Multitemporal MODIS Data to Mapping Rice Field Distribution in Bali Province of Indonesia Based on the Temporal Dynamic Characteristics of the Rice Plant (original) (raw)
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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.
Rice is one of the most important staple food crops in Indonesia. The information on rice crop phenology is thus useful for studies of rice crop mapping and monitoring. This study aims to analyze time-series rice crop phenology in the northern part of West Java Province, Indonesia, from Moderate resolution imaging spectroradiometer (MODIS) data. This study area is one of the key rice production regions in the country. There are two rice cropping systems in this region, namely single-cropped rain-fed rice and double-cropped irrigated rice. The data were processed for the 2011 rice cropping seasons. The normalized difference vegetation index (NDVI) was used in this study to investigate the rice crop phenology. Because the MODIS data were contaminated by clouds commonly observed in the tropical region, the noise obscuring in the time-series MODIS NDVI were filtered with the wavelet transform to mitigate errors that potentially affect the results of crop phenology analysis. Three phenological stages including start of the growing season, end of the season, and heading duration of rice crops were investigated from the time-series NDVI profile. Based on information of rice crop phenology, selection of training samples was also carried for rice crop classification using the support vector machine (SVM). The preliminary results indicated the potential of using MODIS data for characterizing rice crop phenology and delineating rice cropping systems in the study area. Due to the unavailability of rice crop calendar, an assessment of the results could not be made in the present study. Our future work was to verify the accuracy of the crop phenology detection and classification results to confirm the validity of the methods..
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
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
2012 Academic Journals Mapping Indonesian paddy fields using multiple- temporal satellite imagery
There is a growing demand for rice with increase in population. As rice is still the major staple food in Indonesia, the task of increasing rice production continues to engage the attention of national planners. Various methods used in estimating rice areas can provide information periodically through different information satellite data, which have a wide coverage area, and can be used as a source of information on the condition of rice areas. This study has an objective of using multi-temporal satellite imagery from the Moderate Resolution Imaging Spectrometer (MODIS) to map the Indonesian rice paddies area. The algorithm was based on temporal profiles of vegetation strength and water content, using electromagnetic surface reflectance in visible to near infrared range. The results obtained from the analysis were compared to national statistics. Estimated Indonesian regional rice area was 8.27 million ha, which agrees with published values. The model performance was dependent on rice ecosystems. Good linear relationships between the model results and the national statistics were observed for all types of rice fields.
Combining ground-based data and MODIS data for rice crop estimation in Indonesia
2015 International Conference on Information Technology Systems and Innovation (ICITSI), 2015
In this study, ground based data from spectroradiometer International Light type ILT900 combined with remotely sensed data from MODIS (Moderate Resolution Imaging Spectrometer) sensor of experimental farmland of the Ministry of Agriculture Republic of Indonesia in Sukamandi, Subang, West Java were used as input data for rice crop estimation using regression analysis. We chose four spectral bands (1-4) of MODIS data and four spectral bands of spectroradiometer data with same (the most similar) wavelength with chosen MODIS data. In addition to the spectral reflectance data, we also measured rice production data from several 7 x 20 plot areas that contain different rice varieties and different fertilizer compositions. The data from spectroradiometer then used for estimating regression model based on two approaches, Principal Component Regression (PCR) and Partial Least Square Regression (PLSR). The evaluation on ground-based data shows that PCR and PLSR give good accuracy with r2 = 0.968 and 0.984 respectively.
Mapping Rice Cropping System in the Lower Gangetic Plain Using Landasat 8 (Oli) and Modis Imagery
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018
The Indo-Gangetic basin is one of the productive rice growing areas in SouthEast Asia. Within this extensive flat fertile land, lower Gangetic basin, especially the south Bengal, is most intensively cultivated. In this study we map the rice growing areas using Moderate Resolution Imaging Spectroradiometer (MODIS) derived 8-day surface reflectance product from 2014 to 2015. The time series vegetation and wetness indices such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) were used in the decision tree (DT) approach to detect the rice fields. The extracted rice pixels were compared with Landsat OLI derived rice pixels. The accuracy of the derived rice fields were computed with 163 field locations, and further compared with statistics derived from Directorate of Economics and Statistics (DES). The results of the estimation shows a high degree of correlation (r = 0.9) with DES reported area statistics. The estimated error of the area statistics while compared with the Landsat OLI was ±15%. The method, however, shows its efficiency in tracing the periodic changes in rice cropping area in this part of Gangetic basin and its neighboring areas.
Relationship between Rice Spectral and Rice Yield Using Modis Data
Journal of Agricultural Science, 2011
Information on the distribution of paddy rice fields is essential for food security and water resource management. Remote sensing is a technique that can be used to obtain rice production information from space. In the present study, Moderate Resolution Imaging Spectroradiometer (MODIS) images were used to evaluate the relationship between the rice spectral (normalized difference vegetation index (NDVI)) of Landsat images and rice yield. The results indicated that the relationship between the NDVI and rice age was best described by a quadratic equation, and R 2 values ranging from 0.916 to 0.973 were obtained. Three growth variables were evaluated in the present study, and the total NDVI (∑NDVI) showed the strongest exponential relationship to rice yield (R 2 = 0.9203) and the lowest standard error of estimation (SE = 0.076). Alternatively, NDVI max and Age NDVImax presented lower R 2 values (0.8919 and 0.5776, respectively) and higher SEs of estimation (0.089 and 0.175, respectively). Thus, the results indicate that rice yield can be estimated from the following equation: y = 0.4745e 0.0504x , where y is the rice yield and x is the ∑NDVI.