Abstract
Abstract. Rain is one of the major components of water cycle; extreme rain events can cause destruction and misery due to flash flood and droughts. Therefore, assessing rainfall at high temporal and spatial resolution is of fundamental importance which can be achieved only by satellite remote sensing. Though there are many algorithms developed for estimation of rainfall using satellite data, they suffer from various drawbacks. One such challenge in satellite rainfall estimation is to detect rain and no-rain areas properly. To address this problem, in the present study we have used the Support Vector Machines (SVM). It is significant to note that this is the first study to report the utility of SVM in detecting rain and no-rain areas. The developed SVM based index performance has been evaluated by comparing with two most popular rain detection methods used for Indian regions i.e. Simple TIR threshold used in Global Precipitation Index (GPI) technique and Roca method used in Insat Multi Spectral Rainfall Algorithm (IMSRA). Performance of the above considered indices has been analyzed by considering various categorical statistics like Probabil ity of Detection (POD), Probability of no-rain detection (POND), Accuracy, Bias, False Alarm Ratio (FAR) and Heidke Skill Score (HSS). The obtained results clearly show that the new SVM based index performs much better than the earlier indices.
Highlights
Satellite rainfall estimation (SRE) techniques can be broadly categorized into three categories: First are the techniques that use Visible (VIS) and Infrared (IR) Sensor data
Once the Support Vector Machines (SVM) model is developed the validation dataset has been prepared by considering equal proportion of rain and no-rain pixels so that the statistical results would not be biased towards the proportion in the dataset
The article mainly deals with the utility of Support Vector Machines (SVM) for detecting rain and no-rain areas accurately, which is the major initial step towards satellite based rainfall estimation
Summary
Satellite rainfall estimation (SRE) techniques can be broadly categorized into three categories: First are the techniques that use Visible (VIS) and Infrared (IR) Sensor data. These sensors are mounted over geostationary satellites and provide data at very high temporal resolution e.g. Kalpana-1 satellite provides images at every 30min. In the present study Support Vector Machine (SVM) based index has been developed for Indian region and validation has been carried out using various categorical statistics by considering the rain area detected by TRMM 2A25 (microwave based rainfall estimates) as reference data. The double-blind peer-review was conducted on the basis of the full paper
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.