Mapping Paddy Rice Area and Yields Over Thai Binh Province in Viet Nam From MODIS, Landsat, and ALOS-2/PALSAR-2 (original) (raw)
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IEEE journal of selected topics in applied earth observations and remote sensing, 2018
Paddy Rice is the prevalent land cover in the mosaicked landscape of the Hanoi Capital Region, Vietnam. In this study, we map double and single crop rice in Hanoi using a random forest algorithm and a time-series of Sentinel-1 SAR imagery at 10 and 20 m resolution using VV-only, VH-only, and both polarizations. We compare spatial and areal variation and quantify input band importance, estimate crop growth stages, estimate rice field/collective metrics using Fragstats with image segmentation, and highlight the importance of the results for land use and land cover. Results suggest double crop rice ranged from 208 000 to 220 000 ha with 20-m resolution imagery accounting for the most area in all polarizations. Based on accuracy assessment, we found 10 m data for VV/VH to have highest overall accuracy (93.5%, ±1.33%), while VV at 10 and 20 m had lowest overall accuracies (90.9%, ±1.57; 91.0%, ±2.75). Mean decrease in accuracy suggests for all but VV at 10 m, data from harvest and floodi...
GIScience & Remote Sensing
Acquiring accurate and timely information on the spatial distribution of paddy rice fields and the corresponding yield is an important first step in meeting the regional and global food security needs. In this study, using dense vegetation index profiles and meteorological parameters from the Communication, Ocean, and Meteorological Satellite (COMS) geostationary satellite, we estimated paddy areas and applied a novel approach based on a remote sensing-integrated crop model (RSCM) to simulate spatiotemporal variations in rice yield in Northeastern Asia. Estimated seasonal vegetation profiles of plant canopy from the Geostationary Ocean Color Imager (GOCI) were constructed to classify paddy fields as well as their productivity based on a bidirectional reflectance distribution function model (BRDF) and adjusted normalized difference vegetation indices (VIs). In the case of classification, the overall accuracy for detected paddy fields was 78.8% and the spatial distribution of the paddy area was well represented for each selected county based on synthetic applications of dense-time GOCI vegetation index and MODIS water index. For most of the Northeast Asian administrative districts investigated between 2011 and 2017, simulated rice mean yields for each study site agreed with the measured rice yields, with a root-mean-square error of 0.674 t ha −1 , a coefficient of determination of 0.823, a Nash-Sutcliffe efficiency of 0.524, and without significant differences (p-value = 0.235) according to a sample t-test (α = 0.05) for the entire study period. A well-calibrated RSCM, driven by GOCI images, can facilitate the development of novel approaches for the monitoring and management of crop productivity over classified paddy areas, thereby enhancing agricultural decision support systems.
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.
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...
Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
Remote Sensing
Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach.
Indian Journal Of Agricultural Research, 2021
Background: MODerate resolution imaging spectroradiometer (MODIS) is the crucial instrument aboard. It provides global maps of several land surface characteristics. Method: The study uses MODIS to delineate the rice sowing and progress of the rice cropping calendar in the Vietnamese Mekong Delta. The study used multi-time series of normalized difference vegetation index (NDVI) images from 250 m spatial resolution MOD13Q1 images with 16-day combination to determine rice sowing/planting and harvesting schedules (from 01/01/2008 to 30/09/2009). Using 82 MODIS images, the study calculates the NDVI time series for rice sowing/transplanting stages in the Mekong Delta. Over time, the relationship between NDVI values and the rice cropping stages determines each cropping season starting and ending time. Result: As a result, we delineate three (3) major rice cropping systems and eight (8) cropping seasons. In which Main Winter-Spring and Early Summer-Autumn and Late Main Winter-Spring and Mai...
Remote Sensing
Leaf area index (LAI) is a key biophysical variable fundamental in natural vegetation and agricultural land monitoring and modelling studies. This paper is aimed at comparing, validating and discussing different LAI satellite products from operational services and customized solution based on innovative Earth Observation (EO) data such as Landsat-7/8 and Sentinel-2A. The comparison was performed to assess overall quality of LAI estimates for rice, as a fundamental input of different scale (regional to local) operational crop monitoring systems such as the ones developed during the "An Earth obseRvation Model based RicE information Service" (ERMES) project. We adopted a multiscale approach following international recognized protocols of the Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) guidelines in different steps: (1) acquisition of representative field sample measurements, (2) validation of decametric satellite product (10-30 m spatial resolution), and (3) exploitation of such data to assess quality of medium-resolution operational products (~1000 m). The study areas were located in the main European rice areas in Spain, Italy and Greece. Field campaigns were conducted during three entire rice seasons (2014, 2015 and 2016-from sowing to full-flowering) to acquire multi-temporal ground LAI measurements and to assess Landsat-7/8 LAI estimates. Results highlighted good correspondence between Landsat-7/8 LAI estimates and ground measurements revealing high correlations (R 2 ≥ 0.89) and low root mean squared errors (RMSE ≤ 0.75) in all seasons. Landsat-7/8 as well as Sentinel-2A high-resolution LAI retrievals, were compared with satellite LAI products operationally derived from MODIS (MOD15A2), Copernicus PROBA-V (GEOV1), and the recent EUMETSAT Polar System (EPS) LAI product. Good agreement was observed between high-and medium-resolution LAI estimates. In particular, the EPS LAI product was the most correlated product with both Landsat/7-8 and Sentinel-2A estimates, revealing R 2 ≥ 0.93 and RMSE ≤ 0.53 m 2 /m 2. In addition, a comparison exercise of EPS, GEOV1 and MODIS revealed high correlations (R 2 ≥ 0.90) and RMSE ≤ 0.80 m 2 /m 2 in all cases and years. The temporal assessment shows that the three satellite products capture well the seasonality during the crop phenological cycle. Discrepancies are observed mainly in absolute values retrieved for the peak of rice season. This is the first study that provides a quantitative assessment on the quality of available operational LAI product for rice monitoring to both the scientific community and users of agro-monitoring operational services.
Different rice crop information can be derived from different remote sensing sources to provide information for decision making and policies related to agricultural production and food security. The objective of this study is to generate complementary and comprehensive rice crop information from hypertemporal optical and multitemporal high-resolution SAR imagery. We demonstrate the use of MODIS data for rice-based system characterization and X-band SAR data from TerraSAR-X and CosmoSkyMed for the identification and detailed mapping of rice areas and flooding/transplanting dates. MODIS was classified using ISODATA to generate cropping calendar, cropping intensity, cropping pattern and rice ecosystem information. Season and location specific thresholds from field observations were used to generate detailed maps of rice areas and flooding/transplanting dates from the SAR data. Error matrices were used for the accuracy assessment of the MODIS-derived rice characteristics map and the SAR-derived detailed rice area map, while Root Mean Square Error (RMSE) and linear correlation were used to assess the TSX-derived flooding/transplanting dates. Results showed that multitemporal high spatial resolution SAR
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