Assessment of Agricultural Drought Using Vegetation Condition Index and Vegetation Health Index in Niger River Basin, Nigeria
Abstract
Soil moisture indicates the dryness of the ground surface. This phenomenon is directly tied to vegetation quality and Land Surface Temperature in a specific place. As a result, these characteristics indirectly describe the dryness of the ground surface. This study assessed agricultural drought in Niger River in Nigeria. Data used include MODIS driven MOD13Q1 (NDVI), and MOD11A2 (LST) datasets obtained from Land Processes Distributed Active Archive Center. These datasets were used to compute Vegetation Condition Index (VCI) and Vegetation Health Index (VHI) in Niger River Basin, Nigeria. Additionally, correlation and regression analyses were used to check the relationship between LST and NDVI. Results revealed that the mean NDVI for the year 2002 is 0.494, 0.477 in 2007, 0.468 in 2012, 0.458 in 2017 and 0.430 in 2022. The mean LST in Niger River Basin for year 2002 is 32.28 oC, 32.12 oC in 2007, 32.35 oC in 2012, 33.20 oC in 2017 and 33.41 oC in 2022. For the statistical relationship between NDVI and LST, results exhibited negative correlation, with -0.33 in 2002, -0.43 in 2007, -0.42 in 2012, -0.36 in 2017 and -0.27 in 2022. For the VCI results, findings revealed that the mean VCI in the basin was 83.73 in 2002, 64.26 in 2007, 56.76 in 2012, 45.32 in 2017, and 14.93 in 2023. Also, the VHI results revealed that the mean VHI in the basin was 75.44 in 2002, 69.54 in 2007, 61.02 in 2010, 37.22 in 2017 and 18.87 in 2022. The study therefore concluded that vegetation is decreasing in the basin, while land surface temperature is increasing.
References
Adenle, A. A., Boillat, S., & Speranza, C. I. (2022). Key dimensions of land users’ perceptions of land degradation and sustainable land management in Niger State, Nigeria. Environmental Challenges, 8, 100544. https://doi.org/10.1016/J.ENVC.2022.100544
Agbo, E. P., Nkajoe, U., Edet, C. O., & Ali, N. (2023). Identification of trends of key parameters affecting vegetation over Nigeria’s vegetation zones using innovative trend analysis. Environmental Earth Sciences, 82(20), 464. https://doi.org/10.1007/s12665-023-11141-5
Alademomi, A. S., Okolie, C. J., Daramola, O. E., Akinnusi, S. A., Adediran, E., Olanrewaju, H. O., Alabi, A. O., Salami, T. J., & Odumosu, J. (2022). The interrelationship between LST, NDVI, NDBI, and land cover change in a section of Lagos metropolis, Nigeria. Applied Geomatics, 14(2), 299–314. https://doi.org/10.1007/s12518-022-00434-2
Ali, S., Basit, A., Umair, M., Makanda, T. A., Khan, F. U., Shi, S., & Ni, J. (2023). Spatio-temporal variations in trends of vegetation and drought changes in relation to climate variability from 1982-2019 based on remote sensing data from East Asia. Journal of Integrative Agriculture, 22(10). https://doi.org/10.1016/J.JIA.2023.04.028
Amiri, M., Jafari, R., Tarkesh, M., & Modarres, R. (2020). Spatiotemporal variability of soil moisture in arid vegetation communities using MODIS vegetation and dryness indices. Arid Land Research and Management, 34(1), 1–25. https://doi.org/10.1080/15324982.2019.1573441
Babatolu, J. S., & Akinnubi, R. T. (2013). Surface Temperature Anomalies in the River Niger Basin Development Authority Areas, Nigeria. Atmospheric and Climate Sciences, 03(04), 532–537. https://doi.org/10.4236/acs.2013.34056
Babatolu, J. S., & Akinnubi, R. T. (2014). Influence of Climate Change in Niger River Basin Development Authority Area on Niger Runoff, Nigeria. Journal of Earth Science & Climatic Change, 05(09). https://doi.org/10.4172/2157-7617.1000230
Babatolu, J. S., Akinnubi, R. T., Folagimi, A. T., & Bukola, O. O. (2014). Variability and Trends of Daily Heavy Rainfall Events over Niger River Basin Development Authority Area in Nigeria. American Journal of Climate Change, 03(01), 1–7. https://doi.org/10.4236/ajcc.2014.31001
Badou, D. F., Diekkrüger, B., & Montzka, C. (2019). Validation of satellite soil moisture in the absence of in situ soil moisture: the case of the Tropical Yankin Basin. South African Journal of Geomatics, 7(3), 243. https://doi.org/10.4314/sajg.v7i3.3
Bello, N. J. (1987). An assessment of water supply for agriculture in the Niger River Basin Development Authority Area, Nigeria. Agricultural and Forest Meteorology, 40(2), 109–121. https://doi.org/10.1016/0168-1923(87)90001-3
Buzhani, F. I., Sadr, M. K., Sobhanardakani, S., Lorestani, B., & Cheraghi, M. (2023). Remote sensing assessment of multi-year drought vulnerability of agriculture in Kangavar, Kermanshah Province, Western Iran. Natural Hazards, 120(4). https://doi.org/10.1007/s11069-023-06354-7
Carrão, H., Russo, S., Sepulcre-Canto, G., & Barbosa, P. (2016). An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data. International Journal of Applied Earth Observation and Geoinformation, 48, 74–84. https://doi.org/10.1016/J.JAG.2015.06.011
Cetin, M., Sevik, H., Koc, I., & Zeren Cetin, I. (2023). The change in biocomfort zones in the area of Muğla province in near future due to the global climate change scenarios. Journal of Thermal Biology, 112. https://doi.org/10.1016/J.JTHERBIO.2022.103434
Chere, Z., & Debalke, D. B. (2023). Modeling agricultural drought based on the earth observation-derived standardized precipitation evapotranspiration index and vegetation health index in the northeastern highlands of Ethiopia. Natural Hazards, 120(3). https://doi.org/10.1007/s11069-023-06320-3
de Lima, S. C., Neto, J. M. d. M., Lima, J. P., de Lima, F. C., & Saboya, L. M. F. (2023). Response of semi-arid vegetation to agricultural drought determined by indices derived from MODIS satellite. Revista Brasileira de Engenharia Agrícola e Ambiental, 27(8), 632–642. https://doi.org/10.1590/1807-1929/AGRIAMBI.V27N8P632-642
Dutta, D., Kundu, A., Patel, N. R., Saha, S. K., & Siddiqui, A. R. (2015). Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI). The Egyptian Journal of Remote Sensing and Space Science, 18(1), 53–63. https://doi.org/10.1016/J.EJRS.2015.03.006
Fathi-Taperasht, A., Shafizadeh-Moghadam, H., & Kouchakzadeh, M. (2022). MODIS-based evaluation of agricultural drought, water use efficiency and post-drought in Iran; considering the influence of heterogeneous climatic regions. Journal of Cleaner Production, 374, 133836. https://doi.org/10.1016/J.JCLEPRO.2022.133836
Fathi-Taperasht, A., Shafizadeh-Moghadam, H., Minaei, M., & Xu, T. (2022). Influence of drought duration and severity on drought recovery period for different land cover types: evaluation using MODIS-based indices. Ecological Indicators, 141, 109146. https://doi.org/10.1016/J.ECOLIND.2022.109146
Furtak, K., & Wolińska, A. (2023). The impact of extreme weather events as a consequence of climate change on the soil moisture and on the quality of the soil environment and agriculture – A review. CATENA, 231, 107378. https://doi.org/10.1016/J.CATENA.2023.107378
Gao, Z., Gao, W., & Chang, N. Bin. (2011). Integrating temperature vegetation dryness index (TVDI) and regional water stress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+images. International Journal of Applied Earth Observation and Geoinformation, 13(3), 495–503. https://doi.org/10.1016/j.jag.2010.10.005
Ghajarnia, N., Kalantari, Z., Orth, R., & Destouni, G. (2020). Close co-variation between soil moisture and runoff emerging from multi-catchment data across Europe. Scientific Reports, 10(1), 4817. https://doi.org/10.1038/s41598-020-61621-y
Huang, J., Zhuo, W., Li, Y., Huang, R., Sedano, F., Su, W., Dong, J., Tian, L., Huang, Y., Zhu, D., & Zhang, X. (2020). Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield. International Journal of Digital Earth, 13(4), 504–526. https://doi.org/10.1080/17538947.2018.1542040
Ibanga, O. A., Idehen, O. F., & Omonigho, M. G. (2022). Spatiotemporal variability of soil moisture under different soil groups in Etsako West Local Government Area, Edo State, Nigeria. Journal of the Saudi Society of Agricultural Sciences, 21(2), 125–147. https://doi.org/10.1016/J.JSSAS.2021.07.006
Ismail, R. B. Y., Bozorg-Omid, F., Osei, J. H. N., Pi-Bansa, S., Frempong, K. K., Ofei, M. K., ... & Dadzie, S. K. (2024). Predicting the environmental suitability for Anopheles stephensi under the current conditions in Ghana. Scientific Reports, 14(1), 1116.. https://doi.org/10.1038/s41598-024-51780-7
Karnieli, A., Ohana-Levi, N., Silver, M., Paz-Kagan, T., Panov, N., Varghese, D., Chrysoulakis, N., & Provenzale, A. (2019). Spatial and seasonal patterns in vegetation growth-limiting factors over europe. Remote Sensing, 11(20), 2406. https://doi.org/10.3390/rs11202406
Kehinde, M. O., & Umar, A. T. (2021). Assessment of soil moisture storage in nigeria using climatic water budgeting approach. Ghana Journal of Geography, 13(1), 167–202. https://doi.org/10.4314/gjg.v13i1.9
Kloos, S., Yuan, Y., Castelli, M., & Menzel, A. (2021). Agricultural drought detection with modis based vegetation health indices in southeast germany. Remote Sensing, 13(19), 1–24. https://doi.org/10.3390/rs13193907
Luo, X., Li, S., Yang, W., Liu, L., Shi, Y., Lai, Y., Yu, P., Yang, Z., Luo, K., Zhou, T., Yang, X., Wang, X., Chen, S., & Tang, X. (2023). Spatio-temporal changes in global root zone soil moisture from 1981 to 2017. Journal of Hydrology, 626, 130297. https://doi.org/10.1016/J.JHYDROL.2023.130297
Maduako, I. N., Ndukwu, R. I., Ifeanyichukwu, C., & Igbokwe, O. (2017). Multi-Index Soil Moisture Estimation from Satellite Earth Observations: Comparative Evaluation of the Topographic Wetness Index (TWI), the Temperature Vegetation Dryness Index (TVDI) and the Improved TVDI (iTVDI). Journal of the Indian Society of Remote Sensing, 45(4), 631–642. https://doi.org/10.1007/s12524-016-0635-9
Masih, I., Maskey, S., Mussá, F. E. F., & Trambauer, P. (2014). A review of droughts on the African continent: A geospatial and long-term perspective. Hydrology and Earth System Sciences, 18(9), 3635–3649. https://doi.org/10.5194/HESS-18-3635-2014
Masitoh, F., & Rusydi, A. N. (2019). Vegetation Health Index (VHI) analysis during drought season in Brantas Watershed. IOP Conference Series: Earth and Environmental Science, 389(1). https://doi.org/10.1088/1755-1315/389/1/012033
Medugu, N. I., Majid, M. R., & Johar, F. (2011). Drought and desertification management in arid and semi-arid zones of Northern Nigeria. Management of Environmental Quality: An International Journal, 22(5), 595–611. https://doi.org/10.1108/14777831111159725
Meng, L., Li, J., Chen, Z., Xi, W., Chen, D., & Duan, H. (2008). The Calculation of TVDI Based on the Composite of Pixel and Drought Analysis. The International Archives of the Photogrammetry, Remote Sening and Spatial Information Sciences, 38, 519–524.
Monteleone, B., Bonaccorso, B., & Martina, M. (2020). A joint probabilistic index for objective drought identification: the case study of Haiti. Natural Hazards and Earth System Sciences, 20(2), 471-487. https://doi.org/10.5194/nhess-20-471-2020
Murtala, M., Iguisi, E. O., Ibrahim, A. A., Yusuf, Y. O., & Inobeme, J. (2018). Spatio - temporal analysis of drought occurrence and intensity in northwest zone of nigeria. Dutse Journal of Pure and Applied Sciences, 4(1), 111–129.
NASA. (2023). MOD13Q1 - MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid. Retrieved from https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD13Q1
Nguyen, H. H., Cho, S., & Choi, M. (2022). Spatial soil moisture estimation in agro-pastoral transitional zone based on synergistic use of SAR and optical-thermal satellite images. Agricultural and Forest Meteorology, 312, 108719. https://doi.org/10.1016/j.agrformet.2021.108719
Nigerian Meteorological Agency (NIMET). (2018). Daily Meteorological Variables. In Nigerian Meteorological Agency. NiMET.
Ogbue, C., Igboeli, E., Ajaero, C., Ochege, F. U., Yahaya, I. I., Yeneayehu, F., You, Y., & Wang, Y. (2024). Remote sensing analysis of desert sensitive areas using MEDALUS model and GIS in the Niger River Basin. Ecological Indicators, 158, 111404. https://doi.org/10.1016/J.ECOLIND.2023.111404
Ogunrinde, A. T., Oguntunde, P. G., Akinwumiju, A. S., & Fasinmirin, J. T. (2019). Analysis of recent changes in rainfall and drought indices in Nigeria, 1981–2015. Hydrological Sciences Journal, 64(14), 1755–1768. https://doi.org/10.1080/02626667.2019.1673396
Ogunrinde, A. T., Oguntunde, P. G., Akinwumiju, A. S., Fasinmirin, J. T., Adawa, I. S., & Ajayi, T. A. (2023). Effects of climate change and drought attributes in Nigeria based on RCP 8.5 climate scenario. Physics and Chemistry of the Earth, 129, 103339. https://doi.org/10.1016/j.pce.2022.103339
Okpara, J. N. (2022). Composite Index-Based Early Warning System for Drought Monitoring in The Niger River Basin of West Africa. FUT Akure.
Olatunji, W. B. (2023). Drought Management Initiatives (Issue May). Niger Basin Authority.
Oloruntade, A. J., Mohammad, T. A., Ghazali, A. H., & Wayayok, A. (2017). Analysis of meteorological and hydrological droughts in the Niger-South Basin, Nigeria. Global and Planetary Change, 155, 225–233. https://doi.org/10.1016/j.gloplacha.2017.05.002
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, 7153-7163. https://doi.org/10.1007/s10661-011-2487-7
Pradhan, N. R. (2019). Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region, USA. Hydrological Sciences Journal, 64(7), 771–788. https://doi.org/10.1080/02626667.2019.1593417
Przeździecki, K., Zawadzki, J. J., Urbaniak, M., Ziemblińska, K., & Miatkowski, Z. (2023). Using temporal variability of land surface temperature and normalized vegetation index to estimate soil moisture condition on forest areas by means of remote sensing. Ecological Indicators, 148, 110088. https://doi.org/10.1016/J.ECOLIND.2023.110088
Ryu, S., Kwon, Y. J., Kim, G., & Hong, S. (2021). Temperature vegetation dryness index-based soil moisture retrieval algorithm developed for geo-kompsat-2A. Remote Sensing, 13(15). https://doi.org/10.3390/rs13152990
Seun, A. I., Ayodele, A. P., Koji, D., & Akande, S. O. (2022). The potential impact of increased urbanization on land surface temperature over South-West Nigeria. Current Research in Environmental Sustainability, 4, 100142. https://doi.org/10.1016/J.CRSUST.2022.100142
Shamloo, N., Sattari, M. T., & Apaydin, H. (2022). Agricultural drought survey using MODIS-based image indices at the regional scale: case study of the Urmia Lake Basin, Iran. Theoretical and Applied Climatology, 149(1–2), 39–51. https://doi.org/10.1007/S00704-022-04023-6/METRICS
Srivastava, H. S., Sivasankar, T., Gavali, M. D., & Patel, P. (2024). Soil moisture estimation underneath crop cover using high incidence angle C-band Sentinel-1 SAR data. Kuwait Journal of Science, 51(1), 100101. https://doi.org/10.1016/J.KJS.2023.07.007
Taloor, A. K., Manhas, D. S., & Kothyari, G. C. (2021). Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data. Applied Computing and Geosciences, 9, 100051. https://doi.org/10.1016/j.acags.2020.100051
Thakur, P. K., Samant, S. S., Verma, R. K., Saini, A., & Chauhan, M. (2024). Monitoring forest cover changes and its impact on land surface temperature using geospatial technique in Talra Wildlife Sanctuary, Shimla, India. Environment, Development and Sustainability, 1-30. https://doi.org/10.1007/s10668-023-04347-x
Tran, T. V., Bruce, D., Huang, C. Y., Tran, D. X., Myint, S. W., & Nguyen, D. B. (2023). Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data. GIScience & Remote Sensing, 60(1), 1. https://doi.org/10.1080/15481603.2022.2163070
United Nations Commission on Sustainable Development (UNCSD). (2020). Comprehensive assessment of the freshwater resources of the world. Report of the Secretary-General. Retrieved from http://www.un.org/esa/ sustdev/sdissues/water/water documents
Wang, H., Magagi, R., Goïta, K., Colliander, A., Jackson, T., McNairn, H., & Powers, J. (2021). Soil moisture retrieval over a site of intensive agricultural production using airborne radiometer data. International Journal of Applied Earth Observation and Geoinformation, 97, 102287. https://doi.org/10.1016/J.JAG.2020.102287
Wang, T., Wedin, D. A., Franz, T. E., & Hiller, J. (2015). Effect of vegetation on the temporal stability of soil moisture in grass-stabilized semi-arid sand dunes. Journal of Hydrology, 521, 447–459. https://doi.org/10.1016/j.jhydrol.2014.12.037
Wassie, S. B., Mengistu, D. A., & Birlie, A. B. (2022). Agricultural drought assessment and monitoring using MODIS-based multiple indices: the case of North Wollo, Ethiopia. Environmental Monitoring and Assessment, 194(10), 1–25. https://doi.org/10.1007/S10661-022-10455-4/METRICS
Wei, W., Pang, S., Wang, X., Zhou, L., Xie, B., Zhou, J., & Li, C. (2020). Temperature Vegetation Precipitation Dryness Index (TVPDI)-based dryness-wetness monitoring in China. Remote Sensing of Environment, 248, 111957. https://doi.org/10.1016/j.rse.2020.111957
Xie, F., & Fan, H. (2021). Deriving drought indices from MODIS vegetation indices (NDVI/EVI) and Land Surface Temperature (LST): Is data reconstruction necessary? International Journal of Applied Earth Observation and Geoinformation, 101, 102352. https://doi.org/10.1016/j.jag.2021.102352
Yang, C., Liu, C., Gu, Y., Wang, Y., Xing, X., & Ma, X. (2023). A novel comprehensive agricultural drought index accounting for precipitation, evapotranspiration, and soil moisture. Ecological Indicators, 154, 110593. https://doi.org/10.1016/J.ECOLIND.2023.110593
Zeng, J., Zhang, R., Qu, Y., Bento, V. A., Zhou, T., Lin, Y., Wu, X., Qi, J., Shui, W., & Wang, Q. (2022). Improving the drought monitoring capability of VHI at the global scale via ensemble indices for various vegetation types from 2001 to 2018. Weather and Climate Extremes, 35, 100412. https://doi.org/10.1016/j.wace.2022.100412