Meta-Analysis in Using Satellite Precipitation Products for Drought Monitoring: Lessons Learnt and Way Forward
Abstract
:1. Introduction
2. Drought Monitoring: The State of the Art
3. Review Methodology
3.1. Methodology for Selection and Screening of Articles
3.2. Performance Analysis of SPPs in Drought Monitoring
3.2.1. Effect of Climatic Zones on the Performance of SPPs in Drought Monitoring
3.2.2. Effect of Data Record Length on the Performance of SPPs in Drought Monitoring
3.2.3. Effect of Time Scales on the Performance of SPPs in Drought Monitoring
4. Results
4.1. Overall Performance of SPPs in Drought Monitoring
4.2. Effect of Climatic Zones on the Performance of SPPs in Drought Monitoring
4.3. Effect of Data Record Length on the Performance of SPPs in Drought Monitoring
4.4. Effect of Time Scales on the Performance of SPPs in Drought Monitoring
5. Discussion
6. Conclusions and Way Forward
- Identify the best climatological condition under which SPPs can be successfully used for drought monitoring.
- Focus on merging SPPs with other satellite data of high spatial and temporal scale (e.g., soil moisture and vegetation water content) to enhance the precipitation estimation and drought monitoring process.
- Application of various data processing methods such as wavelet packet transform (WPT), discrete wavelet transform (DWT), etc., should be attempted in SPPs to enhance the machine learning performance in blending SPPs with in situ data.
- Bias correction such as quantile mapping etc. should be applied to SPPs before SPI estimation to improve their performance.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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SPI Value | Classification |
---|---|
>2 | Extremely wet |
1.50 to 1.99 | Very Wet |
1.00 to 1.49 | Moderately wet |
−0.99 to 0.99 | Near Normal |
−1.0 to −1.49 | Moderately dry |
−1.5 to −1.99 | Severely dry |
<−2.0 | Extremely dry |
Reference | Country | Analyzed Period | SPP Type | Time Scale | SPI In Situ vs. SPI with SPP | |
---|---|---|---|---|---|---|
PCC | RMSE | |||||
[23] | India | 1998–2016 | CHIRPS | SPI 3 | 0.87 | NA |
[47] | China | 1981–2016 | CHIRPS | SPI 1,3,6,12 | 0.85–0.89 | 0.34–0.39 |
[48] | China | 1981–2015 | CHIRPS | SPI 1,3,6,12 | 0.92–0.94 | NA |
[4] | Nepal | 1981–2010 | CHIRPS | SPI 1 | 0.57 | NA |
[3] | China | 1981–2015 | CHIRPS | SPI 3,6 | 0.84–0.89 | NA |
[49] | Tunisia | 1981–2019 | CHIRPS | SPI 12 | 0.85 | 0.443 |
[50] | Brazil | 1994–2017 | CHIRPS | SPI 6,12 | 0.85–0.94 | 0.33–0.54 |
[51] | China | 1983–2015 | CHIRPS | SPI 3 | 0.89–0.92 | 0.23–0.33 |
[52] | Chile | 1981–2015 | CHIRPS | SPI 3,6 | 0.63–1.13 | |
[53] | China | 1983–2016 | CHIRPS | SPI 1,3,12 | 0.84–0.89 | 0.25–0.28 |
[54] | Bangladesh | 2001–2016 | CHIRPS | SPI 3,6 | 0.90 | 0.43–0.44 |
[46] | China | 2003–2017 | CHIRPS | SPI 1,3,6,12 | 0.85–0.94 | NA |
[23] | India | 1998–2016 | PERSIANN CDR | SPI 3 | 0.88 | NA |
[27] | China | 1983–2014 | PERSIANN CDR | SPI 6 | 0.4–0.9 | NA |
[51] | China | 1983–2015 | PERSIANN CDR | SPI 3 | 0.94–0.97 | 0.18–2.8 |
[50] | Brazil | 1994–2017 | PERSIANN CDR | SPI 6,12 | 0.94–0.96 | 0.29–0.35 |
[25] | Iran | 1983–2012 | PERSIANN CDR | SPI 3,6,12 | 0.27–0.80 | 0.62–1.05 |
[53] | China | 1983–2016 | PERSIANN CDR | SPI 1,3,12 | 0.95–0.96 | 0.15–0.18 |
[46] | China | 2003–2017 | PERSIANN CDR | SPI 1,3,6,12 | 0.85–0.95 | NA |
[55] | Pakistan | 2000–2015 | PERSIANN CDR | SPI 1,3,12 | NA | 1.29–1.73 |
[54] | Bangladesh | 2001–2016 | PERSIANN CDR | SPI 3,6 | 0.90–0.91 | 0.43 |
[52] | Chile | 1983–2015 | PERSIANN CDR | SPI 3,6 | NA | 0.63–1.06 |
[56] | Iraq | 1983–2016 | PERSIANN CDR | SPI 3,6,12 | 0.28–0.70 | −0.49–0.46 |
References | Country | Analyzed Period | SPP Type | Time Scale | SPI In Situ vs. SPI with SPP | |
---|---|---|---|---|---|---|
PCC | RMSE | |||||
[57] | Mexico | 1998–2014 | TRMM-3B42 V7 | SPI 1,3,6,12 | 0.06–0.79 | NA |
[58] | China | 1998–2013 | TRMM-3B42 V7 | SPI 3,6,12 | 0.92–0.97 | NA |
[59] | China | 2015–2017 | TRMM-3B42 V7 | SPI 1,3,6,12 | 0.23–0.84 | 0.39–0.56 |
[46] | China | 2003–2017 | TRMM-3B42 V7 | SPI 1,3,6,12 | 0.64–0.84 | NA |
[55] | Pakistan | 2000–2015 | TRMM-3B42 V7 | SPI 1,3,12 | NA | 1.10–1.76 |
[23] | India | 1998–2016 | TRMM-3B43 V7 | SPI 3 | 0.88 | NA |
[56] | Iraq | 1998–2017 | TRMM-3B43 V7 | SPI 3,6,12 | 0.32–0.90 | 0.21–0.60 |
[60] | China | 1998–2014 | TRMM-3B43 V7 | SPI 3,6,12 | 0.92–0.98 | NA |
[61] | Malaysia | 1998–2014 | TRMM-3B43 V7 | SPI 3,6,12 | 0.42–0.49 | NA |
[62] | China | 1998–2014 | TRMM-3B43 V7 | SPI 3,12 | 0.89–0.91 | NA |
[63] | China | 1998–2013 | TRMM-3B43 V7 | SPI 1,3,6,12 | 0.98–0.99 | NA |
[64] | Singapore | 1998–2014 | TRMM-3B43 V7 | SPI 1,3,6,12 | 0.76–0.80 | 0.63–0.69 |
[65] | Africa | 1998–2010 | TRMM-3B43 V7 | SPI 3 | 0.51–0.82 | NA |
[66] | China | 1998–2016 | TRMM-3B43 V7 | SPI 1,3,6,12 | 0.96–0.98 | NA |
[54] | Bangladesh | 2001–2016 | TRMM-3B43 V7 | SPI 3,6 | 0.90 | 0.41–0.42 |
[67] | China | 1998–2010 | TRMM-3B43 V7 | SPI 3 | 0.93 | NA |
References | Country | Analyzed Period | SPP Type | Time Scale | SPI In Situ vs. SPI with SPP | |
---|---|---|---|---|---|---|
PCC | RMSE | |||||
[46] | China | 2003–2017 | IMERG | SPI 1,3,6,12 | 0.96–0.99 | NA |
[45] | China | 2000–2017 | IMERG | SPI 6 | 0.96–0.99 | 0.05–0.07 |
[55] | Pakistan | 2000–2015 | IMERG | SPI 1,2 12 | NA | 1.12–1.63 |
[46] | China | 2003–2017 | CMORPH | SPI 1,3,6,12 | 0.92–0.98 | NA |
[59] | China | 2015–2017 | CMORPH | SPI 1,3,6,12 | 0.84–0.93 | 0.15–0.56 |
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Hinge, G.; Mohamed, M.M.; Long, D.; Hamouda, M.A. Meta-Analysis in Using Satellite Precipitation Products for Drought Monitoring: Lessons Learnt and Way Forward. Remote Sens. 2021, 13, 4353. https://doi.org/10.3390/rs13214353
Hinge G, Mohamed MM, Long D, Hamouda MA. Meta-Analysis in Using Satellite Precipitation Products for Drought Monitoring: Lessons Learnt and Way Forward. Remote Sensing. 2021; 13(21):4353. https://doi.org/10.3390/rs13214353
Chicago/Turabian StyleHinge, Gilbert, Mohamed M. Mohamed, Di Long, and Mohamed A. Hamouda. 2021. "Meta-Analysis in Using Satellite Precipitation Products for Drought Monitoring: Lessons Learnt and Way Forward" Remote Sensing 13, no. 21: 4353. https://doi.org/10.3390/rs13214353
APA StyleHinge, G., Mohamed, M. M., Long, D., & Hamouda, M. A. (2021). Meta-Analysis in Using Satellite Precipitation Products for Drought Monitoring: Lessons Learnt and Way Forward. Remote Sensing, 13(21), 4353. https://doi.org/10.3390/rs13214353