aperture radar onboard the Sentinel-1 satellite were used for the estimations. We investigated the accuracy of the estimations of the proposed method by smoothing out the time-series data, applying a speckle filter, and by signal synthesis of the surrounding fields. It was found that these variations effectively improved the estimation accuracy. To further improve the estimation accuracy, the data for all incident angles were used after correcting the incident angle dependence of the backscattering coefficients for three types of data with different incident angles (32&deg, 41&deg, and 45&deg, ) obtained in the study area. The estimated transplanting date for each field in the test site was compared with the transplanting date obtained through interviews. The standard deviations of the estimation errors for the four cropping periods from March 2018 to February 2020 were found to be ~5&ndash, 6 days, and the percentages of estimation errors in transplanting dates within 5, 10, and 15 days were estimated to be 69%, 92%, and 97%, respectively. It was confirmed that a sufficiently reliable transplanting date estimation can be obtained ~10&ndash, 15 days after transplantation.

This study aimed to develop a new method for assessing damage to rice plants by pests and diseases using remote sensing data to enable greater efficiency and accuracy for payment of indemnity in the agricultural insurance system of Indonesia, formally operationalized in 2016. The relationships between bacterial leaf blight (BLB) damage ratio in rice crops evaluated by pest observers using the current visual inspection method and the reflectance of each observation band of RapidEye and Sentinel 2, normalized difference vegetation index (NDVI), green NDVI (GNDVI), and red edge multiplied by the green band index (RGI) were studied. The results showed a positive relationship between BLB damage intensity and reflectance of visible wavelength bands, and a particularly strong positive correlation between the red band and BLB damage intensity. The BLB damage intensity can be evaluated based on pixels and paddy parcels. Time series analysis was conducted using Sentinel-2 data acquired during different plant growth periods such as heading, flowering, ripening, maturity, and harvesting. The results showed a strong correlation between the BLB damage intensity and reflectance of the red edge band at the rice heading and flowering stages; the correlation of BLB damage intensity with the reflectance of the visible range became stronger as the rice plant approached the harvesting stage. This study clearly demonstrated that BLB symptoms can be successfully detected and evaluated approximately one or one and a half months before the harvesting period using remote sensing data. We propose that the BLB damage intensity, currently assessed by pest observers through visual inspection methods, can be calculated from satellite data, suggesting that the satellite sensor could play a role similar to the human eye.

Bacterial leaf blight (BLB) has become a major obstacle and a limiting factor in national rice production. Basically, this disease is caused by the bacterium Xanthomonas oryzae pv. oryzae. Therefore, to minimize the risk of disease infection to yield lost; The Ministry of Agriculture of the Republic of Indonesia has the agriculture insurance program. To support the program, the observations of disease incidence of the disease using existing method are needed. The purpose of this study was to determine the development of bacterial leaf blight on rice plants in Subak Munggu and to determine the effect of bacterial leaf blight on the yield of rice plants. Research activities included (1) isolation of pathogens from symptomatic leaves of rice plants, (2) weekly observations (3) morphological identification of pathogens (4) yields analysis. The results of this study showed the development of bacterial leaf blight in Subak Munggu, Munggu Village, Mengwi Sub-district, Badung Regency for ten weeks. The yield by calculating the weight of grains in each plot was calculated, it can be concluded that a sample that showed a high percentage of disease at the harvest time will cause the low grain yield production.

As a tropical country, Indonesia has two seasons, where each season (rainy or dry seasons) lasts for 6 months. In Indonesia, the beginning of rice planting time occurs at the beginning of the rainy season, but a high diversity of rainfall causes the beginning of rice planting to become varies in some areas. In the area that have a same rainfall, however, the beginning of planting time can be also vary due to the rotation of irrigation water distribution. Seeing this phenomenon, the beginning of planting time is very important to be identified, since it can be used to estimate when the harvest time comes and also the production.The purpose of this study were to identify the growth phase of rice crops based on reflectance and vegetation index using Sentinel-2A imageries and distribution of growth phase of rice crops. The research method begins with a transformation of digital numbers into reflectance values in order to correct the effect of a light source, sensor, atmospheric. Analysis of reflectance (NIR) and vegetation index (NDVI, SAVI, EVI) values were carried out on 56 samples spread over five phases of rice growth, i.e. wet fallow, vegetative, generative, harvesting, dry fallow. The results showed that the NDVI can distinguish five phases of rice growth more clearly than EVI, SAVI, NIR. Pattern of reflectance value and vegetation index in the five phases of rice growth shows a quadratic model. Overall accuracy of the composite temporal of NDVI imageries is small (34,30%). The rice crops in harvesting phase is largest (42,04%)

The objective of this study is to detect flooded area in rice paddy fields in Indonesia by using remotely sensed data. We used synthetic aperture radar (SAR) data for this purpose, because it is capable of getting high-resolution data in almost all-weather conditions. The paper gives a result of detecting flooded area occurred in our research sites located close to Bandung area, Tegalluar in Bojongsoang district, from the end of February to the beginning of March in 2018. The C-band SAR data acquired by Sentinel-1 were used for this analysis. We derived the gamma-naught threshold for dividing flood and non-flood areas by using a linear discriminant analysis. Discriminant accuracy reached 98% using VV polarization. By using the gamma-naught threshold and rice paddy field mask (GIS data), the rice paddy flooded area could be extracted with good accuracy.

One of the causes for the low productivity of rice in Indonesia is the occurrence of rice blast disease. Blast disease is one of the main diseases of rice plants around the world. Therefore to minimise the risk of the lost yield by blast disease, the Ministry of Agriculture of the Republic of Indonesia has the agriculture insurance program. To support these program, the research about observations of blast disease in a fixed location are needed. The objective this research is to confirm pathogens causing the blast disease in the fields, to determine the development of blast disease and its effect to the rice yield (grain weight and straw weight). The study was conducted in a fixed paddy field at Subak Uma Dalem, Sobangan Village, Mengwi District, Badung Regency, Bali Province from February 2019 to April 2019. The data showed that the disease incidence of rice blast increased every week starting from 1st week (28 DAT) until 10th week (104 ATP). Our study confirmed that the fungus caused the blast disease in the field was Magnaporthe oryzae. The higher incidence of blast disease in the field resulted reduced the rice yield by decreasing both grain and straw weights.

Land classification is conducted in Gazipur district, located in the northern neighborhood of Dhaka, the capital of Bangladesh. Images of bands 1 - 5 and 7 of Landsat 4 - 5 TM and Landsat 7 ETM+ imagery recorded in years 2001, 2005 and 2009 are classified using unsupervised classification with the technique of image segmentation. It is found that during the eight year period, paddy area increased from 30% to 37%, followed by the increase in the homestead (55% to 57%) and urban area (1% to 3%). These changes occurred at the expense of the decrease in forest land cover (14% to 3%). In the category of homestead, the presence of different kinds of vegetation often makes it difficult to separate the category from paddy field, though paddy exhibits accuracy of 93.70% - 99.95%, which is better than the values for other categories. In addition, the analysis based on digital elevation model reveals that paddy cultivation is implemented in lowland rather than highland. Homestead areas have spread from south (low elevation) to north (high elevation), in association with the decrease in forest-covered areas.

Mapping rice cultivation is indispensable for monitoring food supply conditions in Bangladesh because of the economical importance of the crop for supporting ever increasing population in the country. In this paper, we extract the rice paddy field using the MODIS satellite data for five districts of Pabna, Manikganj, Sherpur, Sylhet, and Gazipur, each of which is characterized with its own aspects in terms of rice cultivation. Land classification is implemented using the vegetation index information derived from the red (band 1) and near-infrared (band 2) bands of MODIS 8-day composite time series data for the two time periods of 2001-2003 and 2011-2013. Results of unsupervised classification indicate that the paddy area coverage increased about 4% and 1% in Gazipur and Sylhet, respectively. In Pabna, Manikganj, and Sherpur, on the other hand, paddy area decreased by 10%, 2% and 5%, respectively, whereas notable increase of 12%, 2% and 7% was found in homestead area coverage, which is becoming more and more important for better management of small-scale agroforestry. At the same time, in Sherpur and Sylhet, forest area increased by 1% and 2% over the same time period. As a validation of these results, the changes detected in Gazipur are compared with those previously derived from the analysis of Landsat data with higher spatial resolution of 30 m as compared with that of MODIS (250 m). Also, the seasonal rice cropping pattern is studied in these five districts for discriminating cultivated rice types. These changes suggest that as a whole, efforts are being made to increase the food production, though the influence of population pressure and economic growth is apparent in these regions.

Center for Environmental Remote Sensing, Chiba University develops L Band circularly polarized synthetic aperture radar (SAR) sensor for microsatellite (150 kg class). This paper explains the project, specification, antenna deployment, RF system, and experiment of circularly polarized SAR in anechoic chamber. In the future, this sensor will be employed to monitor global land deformation.

This study presents a method for detecting rice crop damage due to bacterial leaf blight (BLB) infestation. Rice crop samples are first analyzed using a handheld spectroradiometer. Then, multi-temporal satellite image analysis is used to determine the most suitable vegetation indices for detecting BLB. The results showed that healthy plants have the highest first derivative value of spectral reflectance of the different categories of diseased plants. Significant difference can be found at approximately 690-770 nm (red edge region) which peak or maximum of the first derivative occurs in healthy crop whereas the highest percentage of BLB showed the lowest in that region. Moreover, visible bands such as blue, green, red, and red edge 1 band show variation of correlation in the early (vegetative) to generative stage then getting high especially in early of harvesting stage than the other bands; the NIR band exhibits a low correlation from the early stage of the growing season whereas the red and red edge bands reveal the highest correlations in the later stage of harvesting. Similarly, the satellite image analysis also reveals that disease incidence gradually increases with increasing age of the plant. The vegetation indices whose formulas consist of blue, green, red, and red edge bands (NGRDI, NPCI, and PSRI) exhibit the highest correlation with BLB infestation. NPCI and PSRI indices indicate that crop stress due to BLB is detected from ripening stage of NPCI then the senescence condition is then detected 12 days later. The coefficients of determination between these indices and BLB are 0.44, 0.63, and 0.67, respectively

Existing methods for rice field classification have some limitations due to the large variety of land covers attributed to rice fields. This study used temporal variance analysis of daily Moderate Resolution Imaging Spectroradiometer MODIS satellite images to discriminate rice fields from other land uses. The classification result was then compared with the reference data. Regression analysis showed that regency and district comparisons produced coefficients of determination R 2 of 0.97490 and 0.92298, whereas the root mean square errors RMSEs were 1570.70 and 551.36 ha, respectively. The overall accuracy of the method in this study was 87.91%, with commission and omission errors of 35.45% and 17.68%, respectively. Kappa analysis showed strong agreement between the results of the analysis of the MODIS data using the method developed in this study and the reference data, with a kappa coefficient value of 0.8371. The results of this study indicated that the algorithm for variance analysis of multitemporal MODIS images could potentially be applied for rice field mapping.

Soil carbon stocks (SCSs) in an upland area of Tokachi District, Hokkaido, Japan, were estimated by satellite remote sensing and a soil survey. The soil parent materials in the studied area were alluvial deposits, volcanic ash plus alluvial deposits, and volcanic ash. Surface soil carbon concentrations (SSCCs) were negatively correlated with satellite image data (green, red, and near-infrared reflectance) for each parent material. The highest correlations between reflectance and SSCCs were obtained from red wavelength reflectance for alluvial deposits (r = –0.82, p

To clarify the effect of soil type on changes in sugar beet (Beta vulgaris L.) productivity since 1980 in Tokachi District (Hokkaido, Japan), we analyzed yield data from 121 settlements from 1980 to 2002 using maps of parent materials and surface organic matter contents in a geographical information system. The soil types were Brown Lowland soils, Andosols with an alluvial subsoil, Wet Andosols and Andosols. The sugar beet yields were highest in the Andosols and moderate in Andosols with an alluvial subsoil. Yields in Brown Lowland soils in the 1980s were similar to those in Andosols, but decreased below the yields in the Andosols by the 1990s. The yields in Wet Andosols were the lowest in the 1980s, but have been similar to those in Andosols with an alluvial subsoil since 1990. Thus, productivity appears to have varied over time in Brown Lowland soils and Wet Andosols. The correlation coefficients between yields and cumulative daily mean temperature from late April to mid-July since 1990 were hi...

The impact of climate change on food production in Indonesia is anticipated to be a level where food production in 2050 will decline ranging from 38% to more than ten-folds of the current production, and it is necessary to improve a cultivation management system aiming at increasing rice yield and also the stability. For this improvement, effective use of irrigation water and water distribution management plan is critical and has to be carefully reviewed and designed. In this study, the amount of irrigation water necessary for rice cultivation at each stage of its growth was calculated and reviewed using satellite data acquired in dry cultivation season for the paddy field of West Java, Indonesia. The results of the study make it clear that the new method developed in the study can calculate and estimate the water amount to be required at each growth stage of rice; the requirement amount can be visualized by each pixel of satellite data; also, the change of the water requirement amount can be checked on almost real-time basis.

Although satellite remote sensing provide large-scale coverage of traditional agricultural region, most “high-resolution” instruments, such as Landsat ETM+, SPOT HRVIR, provide limited descriptive resolution of the Japanese traditional agricultural landscape. With the launch of the IKONOS satellite in 1999, imagery with a 4m spatial resolution in multispectral mode can now be combined with other satellite data archives for change detection. We demonstrate this potential by combining SPOT/Landsat and IKONOS image to detect change in the traditional terrace rice fields for Awaji Island, Hyogo, Japan. According to our analysis using data in 1987 and 2001, losses in rice paddy fields over this period are faster at the hilly mountainous region and slower in flat agricultural region. Synergetic use of IKONOS and SPOT/Landsat appears to be a relevant source of information to show change and fragmentation in traditional terrace rice fields.

Purpose of this study is to estimate rice yield on a plot basis with use of satellite data and field investigation data and to calculate potential quantity of rice straw to be utilized as a raw material for biofuel production in Bali, Indonesia. In addition, for continuous supply of rice straw by farmers to a biofuel producer, it is absolutely necessary to investigate farmer’s interest, behavior and potential issues to solve. For this investigation, an interview was made to the subak heads. The subak is a traditional social organization consisting of farmers for managing irrigation and agriculture in the farmers’ village.The created estimation equation of rice yield had NDVI from SPOT satellite data as a predictor and was significant at 1% level. Based on the rice yield estimated through the equation and the paddy area, quantity of rice straw to be available and quantity of bioethanol to be produced were estimated. In case of Kediri, the rice straw quantity was 42,274 t/year and the ethanol quantity was 12,682 kl.On the other hand, the handling of rice straw after harvesting was leaving/putting back to soil or burning, which accounted for 76% of the rice straw. Concerning a price of rice straw, about 60% of farmers expressed their willingness to sell at 100 rupiah (1 cent US$) per kg, and about 88% including people saying high probability seemed to think this price would be as an adequate level of sales price. Through the interview work, it becomes clear that, when some issues such as price of rice straw are settled, farmers are positive to selling of rice straw for production of biofuel.For realizing bioethanol production from rice straw, there still remain some items to be studied further such as production process, transportation and storage system and costs. The result of our study suggests that a proposal for improvement of stable production can be made through rice yield estimation and monitoring using satellite data and that rice straw can be supplied as a promising resource of raw material for bioethanol production. This is considered to contribute the promotion of activity to reach the national goal of bioethanol production in future.

In Indonesia, the food shortage resulting from a rapid increase in population and also the stability of food production largely affected by climate conditions have been serious problems, and it is necessary to improve a cultivation management system aiming at increasing rice yield and also the stability. The purposes of this study are to estimate the rice yield using remote sensing data and field investigation data considering soil characteristics, and also to grasp spatial distribution of the rice yield in wide area. In this study, at first, the relationship between rice yields of different soil types was examined. The soil type of each field could be classified into 3 types, according to the definition of USA soil taxonomy, which are Inceptisol, Ultisol and Entisol. Evaluation on the difference in yield among the soil types was made with the result that there was significant difference between the two: Entiso and Inceptisol. Then, the correlation between the yield and each satellite band and index was examinded about each soil type and the whole soil. As a result, both of Entisol and the whole soil showed significant correlation on all satellite bands and indexes except short-wavelength infrared, but other soil types had little correlation. This suggests that it would be better to use the whole soil without considering soil types for creating the yield estimation formula from SPOT satellite data and the ground investigated yield data. Finally, multiple linear regression analysis was conducted to develop a yield estimation formula, using the investigated rice yield data and also the reflection density of each band, NDVI, GNDVI, NDWI from SPOT satellite data of harvesting season. As a result, the estimation formula was created using NDVI and middle infrared as predictors (r2=0.428, p

Unusual weather during the summers of 1990's was detrimental to forests and crops. The scientists responsible for the observed forest decline have not been identified, and still trying to find the causes. In this study, measurements of leaf spectral reflectance, photosynthesis, and stomatal conductance of Stewartia pseudocamellia Maxim. (Natsutsubaki) seedlings were carried to detect an indicator of plant water status. Reflectance on infrared wavelength increased with decreasing leaf water content. The slope and inflection point position of the red edge reflectance feature was determined by calculating the first derivative of the spectral curve. A distinctive blue shift, defined here as a shift of the red edge inflection point to shorter wavelengths, is seen in reflectance data from samples collected of the water deficient seedlings. Stress index (SI) was also derived from leaf spectral reflectance data. The photosynthesis and stomatal conductance were affected by low water potential, and these parameters decreased with increasing SI value. The relationships between the physiological parameters of tree seedlings and SI derived from remote sensing data suggest that SI value is useful for detecting and monitoring stress status of forests.

The second issue is environmental problems resulting from the spread of modern agricultural methods. Since the Green Revolution, Asian nations have greatly increased land productivity through the widespread use of modern agricultural methods such as the adoption of highyielding varieties and chemical fertilizers, in response to population growth. However, modern agricultural methods that promote uniform cultivation simplify ecosystems and are harmful to regional biodiversity. This in turn erodes regional characteristics and weakens the ability of regions to adapt to external change, which carries the risk that a major environmental change could result in catastrophic damages.

A rice-planted area map is a basic information resource for rice production management. In this study, a method is proposed to detect the annual rice-planted area by using multi-temporal phased array type L-band synthetic aperture radar. This method addresses two problems: (i) the σ0 value of planted paddies is not consistent; and (ii) annual data fails to reveal time series change of σ0 value in rice-planted area. Data were acquired from both planted and non-planted paddies in the dry season. Accuracy assessment was performed based on field survey data. As a result, the proposed method was effective at revealing the annual rice-planted area. It was also found that data acquired in the rice growth stage contributes to determining the rice-planted area with high accuracy. Further investigation is required that sets more detailed classes to reduce the rate of misclassification and eliminates small regions in detected images.

This study was carried out for the following two purposes through analysis of satellite data and meteorological data. One is to assess the feasibility of developing an efficient cultivation support system for effective reduction of costs relating to sugar beet cultivation. The other is to predict the root yield before the harvesting season. The cumulative temperature, cumulative precipitation and cumulative solar radiation from the end of April to the middle of July were selected as the predictors of the root yield prediction formula. The predictive error was 3.8t/ha, that was a result calculated by the farmers group, after weighted to the predicted root yield using NDVI. The results suggest that it is possible to predict the root yield before three months of the harvesting season. For providing such results to sugar companies in much earlier before the harvesting, the efficiency of sugar beet collection and sugar production will increase substantially.

Forecasting rice yield before harvest time is important to supporting planners and decision makers to predict the amount of rice that should be imported or exported and to enable governments to put in place strategic contingency plans for the redistribution of food during times of famine. This study used the Normalized Difference Vegetation Index (NDVI) of Landsat Enhanced Thematic Mapper plus (ETM+) images of rice plants to estimate rice yield based on field observation. The result showed that the rice yield could be estimated using the exponential equation of y = 0.3419e 4.1587x , where y and x are rice yield and NDVI, respectively. The R 2 and SE of the estimation were 0.852 and 0.077 ton/ha, respectively. An accuracy assessment of rice yield estimation using Landsat images was performed by comparing the rice yields from the estimation result and the reference data. The results show that the linear relationship with the R