Precipitation

Kidder, Stanley Q.; Haar, Thomas H. Vonder

doi:10.1016/b978-0-08-057200-0.50013-4

Cited by 2 publications

(1 citation statement)

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“…Hence, new techniques are evolved in recent decades (Ba and Gruber, 2001;Haile et al 2010;Mishra et al 2009Mishra et al , 2010, which combines the advantages of both the methods i. For any SRE algorithm which involves VIS/IR data, the major limitation is proper rain area detection which is the initial step towards SRE (Kidder and Vonder Haar, 1995). If the rain area is not properly delineated then it may lead to over/under estimation of rainfall area or it may even oversight a complete rainfall event.…”

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Support Vector Machine (SVM) based Rain Area Detection from Kalpana-1 Satellite Data

Upadhyaya

Ramsankaran

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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confidence: 99%

Support Vector Machine (SVM) based Rain Area Detection from Kalpana-1 Satellite Data

Upadhyaya

Ramsankaran

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Remote Sensing of Applications in Hydrology

Black¹,

Endreny

2004

Water Encyclopedia

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Remote sensing is the science of observing and analyzing an area, object, or phenomenon based on data collected by sensors physically separated from the target. The sensors are mounted on platforms such as ground‐based, airplane, or satellite platforms. Physical radiation transfer is the foundation for remote sensing. Typically, the observation is of radiation reflected or emitted from the object of interest. There are two types of remote sensing: active and passive. In active remote sensing, the object is illuminated by radiation of a known wavelength (or frequency); the backscatter from the object is recorded by the sensor. In passive remote sensing, the sensor records radiation generated from natural sources, such as the sun, or radiation emitted from the earth or atmosphere. Radiation that the sensor receives has a spectral response that reveals properties of the atmosphere and object of interest. By extending remote sensing applications to improve the spatial and temporal resolution of certain key water resource variables, scientists can better understand and quantify water resource processes.

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Precipitation

Cited by 2 publications

References 100 publications

Support Vector Machine (SVM) based Rain Area Detection from Kalpana-1 Satellite Data

Support Vector Machine (SVM) based Rain Area Detection from Kalpana-1 Satellite Data

Remote Sensing of Applications in Hydrology

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