Classification of convective and stratiform rain based on the spectral and textural features of Meteosat Second Generation infrared data

Giannakos, Apostolos; Feidas, H.

doi:10.1007/s00704-012-0802-z

Cited by 33 publications

(21 citation statements)

References 52 publications

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“…The texture feature involves the extraction and analysis of spatial distribution patterns of gray grade in an image, which is widely used in image classification and target recognition [53][54][55]. Texture feature parameters are extracted to obtain qualitative or quantitative descriptions of texture by certain image processing technology.…”

Section: Principal Components and Texturementioning

confidence: 99%

Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image

Sun

Shu

et al. 2019

Plant Phenomics

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Lodging is one of the main factors affecting the quality and yield of crops. Timely and accurate determination of crop lodging grade is of great significance for the quantitative and objective evaluation of yield losses. The purpose of this study was to analyze the monitoring ability of a multispectral image obtained by an unmanned aerial vehicle (UAV) for determination of the maize lodging grade. A multispectral Parrot Sequoia camera is specially designed for agricultural applications and provides new information that is useful in agricultural decision-making. Indeed, a near-infrared image which cannot be seen with the naked eye can be used to make a highly precise diagnosis of the vegetation condition. The images obtained constitute a highly effective tool for analyzing plant health. Maize samples with different lodging grades were obtained by visual interpretation, and the spectral reflectance, texture feature parameters, and vegetation indices of the training samples were extracted. Different feature transformations were performed, texture features and vegetation indices were combined, and various feature images were classified by maximum likelihood classification (MLC) to extract four lodging grades. Classification accuracy was evaluated using a confusion matrix based on the verification samples, and the features suitable for monitoring the maize lodging grade were screened. The results showed that compared with a multispectral image, the principal components, texture features, and combination of texture features and vegetation indices were improved by varying degrees. The overall accuracy of the combination of texture features and vegetation indices is 86.61%, and the Kappa coefficient is 0.8327, which is higher than that of other features. Therefore, the classification result based on the feature combinations of the UAV multispectral image is useful for monitoring of maize lodging grades.

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Section: Principal Components and Texturementioning

confidence: 99%

Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image

Sun

Shu

et al. 2019

Plant Phenomics

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“…This higher spectral information can improve the IR-only part of the IMERG product, relying solely on the 10.8 µm channel. Several studies have documented improved satellite-based rainfall estimation by integrating full spectral information compared to a single IR channel (Behrangi et al, 2010;Giannakos & Feidas, 2013;Kolbe et al, 2020;Kühnlein et al, 2014aKühnlein et al, , 2014bMeyer et al, 2017;Turini et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Random forest-based rainfall retrieval for Ecuador using GOES-16 and IMERG-V06 data

Turini

Thies

Horna³

et al. 2021

European Journal of Remote Sensing

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A new satellite-based algorithm for rainfall retrieval in high spatio-temporal resolution for Ecuador is presented. The algorithm relies on the precipitation information from the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) and infrared (IR) data from the Geostationary Operational Environmental Satellite-16 (GOES-16). It was developed to (i) classify the rainfall area (ii) assign the rainfall rate. In each step, we selected the most important predictors and hyperparameter tuning parameters monthly. Between 19 April 2017 and 30 November 2017, brightness temperature derived from the GOES-16 IR channels and ancillary geo-information were trained with microwave-only IMERG-V06 using random forest (RF). Validation was done against independent microwave-only IMERG-V06 information not used for training. The validation results showed the new rainfall retrieval technique (multispectral) outperforms the IR-only IMERG rainfall product. This offers using the multispectral IR data can improve the retrieval performance compared to single-spectrum IR approaches. The standard verification scored a median Heidke skill score of ~0.6 for the rain area delineation and R between ~0.5 and ~0.62 for the rainfall rate assignment, indicating uncertainties for Andes's high elevation. Comparison of RF rainfall rates in 2 km 2 resolution with daily rain gauge measurements reveals the correlation of R = ~0.33.

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“…In addition to global rainfall retrievals, a number of regionally adapted retrievals were developed in the last decades (Kühnlein et al, 2014b, a;Meyer et al, 2016;Feidas and Giannakos, 2012;Giannakos and Feidas, 2013). Kühnlein et al (2014a, b) and Meyer et al (2016) presented a methodology to estimate rainfall from optical Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data for Germany.…”

Section: Introductionmentioning

confidence: 99%

Satellite-based high-resolution mapping of rainfall over southern Africa

2017

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Abstract. A spatially explicit mapping of rainfall is necessary for southern Africa for eco-climatological studies or nowcasting but accurate estimates are still a challenging task. This study presents a method to estimate hourly rainfall based on data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SE-VIRI). Rainfall measurements from about 350 weather stations from 2010-2014 served as ground truth for calibration and validation. SEVIRI and weather station data were used to train neural networks that allowed the estimation of rainfall area and rainfall quantities over all times of the day. The results revealed that 60 % of recorded rainfall events were correctly classified by the model (probability of detection, POD). However, the false alarm ratio (FAR) was high (0.80), leading to a Heidke skill score (HSS) of 0.18. Estimated hourly rainfall quantities were estimated with an average hourly correlation of ρ = 0.33 and a root mean square error (RMSE) of 0.72. The correlation increased with temporal aggregation to 0.52 (daily), 0.67 (weekly) and 0.71 (monthly). The main weakness was the overestimation of rainfall events. The model results were compared to the Integrated Multi-satellitE Retrievals for GPM (IMERG) of the Global Precipitation Measurement (GPM) mission. Despite being a comparably simple approach, the presented MSGbased rainfall retrieval outperformed GPM IMERG in terms of rainfall area detection: GPM IMERG had a considerably lower POD. The HSS was not significantly different compared to the MSG-based retrieval due to a lower FAR of GPM IMERG. There were no further significant differences between the MSG-based retrieval and GPM IMERG in terms of correlation with the observed rainfall quantities. The MSG-based retrieval, however, provides rainfall in a higher spatial resolution. Though estimating rainfall from satellite data remains challenging, especially at high temporal resolutions, this study showed promising results towards improved spatio-temporal estimates of rainfall over southern Africa.

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Classification of convective and stratiform rain based on the spectral and textural features of Meteosat Second Generation infrared data

Cited by 33 publications

References 52 publications

Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image

Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image

Random forest-based rainfall retrieval for Ecuador using GOES-16 and IMERG-V06 data

Satellite-based high-resolution mapping of rainfall over southern Africa

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