EVALUATING REMOTE SENSING-BASED DROUGHT INDICES: STRENGTHS, LIMITATIONS, AND APPLICABILITY ACROSS SUB-SAHARAN AFRICA'S AGRO-ECOLOGICAL ZONES: A REVIEW
Abstract
This study reviews the application and effectiveness of various remote sensing (RS) indices for drought monitoring in Sub-Saharan Africa (SSA). Given the region’s diverse climatic zones and frequent drought occurrences, accurate and timely assessment tools are crucial. The study examines indices from different spectral regions, including optical, thermal infrared, and microwave bands, focusing on their spatial and temporal resolutions, data availability, strengths, and limitations. Optical indices such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) are effective in semi-arid and sub-humid zones where vegetation density varies. Thermal infrared indices, including the Temperature Condition Index (TCI), the Vegetation Health Index (VHI), and the Temperature Vegetation Dryness Index (TVDI), provide insights into thermal anomalies and vegetation health, with TCI particularly suited for semi-arid zones and TVDI useful in both semi-arid and sub-humid zones. Microwave indices, such as the Normalized Backscatter Moisture Index (NBMI), Vegetation Optical Depth (VOD), and the Microwave Polarization Difference Index (MPDI), excel in capturing soil moisture and vegetation water content, proving useful in humid forest and semi-arid zones. The integration of these indices with other meteorological and hydrological data enhances drought monitoring and management strategies. Recommendations are made for the optimal use of these indices across different SSA agroecological zones.
References
Alahacoon, N., & Edirisinghe, M. (2022). A comprehensive assessment of remote sensing and traditional based drought monitoring indices at global and regional scale. In Geomatics, Natural Hazards and Risk (Vol. 13, Issue 1, pp. 762–799). Taylor and Francis Ltd. https://doi.org/10.1080/19475705.2022.2044394 DOI: https://doi.org/10.1080/19475705.2022.2044394
Ali, S., Khorrami, B., Jehanzaib, M., Tariq, A., Ajmal, M., Arshad, A., Shafeeque, M., Dilawar, A., Basit, I., Zhang, L., Sadri, S., Niaz, M. A., Jamil, A., & Khan, S. N. (2023). Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS). Remote Sensing, 15(4). https://doi.org/10.3390/rs15040873 DOI: https://doi.org/10.3390/rs15040873
Anderson, M. C., Zolin, C. A., Sentelhas, P. C., Hain, C. R., Semmens, K., Tugrul Yilmaz, M., Gao, F., Otkin, J. A., & Tetrault, R. (2016). The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts. Remote Sensing of Environment, 174, 82–99. https://doi.org/10.1016/j.rse.2015.11.034 DOI: https://doi.org/10.1016/j.rse.2015.11.034
Becker, F., & Choudhury, B. J. (1988). Relative Sensitivity of Normalized Difference Vegetation Index (NDVI) and Microwave Polarization Difference Index (MPDI) for Vegetation and Desertification Monitoring. In REMOTE SENSING OF ENVIRONMENT (Vol. 24). DOI: https://doi.org/10.1016/0034-4257(88)90031-4
Bento, V. A., Gouveia, C. M., DaCamara, C. C., & Trigo, I. F. (2018). A climatological assessment of drought impact on vegetation health index. Agricultural and Forest Meteorology, 259, 286–295. https://doi.org/10.1016/j.agrformet.2018.05.014 DOI: https://doi.org/10.1016/j.agrformet.2018.05.014
Bhaga, T. D., Dube, T., Shekede, M. D., & Shoko, C. (2020). Impacts of climate variability and drought on surface water resources in sub-saharan africa using remote sensing: A review. In Remote Sensing (Vol. 12, Issue 24, pp. 1–34). MDPI AG. https://doi.org/10.3390/rs12244184 DOI: https://doi.org/10.3390/rs12244184
Bhaga, T. D., Dube, T., & Shoko, C. (2021). Satellite monitoring of surface water variability in the drought prone Western Cape, South Africa. Physics and Chemistry of the Earth, 124. https://doi.org/10.1016/j.pce.2020.102914 DOI: https://doi.org/10.1016/j.pce.2020.102914
Bhushan, B., Apurva, D., & Akanksha, S. (2024). Meteorological and Agricultural Drought Monitoring Using Geospatial Techniques. In S. Pravat Kumar, D. Dipanwita, D. Tapan Kumar, D. Sandipan, B. Gouri Sankar, D. Pulakesh, & S. Satiprasad (Eds.), Geospatial Practices in Natural Resources Management (pp. 273–304). Springer. https://doi.org/https://doi.org/10.1007/978-3-031-38004-4_13 DOI: https://doi.org/10.1007/978-3-031-38004-4_13
Bormudoi, A., Nagai, M., Katiyar, V., Ichikawa, D., & Eguchi, T. (2023). Soil Moisture Change Detection with Sentinel-1 SAR Image for Slow Onsetting Disasters: An Investigative Study Using Index Based Method. In Land (Vol. 12, Issue 2). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/land12020506 DOI: https://doi.org/10.3390/land12020506
Chen, A., Jiang, J., Luo, Y., Zhang, G., Hu, B., Wang, X., & Zhang, S. (2023). Temperature vegetation dryness index (TVDI) for drought monitoring in the Guangdong Province from 2000 to 2019. PeerJ, 11. https://doi.org/10.7717/peerj.16337 DOI: https://doi.org/10.7717/peerj.16337
Chen, J., Wang, C., Jiang, H., Mao, L., & Yu, Z. (2011). Estimating soil moisture using temperature-vegetation dryness index (TVDI) in the Huang-huai-hai (HHH) plain. International Journal of Remote Sensing, 32(4), 1165–1177. https://doi.org/10.1080/01431160903527421 DOI: https://doi.org/10.1080/01431160903527421
Cheng, T., Hong, S., Huang, B., Qiu, J., Zhao, B., & Tan, C. (2021). Passive microwave remote sensing soil moisture data in agricultural drought monitoring: Application in Northeastern China. Water (Switzerland), 13(19). https://doi.org/10.3390/w13192777 DOI: https://doi.org/10.3390/w13192777
DEAfrica. (2021). Normalised Difference Vegetation Index (NDVI) Climatology Service overview Description Specifications Media and example images License Data Access Amazon Web Services S3 Open Data Cube (ODC) OGC Web Services (OWS) Technical information Landsat Harmonization method NDVI Climatology Algorithm. https://docs.digitalearthafrica.org/en/latest/data_specs/NDVI_Climatology_specs.html
Drought Management Info. (2016). Temperature Condition Index (TCI). Integrated Drought Management Program. https://www.droughtmanagement.info/temperature-condition-index-tci/
Du, L., Song, N., Liu, K., Hou, J., Hu, Y., Zhu, Y., Wang, X., Wang, L., & Guo, Y. (2017). Comparison of two simulation methods of the Temperature Vegetation Dryness Index (TVDI) for drought monitoring in semi-arid regions of China. Remote Sensing, 9(2). https://doi.org/10.3390/rs9020177 DOI: https://doi.org/10.3390/rs9020177
Ejaz, N., Bahrawi, J., Alghamdi, K. M., Rahman, K. U., & Shang, S. (2023). Drought Monitoring Using Landsat Derived Indices and Google Earth Engine Platform: A Case Study from Al-Lith Watershed, Kingdom of Saudi Arabia. Remote Sensing, 15(4). https://doi.org/10.3390/rs15040984 DOI: https://doi.org/10.3390/rs15040984
Felde, G. W. (1998). The effect of soil moisture on the 37GHz microwave polarization difference index (MPDI). International Journal of Remote Sensing, 19(6), 1055–1078. https://doi.org/10.1080/014311698215603 DOI: https://doi.org/10.1080/014311698215603
Feng, H., Chen, C., Dong, H., Wang, J., & Meng, Q. (2013). Modified shortwave infrared perpendicular water stress index: A farmland water stress monitoring method. Journal of Applied Meteorology and Climatology, 52(9), 2024–2032. https://doi.org/10.1175/JAMC-D-12-0164.1 DOI: https://doi.org/10.1175/JAMC-D-12-0164.1
Fensholt, R., & Sandholt, I. (2003). Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment. Remote Sensing of Environment, 87(1), 111–121. https://doi.org/10.1016/j.rse.2003.07.002 DOI: https://doi.org/10.1016/j.rse.2003.07.002
Frantzova, A. (2023a). Drought Monitoring Using Remote Sensing Data. Aerospace Research in Bulgaria, 35, 52–62. https://doi.org/10.3897/arb.v35.e06
Frantzova, A. (2023b). Drought Monitoring Using Remote Sensing Data. Aerospace Research in Bulgaria, 35, 52–62. https://doi.org/10.3897/arb.v35.e06 DOI: https://doi.org/10.3897/arb.v35.e06
Gao, B.-C. (1996). NDWI - A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space. In REMOTE SENS. ENVIRON (Vol. 7212). ©Elsevier Science Inc. https://doi.org/https://doi.org/10.1016/S0034-4257(96)00067-3 DOI: https://doi.org/10.1016/S0034-4257(96)00067-3
Gao, Q., Zribi, M., Escorihuela, M. J., & Baghdadi, N. (2017). Synergetic use of sentinel-1 and sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors (Switzerland), 17(9). https://doi.org/10.3390/s17091966 DOI: https://doi.org/10.3390/s17091966
Gbaguidi, G. J., Idrissou, M., Topanou, N., Filho, W. L., & Ketoh, G. K. (2024). Application of advanced very high-resolution radiometer (AVHRR)-based vegetation health indices for modelling and predicting malaria in Northern Benin, West Africa. Malaria Journal, 23(1). https://doi.org/10.1186/s12936-024-04879-1 DOI: https://doi.org/10.1186/s12936-024-04879-1
Guo, Y., Han, L., Zhang, D., Sun, G., Fan, J., & Ren, X. (2023). The Factors Affecting the Quality of the Temperature Vegetation Dryness Index (TVDI) and the Spatial–Temporal Variations in Drought from 2011 to 2020 in Regions Affected by Climate Change. Sustainability (Switzerland), 15(14). https://doi.org/10.3390/su151411350 DOI: https://doi.org/10.3390/su151411350
Hazaymeh, K., & Hassan, K. Q. (2016). Remote sensing of agricultural drought monitoring: A state of art review. AIMS Environmental Science, 3(4), 604–630. https://doi.org/10.3934/environsci.2016.4.604 DOI: https://doi.org/10.3934/environsci.2016.4.604
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. https://doi.org/https://doi.org/10.1016/S0034-4257(02)00096-2 DOI: https://doi.org/10.1016/S0034-4257(02)00096-2
Iyinoluwa Ojumu. (2023). Using Normalized Difference Water Index (NDWI). https://storymaps.arcgis.com/stories/f94f50c05fa24667848b4b51af614935/print
Jiao, W., Wang, L., & McCabe, M. F. (2021). Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future. Remote Sensing of Environment, 256. https://doi.org/10.1016/j.rse.2021.112313 DOI: https://doi.org/10.1016/j.rse.2021.112313
Karnieli, A., Agam, N., Pinker, R. T., Anderson, M., Imhoff, M. L., Gutman, G. G., Panov, N., & Goldberg, A. (2010). Use of NDVI and land surface temperature for drought assessment: Merits and limitations. Journal of Climate, 23(3), 618–633. https://doi.org/10.1175/2009JCLI2900.1 DOI: https://doi.org/10.1175/2009JCLI2900.1
Kogan, F. N. (1995). Application of Vegetation Index and Brightness Temperature for Drought Detection. Advanced Space Research, 15(11), 273–1177. https://doi.org/https://doi.org/10.1016/0273-1177(95)00079-T DOI: https://doi.org/10.1016/0273-1177(95)00079-T
Komi, E., Shuanggen, J., Usman, M., Iñigo, M., Andres, C., & Irfan Ullah. (2024). Monitoring the drought in Southern Africa from space-borne GNSS-R and SMAP data. Natural Hazards , 120, 7947, – 7967. https://doi.org/https://doi.org/10.1007/s11069-024-06546-9 DOI: https://doi.org/10.1007/s11069-024-06546-9
Kotir, J. H. (2011). Climate change and variability in Sub-Saharan Africa: A review of current and future trends and impacts on agriculture and food security. Environment, Development and Sustainability, 13(3), 587–605. https://doi.org/10.1007/s10668-010-9278-0 DOI: https://doi.org/10.1007/s10668-010-9278-0
Li, Z. L., Tang, B. H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I. F., & Sobrino, J. A. (2013). Satellite-derived land surface temperature: Current status and perspectives. In Remote Sensing of Environment (Vol. 131, pp. 14–37). https://doi.org/10.1016/j.rse.2012.12.008 DOI: https://doi.org/10.1016/j.rse.2012.12.008
Lin, R., Wei, Z., Hu, R., Chen, H., Li, Y., Zhang, B., Wang, F., & Hu, D. (2024). Construction and Validation of Surface Soil Moisture Inversion Model Based on Remote Sensing and Neural Network. Atmosphere, 15(6), 647. https://doi.org/10.3390/atmos15060647 DOI: https://doi.org/10.3390/atmos15060647
Lottering, S., Mafongoya, P., & Lottering, R. (2021). Drought and its impacts on small-scale farmers in sub-Saharan Africa: a review. South African Geographical Journal, 103(3), 319–341. https://doi.org/10.1080/03736245.2020.1795914 DOI: https://doi.org/10.1080/03736245.2020.1795914
Meier, W. N. , Stewart, J. S. , Wilcox, H. , Scott, D. J., & & Hardman, M. A. (2021). DMSP SSM_I-SSMIS Daily Polar Gridded Brightness Temperatures, Version 6. In NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/https://doi.org/10.5067/MXJL42WSXTS1
Mishra, A. K., & Singh, V. P. (2011). Drought modeling - A review. In Journal of Hydrology (Vol. 403, Issues 1–2, pp. 157–175). https://doi.org/10.1016/j.jhydrol.2011.03.049 DOI: https://doi.org/10.1016/j.jhydrol.2011.03.049
Moesinger, L., Dorigo, W., De Jeu, R., Van Der Schalie, R., Scanlon, T., Teubner, I., & Forkel, M. (2020). The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA). Earth System Science Data, 12(1), 177–196. https://doi.org/10.5194/essd-12-177-2020 DOI: https://doi.org/10.5194/essd-12-177-2020
Moser, L., Voigt, S., Schoepfer, E., & Palmer, S. (2014). Multitemporal wetland monitoring in sub-Saharan West-Africa using medium resolution optical satellite data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8), 3402–3415. https://doi.org/10.1109/JSTARS.2014.2336875 DOI: https://doi.org/10.1109/JSTARS.2014.2336875
Mullapudi, A., Vibhute, A. D., Mali, S., & Patil, C. H. (2023). A review of agricultural drought assessment with remote sensing data: methods, issues, challenges and opportunities. In Applied Geomatics (Vol. 15, Issue 1, pp. 1–13). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/s12518-022-00484-6 DOI: https://doi.org/10.1007/s12518-022-00484-6
NCEI-NOAA. (2024). Precipitation - CMORPH CDR. National Centers for Environmental Information (NCEI) and National Oceanic and Atmospheric Administration (NOAA)). https://doi.org/10.25921/w9va-q159
Patel, N. R., Parida, B. R., Venus, V., Saha, S. K., & Dadhwal, V. K. (2012). Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data. Environmental Monitoring and Assessment, 184(12), 7153–7163. https://doi.org/10.1007/s10661-011-2487-7 DOI: https://doi.org/10.1007/s10661-011-2487-7
Payra, S., Sharma, A., & Verma, S. (2023). Chapter 14 - Application of remote sensing to study forest fires. In Atmospheric Remote Sensing Principles and Applications (pp. 239–260). https://doi.org/10.1016/B978-0-323-99262-6.00015-8 DOI: https://doi.org/10.1016/B978-0-323-99262-6.00015-8
Peng, J., Dadson, S., Hirpa, F., Dyer, E., Lees, T., Miralles, D. G., Vicente-Serrano, S. M., & Funk, C. (2020). A Pan-African High-Resolution Drought Index Dataset. Earth Syst. Sci. Data. https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb DOI: https://doi.org/10.5194/egusphere-egu2020-18591
Pettorelli, N. (2013). The Normalized Difference Vegetation Index. Oxford University Press. https://doi.org/https://doi.org/10.1093/acprof:osobl/9780199693160.001.0001 DOI: https://doi.org/10.1093/acprof:osobl/9780199693160.001.0001
Copyright (c) 2024 FUDMA JOURNAL OF SCIENCES
This work is licensed under a Creative Commons Attribution 4.0 International License.
FUDMA Journal of Sciences
