Abstract
In recent years, satellite precipitation products (SPPs) have emerged as an essential source of data and information. This work intends to summarize lessons learnt on using SPPs for drought monitoring and to propose ways forward in this field of research. A thorough literature review was conducted to review three aspects: effects of climate type, data record length, and time scale on SPPs performance. The conducted meta-analysis showed that the performance of SPPs for drought monitoring largely depends upon the climate type of the location and length of the data record. SPPs drought monitoring performance was shown to be higher in temperate and tropical climates than in dry and continental ones. SPPs were found to perform better with an increase in data record length. From a general standpoint, SPPs offer great potential for drought monitoring, but the performance of SPPs needs to be improved for operational purposes. The present study discusses blending SPPs with in situ data and other lessons learned, as well as future directions of using SPPs for drought applications.
Highlights
IntroductionThere are many satellite precipitation products (SPPs) available, such as Climate Prediction Center MORPHing method (CMORPH) [5], Climate Hazards Group infrared precipitation with station data (CHIRPS) [6], integrated multi-satellite retrievals for GPM (IMERG) [7], precipitation estimation from remotely sensed information using artificial neural networks—climate data record (PERSIANN-CDR) [8], tropical rainfall measuring mission (TRMM) [9] and many others
Precipitation is an important variable for drought monitoring and for the calculation of many drought indices [20]
This work reviewed the most prevalent satellite precipitation products (SPPs) used in drought monitoring and evaluated the factors that can potentially affect the performance of SPPs
Summary
There are many SPPs available, such as Climate Prediction Center MORPHing method (CMORPH) [5], Climate Hazards Group infrared precipitation with station data (CHIRPS) [6], integrated multi-satellite retrievals for GPM (IMERG) [7], precipitation estimation from remotely sensed information using artificial neural networks—climate data record (PERSIANN-CDR) [8], tropical rainfall measuring mission (TRMM) [9] and many others. SPPs have proven to provide data at a high spatial and temporal scale [10]. They are capable of producing precipitation data even in inaccessible areas [11,12,13]
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