2012 Academic Journals Mapping Indonesian paddy fields using multiple- temporal satellite imagery (original) (raw)
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Earth Science …, 2012
Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite data has been widely employed for many applications. It has fine temporal, spectral and spatial resolution. This feature can be used for monitoring earth condition continuously, such as the rice field distribution. Rice fields can be known by detecting the rice plant in that area. Enhanced Vegetation Index 2 (EVI2) is one of the indexes, which describe vegetation conditions and was employed to mapping the rice field. Rice field area was identified using growth curve recognition of EVI2 MODIS data based on rice plant temporal dynamic characteristics. The rice field distributions in Bali that were estimated from MODIS data in 2009 show reasonable spatial agreement with rice field distribution from land use map of 2008. The total area of rice field from MODIS data is 101,218.75 hectare (accuracy 88.21% of reference data). The southern part of Bali has wider rice coverage compared to northern part of Bali because of the topographic condition in southern Bali is suitable for rice cultivation. The regency and district level comparison of rice field area in Bali Province showed a good spatial agreement of accuracy. This indicates that MODIS EVI2 250 m data can be used to mapping homogeneous areas in the small region of district scale. The accuracy identify rice field are affected by several factors, such as spatial resolution and cloud cover of satellite data, elevation of rice field area, and type of rice field.
Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images
Remote Sensing of Environment, 2006
In this paper, we developed a new geospatial database of paddy rice agriculture for 13 countries in South and Southeast Asia. These countries have¨30% of the world population and¨2/3 of the total rice land area in the world. We used 8-day composite images (500-m spatial resolution) in 2002 from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period a mixture of surface water and rice seedlings exists. We applied a paddy rice mapping algorithm that uses a time series of MODIS-derived vegetation indices to identify the initial period of flooding and transplanting in paddy rice fields, based on the increased surface moisture. The resultant MODIS-derived paddy rice map was compared to national agricultural statistical data at national and subnational levels. Area estimates of paddy rice were highly correlated at the national level and positively correlated at the subnational levels, although the agreement at the national level was much stronger. Discrepancies in rice area between the MODIS-derived and statistical datasets in some countries can be largely attributed to: (1) the statistical dataset is a sown area estimate (includes multiple cropping practices); (2) failure of the 500-m resolution MODIS-based algorithm in identifying small patches of paddy rice fields, primarily in areas where topography restricts field sizes; and (3) contamination by cloud. While further testing is needed, these results demonstrate the potential of the MODIS-based algorithm to generate updated datasets of paddy rice agriculture on a timely basis. The resultant geospatial database on the area and spatial distribution of paddy rice is useful for irrigation, food security, and trace gas emission estimates in those countries. D
2010
Information about an area and its distribution of planted and harvested paddy rice field is essential for food security management, water resource management, and for the estimation of gas emissions. Rice field has specific coverage characteristics throughout its life time. Its coverage is mixed proportionally in accordance with its age, between water, soil, and rice vegetation. Landsat ETM+ has a good spatial, spectral, and temporal resolution for rice growth monitoring and production estimation. The study was done in Tabanan Regency, Bali Province, Indonesia. The objectives of the study were: (1) to develop the rice growth vegetation index (RGVI), (2) to map the rice distribution and its age, and (3) to quantitative compare the rice field area’s analysis results with the reference data. The results of this study show that an exponential equation is the best relationship form between a rice field spectral and the rice age. The visible bands of Landsat ETM+ (Band 1, Band 2, and Band...
Mapping paddy rice agriculture in southern China using multi-temporal MODIS images
Remote Sensing of Environment, 2005
Information on the area and spatial distribution of paddy rice fields is needed for trace gas emission estimates, management of water resources, and food security. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period open canopy (a mixture of surface water and rice crops) exists. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite has visible, near infrared and shortwave infrared bands; and therefore, a number of vegetation indices can be calculated, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) that is sensitive to leaf water and soil moisture. In this study, we developed a paddy rice mapping algorithm that uses time series of three vegetation indices (LSWI, EVI, and NDVI) derived from MODIS images to identify that initial period of flooding and transplanting in paddy rice fields, based on the sensitivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting. We ran the algorithm to map paddy rice fields in 13 provinces of southern China, using the 8-day composite MODIS Surface Reflectance products (500-m spatial resolution) in 2002. The resultant MODIS-derived paddy rice map was evaluated, using the National Land Cover Dataset (1:100,000 scale) derived from analysis of Landsat ETM+ images in 1999/2000. There were reasonable agreements in area estimates of paddy rice fields between the MODIS-derived map and the Landsat-based dataset at the provincial and county levels. The results of this study indicated that the MODIS-based paddy rice mapping algorithm could potentially be applied at large spatial scales to monitor paddy rice agriculture on a timely and frequent basis. D
International Journal of Remote Sensing, 2012
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.
Availability of remote sensed data provides powerful access to the spatial and temporal information of the earth surface. Real-time earth observation data acquired during a cropping season can assist in assessing crop growth and development performance. As remote sensed data is generally available at large scale, rather than at field-plot level, use of this information would help to improve crop management at broad-scale. Utilizing the Landsat TM/ETM+ ISODATA clustering algorithm and MODIS (Terra) the normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) datasets allowed the capturing of relevant rice cropping differences. In this study, we tried to analyze the MODIS (Terra) EVI/NDVI (February, 2000 to February, 2013 datasets for rice fractional yield estimation in Narowal, Punjab province of Pakistan. For large scale applications, time integrated series of EVI/NDVI, 250-m spatial resolution offer a practical approach to measure crop production as they relate to the overall plant vigor and photosynthetic activity during the growing season. The required data preparation for the integration of MODIS data into GIS is described with a focus on the projection from the MODIS/Sinusoidal to the national coordinate systems. However, its low spatial resolution has been an impediment to researchers pursuing more accurate classification results and will support environmental planning to develop sustainable land-use practices. These results have important implications for parameterization of land surface process models using biophysical variables estimated from remotely sensed data and assist for forthcoming rice fractional yield assessment.
Agricultural land use information is required to provide timely spatial information to formulate national food security policies. This study, focused on Paddy rice mapping in Kashmir Himalayan region using multi-temporal data from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. As part of process, the potential of using satellite spectral reflectance measurements to map and monitor paddy rice in region was evaluated. MODIS satellite data and ground-based field measurements were used to establish the efficacy of results based on remotely sensed indices. The two indices studied were the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The water inundation during the initial stage of paddy crop transplantation were used as inputs for Normalized Difference Water Index (NDWI) and absence of vegetation at latter stages of harvest were used as inputs for Normalized Difference Vegetation Index (NDVI) to identifying the initial ...
Mapping rice areas of South Asia using MODIS multitemporal data
Journal of Applied Remote Sensing, 2011
Our goal is to map the rice areas of six South Asian countries using moderateresolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the world's harvested rice area and is also home to 74% of the population that lives on less than $2.00 a day. The population of the region is growing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R 2 values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). Use: http://spiedl.org/terms Gumma et al.: Mapping rice areas of South Asia using MODIS multitemporal data
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018
This study uses multiple satellite datasets to map paddy rice areas and yields for the Thai Binh Province, Viet Nam, over the summer growing season of 2015. The major datasets used are: first, surface reflectance and vegetation indices (VI) by fusing the optical observations from the Landsat sensors and the MODerate Resolution Imaging Spectroradiometer; and second, the L-band radar data from the PALSAR-2 sensor onboard the Advanced Land Observing Satellite 2. We find that although the fused VI time series are not necessarily beneficial for paddy rice mapping, the fusion datasets reduce observational gaps and allow us to better identify peak VI values and derive their empirical relationships with crop-cutting yield data (R 2 = 0.4 for all the rice types, and R 2 = 0.69 for the dominant rice type −58% of all the sampled fields). The L-band radar data have slightly lower performance in rice mapping than the optical satellite data, while it has much less contribution to yield estimation than the optical data. Furthermore, our study suggests the geolocation errors of satellite images be taken into account when selecting small sample areas for crop cutting. This practice will ensure the representativeness of crop-cutting sample areas with regard to satellite observations and thus better linkages between field data and satellite pixels for yield Manuscript
2011
Rice is a staple food for Indonesia. For some time, a food availability approach was used for based of Indonesia’s food security program. Monitoring and early warning systems is one of inherent components in the implementation of food security paradigm. Spatial information about paddy plated area and production is an important element for monitoring in agriculture. Further, the data spatially and time series, both historically and in real time, is required for consideration in the planning management and development of agricultural land. Purpose of his study is to assess spectral character of ALOS satellite imagery and combination of multi-sensors SAR and Optic to identify paddy planting area in paddy field. Research has conducted on Subang District area, West Java Province. Data used is PALSAR for analyzing of backscatter and soil moisture content and ALOS AVNIR-2 is for analyzing of NDVI and checking of land cover visually, with acquisition date on 10 May 2007. Research result sho...