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16
Integrating SAR and optical imagery for regional mapping of paddy rice attributes
- in the Poyang Lake
"... Abstract. The development of agricultural monitoring tools is a focus of the Group on Earth Observations for the Global Earth Observation System of Systems (GEOSS). This requires combining synthetic aperture radar (SAR) and optical satellite sensors to provide more meaningful information in an opera ..."
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Abstract. The development of agricultural monitoring tools is a focus of the Group on Earth Observations for the Global Earth Observation System of Systems (GEOSS). This requires combining synthetic aperture radar (SAR) and optical satellite sensors to provide more meaningful information in an operational context. The goal of this research was to characterize paddy rice agro-ecological attributes in an operational context. A decision tree framework combined multitemporal and multiscale PALSAR, MODIS, and Landsat observations to map rice extent, hydroperiod, crop calendar, and cropping intensity. The study was carried out in the Poyang Lake Watershed, Jiangxi Province, China. A multiseason field campaign was carried out to calibrate algorithms and validate the rice maps. The field data corresponded relatively well with the remote sensing metrics and validation found that the derived rice paddy maps possessed a high overall accuracy of 89%. The rice maps indicate that the watershed has 25 % rice agriculture with 85 % of all paddies undergoing a double-crop management pattern. Remotely sensed metrics showed that inundation periods for early rice were typically twice as long as inundation periods between crops, which corresponded to site level measurements at double crop locations. Using hydroperiod and crop intensity information a crop calendar indicated that the day of year (DOY) planting and harvesting activities was typically around DOY 77 and 329, respectively. The automated approach combining SAR and optical platforms with continental-scale acquisition strategies might allow for large-area, operational agricultural rice mapping. This can contribute to a GEOSS framework for improved agricultural monitoring.
Automatic detection of rivers in high-resolution SAR data
- IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens
"... Abstract—Remote sensing plays a major role in supporting decision-making and surveying compliance of several multilateral environmental treaties. In this paper, we present an approach for supporting monitoring compliance of river networks in con-text of the European Water Framework Directive. Only a ..."
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Abstract—Remote sensing plays a major role in supporting decision-making and surveying compliance of several multilateral environmental treaties. In this paper, we present an approach for supporting monitoring compliance of river networks in con-text of the European Water Framework Directive. Only a few approaches have been developed for extracting river networks from satellite data and usually they require manual input, which seems not feasible for automatic and operational application. We propose a method for the automatic extraction of river structures in TerraSAR-X data. The method is based on mathematical mor-phology and supervised image classification, using automatically selected training samples. The method is applied on TerraSAR-X images from two different study sites. In addition, the results are compared to an alternative method, which requires manual user interaction. The detailed accuracy assessment shows that the proposed method achieves accurate results (Kappa 0.7) and performs almost similar in terms of accuracy, when compared to the alternative approach. Moreover, the proposed method can be applied on various datasets (e.g., multitemporal, multisensoral and multipolarized) and does not require any additional user input. Thus, the highly flexible approach is interesting in terms of operational monitoring systems and large scale applications. Index Terms—Linear features extraction, mathematical mor-phology, SVM, TerraSAR-X, water framework directive. I.
Article Meeting Earth Observation Requirements for Global Agricultural Monitoring: An Evaluation of the Revisit Capabilities of Current and Planned Moderate Resolution Optical Earth Observing Missions
"... remote sensing ..."
REGIONAL SCALE CROP MAPPING USING MULTI-TEMPORAL SATELLITE IMAGERY
"... One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this paper, a new approach to classification of multi-temporal optical satellite imagery with missing data du ..."
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One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this paper, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using non-missing values. Missing values are restored through a special procedure that substitutes input sample's missing components with neuron's weight coefficients. After missing data restoration, a supervised classification is performed for multi-temporal satellite images. An ensemble of neural networks, in particular multilayer perceptrons (MLPs), is proposed. Ensembling of neural networks is done by the technique of average committee, i.e. to calculate the average class probability over classifiers and select the class with the highest average posterior probability for the given input sample. The proposed approach is applied for regional scale crop classification using multi temporal Landsat-8 images for the JECAM test site in Ukraine in 2013. It is shown that ensemble of MLPs provides better performance than a single neural network in terms of overall classification accuracy, kappa coefficient, and producer's and user's accuracies for separate classes. The overall accuracy more than 85 % is achieved. The obtained classification map is also validated through estimated crop areas and comparison to official statistics.
Article OPEN ACCESS Remote Sensing
, 2013
"... Abstract: Because of all-weather working ability, sensitivity to biomass and moisture, and high spatial resolution, Synthetic aperture radar (SAR) satellite images can perfectly complement optical images for pasture monitoring. This paper aims to examine the potential of the integration of COnstella ..."
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Abstract: Because of all-weather working ability, sensitivity to biomass and moisture, and high spatial resolution, Synthetic aperture radar (SAR) satellite images can perfectly complement optical images for pasture monitoring. This paper aims to examine the potential of the integration of COnstellation of small Satellites for the Mediterranean basin Observasion (COSMO-SkyMed), Environmental Satellite Advanced Synthetic Aperture
Article A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)
"... www.mdpi.com/journal/remotesensing ..."
Article Mapping Priorities to Focus Cropland Mapping Activities: Fitness Assessment of Existing Global, Regional and National Cropland Maps
"... remote sensing ..."
Article Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa
, 2014
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Article Potential of X-Band Images from High-Resolution Satellite SAR Sensors to Assess Growth and Yield in Paddy Rice
, 2014
"... remote sensing ..."
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Formosat-2 multi-temporal and panchromatic images
, 2014
"... of the crop row orientations from ..."
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