scholarly journals RICE CROP MAPPING USING SENTINEL-1A PHENOLOGICAL METRICS

2016 ◽  
Vol XLI-B8 ◽  
pp. 863-865 ◽  
Author(s):  
C. F. Chen ◽  
N. T. Son ◽  
C. R. Chen ◽  
L. Y. Chang ◽  
S. H. Chiang
Keyword(s):  
Accuracy Assessment ◽  
Close Correlation ◽  
Rice Crop ◽  
Crop Acreage ◽  
Study Region ◽  
Rice Area ◽  
Multi Temporal ◽  
Crop Mapping ◽  
Cropping Seasons ◽  
Phenological Metrics

Rice is the most important food crop in Vietnam, providing food more than 90 million people and is considered as an essential source of income for majority of rural populations. Monitoring rice-growing areas is thus important to developing successful strategies for food security in the country. This paper aims to develop an approach for crop acreage estimation from multi-temporal Sentinel-1A data. We processed the data for two main cropping seasons (e.g., winter–spring, summer–autumn) in the Mekong River Delta (MRD), Vietnam through three main steps: (1) data pre-processing, (3) rice classification based on crop phenological metrics, and (4) accuracy assessment of the mapping results. The classification results compared with the ground reference data indicated the overall accuracy of 86.2% and Kappa coefficient of 0.72. These results were reaffirmed by close correlation between the government’s rice area statistics for such crops (R<sup>2</sup> > 0.95). The values of relative error in area obtained for the winter–spring and summer–autumn were -3.6% and 6.7%, respectively. This study demonstrates the potential application of multi-temporal Sentinel-1A data for rice crop mapping using information of crop phenology in the study region.

scholarly journals RICE CROP MAPPING USING SENTINEL-1A PHENOLOGICAL METRICS

2016 ◽  
Vol XLI-B8 ◽  
pp. 863-865
Author(s):  
C. F. Chen ◽  
N. T. Son ◽  
C. R. Chen ◽  
L. Y. Chang ◽  
S. H. Chiang
Keyword(s):  
Accuracy Assessment ◽  
Close Correlation ◽  
Rice Crop ◽  
Crop Acreage ◽  
Study Region ◽  
Rice Area ◽  
Multi Temporal ◽  
Crop Mapping ◽  
Cropping Seasons ◽  
Phenological Metrics

Rice is the most important food crop in Vietnam, providing food more than 90 million people and is considered as an essential source of income for majority of rural populations. Monitoring rice-growing areas is thus important to developing successful strategies for food security in the country. This paper aims to develop an approach for crop acreage estimation from multi-temporal Sentinel-1A data. We processed the data for two main cropping seasons (e.g., winter–spring, summer–autumn) in the Mekong River Delta (MRD), Vietnam through three main steps: (1) data pre-processing, (3) rice classification based on crop phenological metrics, and (4) accuracy assessment of the mapping results. The classification results compared with the ground reference data indicated the overall accuracy of 86.2% and Kappa coefficient of 0.72. These results were reaffirmed by close correlation between the government’s rice area statistics for such crops (R<sup>2</sup> > 0.95). The values of relative error in area obtained for the winter–spring and summer–autumn were -3.6% and 6.7%, respectively. This study demonstrates the potential application of multi-temporal Sentinel-1A data for rice crop mapping using information of crop phenology in the study region.

scholarly journals Rice Crop Monitoring and Yield Estimation Through Cosmo Skymed and TerraSAR-X: A SAR-Based Experience in India

2015 ◽  
Vol XL-7/W3 ◽  
pp. 85-92 ◽  
Author(s):  
S. Pazhanivelan ◽  
P. Kannan ◽  
P. Christy Nirmala Mary ◽  
E. Subramanian ◽  
S. Jeyaraman ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Food Security ◽  
Accuracy Assessment ◽  
Rice Crop ◽  
Policy Decisions ◽  
Simulation Accuracy ◽  
Sustainable Solutions ◽  
Rice Area ◽  
Sar Imagery ◽  
Crop Parameters

Rice is the most important cereal crop governing food security in Asia. Reliable and regular information on the area under rice production is the basis of policy decisions related to imports, exports and prices which directly affect food security. Recent and planned launches of SAR sensors coupled with automated processing can provide sustainable solutions to the challenges on mapping and monitoring rice systems. High resolution (3m) Synthetic Aperture Radar (SAR) imageries were used to map and monitor rice growing areas in selected three sites in TamilNadu, India to determine rice cropping extent, track rice growth and estimate yields. A simple, robust, rule-based classification for mapping rice area with multi-temporal, X-band, HH polarized SAR imagery from COSMO Skymed and TerraSAR X and site specific parameters were used. The robustness of the approach is demonstrated on a very large dataset involving 30 images across 3 footprints obtained during 2013-14. A total of 318 in-season site visits were conducted across 60 monitoring locations for rice classification and 432 field observations were made for accuracy assessment. Rice area and Start of Season (SoS) maps were generated with classification accuracies ranging from 87- 92 per cent. Using ORYZA2000, a weather driven process based crop growth simulation model; yield estimates were made with the inclusion of rice crop parameters derived from the remote sensing products viz., seasonal rice area, SoS and backscatter time series. Yield Simulation accuracy levels of 87 per cent at district level and 85- 96 per cent at block level demonstrated the suitability of remote sensing products for policy decisions ensuring food security and reducing vulnerability of farmers in India.

scholarly journals RICE AREA ESTIMATION USING PARAMETERIZED CLASSIFICATION OF SENTINEL 1A SAR DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 141-147 ◽  
Author(s):  
M. G. Raman ◽  
R. Kaliaperumal ◽  
S. Pazhanivelan ◽  
B. Kannan
Keyword(s):  
Satellite Data ◽  
Tamil Nadu ◽  
Accuracy Assessment ◽  
Kappa Index ◽  
Transplanted Rice ◽  
Cropping Season ◽  
Rice Area ◽  
Multi Temporal ◽  
Sar Data ◽  
Crop Parameters

<p><strong>Abstract.</strong> A research study was conducted during <i>Rabi</i> 2016 (Samba season) to estimate rice area using SAR data in Tiruvarur district of Tamil Nadu. Multi temporal Sentinel 1A satellite data with VV and VH polarization at 20&amp;thinsp;m spatial resolution was acquired between September 2016 and January 2017 at 12 days interval and processed using rule-based Parameterized classification in MAPscape-RICE software. Continuous monitoring for crop parameters and validation exercise was done for accuracy assessment. Spectral dB curve of rice was generated and the dB values ranged from &amp;minus;12.76 to &amp;minus;9.95 for VV and from &amp;minus;19.25 to &amp;minus;15.15 for VH polarization with an average primary variation of 1.3 and 2.5&amp;thinsp;dB respectively. Start of Season (SOS) map was derived from satellite data showing rice emergence dates for the cropping season. A total rice area of 106773&amp;thinsp;ha was estimated in Tiruvarur district using VV polarization with an overall accuracy of 79.5% and 0.59 kappa index, while in VH polarization, the rice area was estimated to be 91007&amp;thinsp;ha with 82.1% over all accuracy and 0.64 kappa index. The lesser accuracy in VV polarization was due to underestimate of direct seeded rice area and in VH polarization, it was due to underestimate in Transplanted rice area. The VV and VH rice area maps were then integrated to derive a VV-VH rice area map in MAPscape-RICE software and it recorded a total rice area of 124551 ha with an accuracy of 91.5% and 0.83-kappa index.</p>

scholarly journals Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland

2021 ◽  
Vol 13 (11) ◽  
pp. 2045
Author(s):  
Anaí Caparó Bellido ◽  
Bradley C. Rundquist
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Accuracy Assessment ◽  
Important Variable ◽  
Field Station ◽  
Near Surface ◽  
Fractional Snow Cover ◽  
Rgb Images ◽  
Phenological Metrics

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam Network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve the satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. We developed a semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research employing RGB images only, our use of the monochrome RGB + NIR (near-infrared) reduced pixel misclassification and increased accuracy. The results had an average RMSE of less than 8% FSC compared to visual estimates. Our pixel-based accuracy assessment showed that the overall accuracy of the images selected for validation was 92%. This is a promising outcome, although not every PhenoCam Network system has NIR capability.

scholarly journals Mapping of summer crops in the State of Paraná, Brazil, through the 10-day spot vegetation NDVI composites

2011 ◽  
Vol 31 (4) ◽  
pp. 760-770 ◽  
Author(s):  
Gleyce K. D. Araújo ◽  
Jansle V. Rocha ◽  
Rubens A. C. Lamparelli ◽  
Agmon M. Rocha
Keyword(s):  
Satellite Images ◽  
Accuracy Assessment ◽  
State Level ◽  
The State ◽  
Yield Prediction ◽  
Concordance Index ◽  
Kappa Index ◽  
Cropping Seasons ◽  
Rgb Image ◽  
Resolution Data

The search for low subjectivity area estimates has increased the use of remote sensing for agricultural monitoring and crop yield prediction, leading to more flexibility in data acquisition and lower costs comparing to traditional methods such as census and surveys. Low spatial resolution satellite images with higher frequency in image acquisition have shown to be adequate for cropland mapping and monitoring in large areas. The main goal of this study was to map the Summer crops in the State of Paraná, Brazil, using 10-day composition of NDVI SPOT Vegetation data for 2005/2006, 2006/2007 and 2007/2008 cropping seasons. For this, a supervised digital classification method with Parallelepiped algorithm in multitemporal RGB image composites was used, in order to generate masks of Summer cultures for each 10-day composition. Accuracy assessment was performed using Kappa index, overall accuracy and Willmott's concordance index, resulting in good levels of accuracy. This methodology allowed the accomplishment, with free and low resolution data, of the mapping of Summer cultures at State level.

scholarly journals Bacterial Leaf Blight Detection in Rice Crops Using Ground-Based Spectroradiometer Data and Multi-temporal Satellites Images

2020 ◽  
Vol 12 (2) ◽  
pp. 38
Author(s):  
Rani Yudarwati ◽  
Chiharu Hongo ◽  
Gunardi Sigit ◽  
Baba Barus ◽  
Budi Utoyo
Keyword(s):  
Image Analysis ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Leaf Blight ◽  
Rice Crop ◽  
Bacterial Leaf Blight ◽  
First Derivative ◽  
Red Edge ◽  
Multi Temporal ◽  
Satellite Image Analysis

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

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