Remote sensing data for drought stress and croplands productivity assessment in Kherson region
Abstract
Formulation of the problem. Remote sensing data might be used for indirect assessment of croplands conditions and drought stress through the calculation of specific vegetation indices, such as vegetation health index (VHI), agriculture stress index (ASI), and drought intensity or weighted mean vegetation health index (WMVHI). However, the accuracy of these indices is not clear for some territories. For example, the South of Ukraine is a zone of risky agriculture, because of low natural moisture supply and high evapotranspiration. Moisture supply is the main limiting factor for sustainable crop production in this region.
The goals of this study were: 1) to assess the reliability of the mentioned vegetation indices in drought assessment through the direct comparison with the UNEP aridity index; 2) to find out whether remote sensing drought indicators could be used for the yield prediction of major crops on the regional scale.
Methods. The study was conducted for Kherson region of Ukraine, as it is one of the most arid regions of the country with very high drought risks. The data on average weighted annual VHI, ASI, and WMVHI for the period 1984-2022 (Season 1) were collected and generalized from the FAO Earth Observation services. UNEP aridity index was calculated using the data from Kherson regional hydrometeorological center. Correlation and linear regression analysis were performed using common statistical methodology.
Results. As a result, it was found that 1) all the studied remote sensing drought indicators demonstrate poor correlation with the aridity index, therefore, they should not be used to determine meteorological drought in the region; 2) all the studied remote sensing indices, especially VHI, demonstrate moderate-to-strong correlation with the yields of certain crops, cultivated in Kherson region (R=0.54-0.86), and could be used for the yield prediction; 3) the aridity index have poor relation to the yields of major crops, cultivated in the studied area; 4) VHI-based linear regression models for the crops’ yields prediction are reliable and reasonable for scientific and practical use just for cereal crops, and are much less accurate for grain corn and sunflower; 5) based on the study findings, it could be concluded that aridity index provides pure climatological characteristics of the region, while the studied vegetation indices are mainly focused on the level of drought stress that impacts crops during the growing season.
Scientific novelty and practical significance. The article provides novel insights on the implementation of remote sensing data in drought risks assessment in crop production, and their utilization for the purpose of croplands productivity prediction. The study has theoretical and practical importance for current agriculture, and the findings could be used both in scientific, educational, and practical purposes.
Downloads
References
Devendra, C. (2012). Climate change threats and effects: challenges for agriculture and food security. Kuala Lumpur: Academy of Sciences Malaysia.
Tkachuk, O.P., & Viter, N.G. (2023). Dynamika klimatychnykh pokaznykiv ta ikh vplyv na urozhainist osnovnykh silskohospodarskykh kultur u Vinnytskii oblasti [Dynamics of climatic indicators and their influence on the productivity of the main agricultural crops in the Vinnytsia region]. Agrarian Innovations, 17, 139–149. [In Ukrainian] https://doi.org/10.32848/agrar.innov.2023.17.19
El-Beltagy, A., & Madkour, M. (2012). Impact of climate change on arid lands agriculture. Agriculture & Food Security, 1, 1–12. https://doi.org/10.1186/2048-7010-1-3
Rosa, L., Chiarelli, D. D., Rulli, M. C., Dell’Angelo, J., & D’Odorico, P. (2020). Global agricultural economic water scarcity. Science Advances, 6(18), eaaz6031. https://doi.org/10.1126/sciadv.aaz6031
Colantoni, A., Delfanti, L., Cossio, F., Baciotti, B., Salvati, L., Perini, L., & Lord, R. (2015). Soil aridity under climate change and implications for agriculture in Italy. Applied Mathematical Sciences, 9(50), 2467–2475. https://doi.org/10.12988/ams.2015.52112
Arora, V. K. (2002). The use of the aridity index to assess climate change effect on annual runoff. Journal of hydrology, 265(1-4), 164–177. https://doi.org/10.1016/S0022-1694(02)00101-4
Bannayan, M., Sanjani, S., Alizadeh, A., Lotfabadi, S. S., & Mohamadian, A. (2010). Association between climate indices, aridity index, and rainfed crop yield in northeast of Iran. Field crops research, 118(2), 105–114. https://doi.org/10.1016/j.fcr.2010.04.011
Van Hoolst, R., Eerens, H., Haesen, D., Royer, A., Bydekerke, L., Rojas, O., ... & Racionzer, P. (2016). FAO’s AVHRR-based Agricultural Stress Index System (ASIS) for global drought monitoring. International Journal of Remote Sensing, 37(2), 418–439. https://doi.org/10.1080/01431161.2015.1126378
Rojas, O., Li, Y., & Cumani, R. (2014). Understanding the drought impact of El Niño on the global agricultural areas: an assessment using FAO's Agricultural Stress Index (ASI) (No. 23). Food and Agriculture Organization of the United Nations (FAO).
Gouveia, C., Trigo, R. M., & DaCamara, C. C. (2009). Drought and vegetation stress monitoring in Portugal using satellite data. Natural Hazards and Earth System Sciences, 9(1), 185–195. https://doi.org/10.5194/nhess-9-185-2009
Karnieli, A., Bayasgalan, M., Bayarjargal, Y., Agam, N., Khudulmur, S., & Tucker, C. J. (2006). Comments on the use of the vegetation health index over Mongolia. International Journal of Remote Sensing, 27(10), 2017–2024. https://doi.org/10.1080/01431160500121727
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
Rahman, A., Roytman, L., Krakauer, N. Y., Nizamuddin, M., & Goldberg, M. (2009). Use of vegetation health data for estimation of Aus rice yield in Bangladesh. Sensors, 9(4), 2968–2975. https://doi.org/10.3390/s90402968
Kogan, F., Guo, W., Yang, W., & Shannon, H. (2018). Space-based vegetation health for wheat yield modeling and prediction in Australia. Journal of Applied Remote Sensing, 12(2), 026002-026002. https://doi.org/10.1117/1.JRS.12.026002
Rojas, O. (2020). Agricultural extreme drought assessment at global level using the FAO-Agricultural Stress Index System (ASIS). Weather and Climate Extremes, 27, 100184. https://doi.org/10.1016/j.wace.2018.09.001
Vozhehova, R.A. (2021). Dynamika zminy temperaturnoho rezhymu ta kilkosti opadiv u Khersonskii oblasti v konteksti zmin klimatu [Dynamics of changes in air temperature and precipitation amounts in Kherson oblast in the context of climate change]. Agrarian Innovations, 5, 17–21. [In Ukrainian] https://doi.org/10.32848/agrar.innov.2021.5.3
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific data, 5(1), 1–12. https://doi.org/10.1038/sdata.2018.214
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2020). Publisher Correction: Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific data, 7, 274. https://doi.org/10.1038/s41597-020-00616-w
Vozhehova, R., Lykhovyd, P., & Biliaieva, I. (2020). Aridity assessment and forecast for Kherson oblast (Ukraine) at the climate change. EurAsian Journal of BioSciences, 14(1), 1455–1462.
Gogtay, N.J., & Thatte, U.M. (2017). Principles of correlation analysis. Journal of the Association of Physicians of India, 65(3), 78–81.
Evans, J.D. (1995). Straightforward Statistics for the Behavioral Sciences; Thomson Brooks/Cole Publishing Co: Portland, USA. 624 pp.
Pandey, S. (2020). Principles of correlation and regression analysis. Journal of the practice of cardiovascular sciences, 6(1), 7–11. https://doi.org/10.4103/jpcs.jpcs_2_20
Lykhovyd, P. (2021). Irrigation needs in Ukraine according to current aridity level. Journal of Ecological Engineering, 22(8), 11–18. https://doi.org/10.12911/22998993/140478
Rojas, O., Li, Y., & Cumani, R. (2014). Understanding the drought impact of El Niño on the global agricultural areas: an assessment using FAO's Agricultural Stress Index (ASI) (No. 23). Food and Agriculture Organization of the United Nations (FAO).
Rojas, O., & Ahmed, S. (2016). Feasibility of using the FAO-Agricultural Stress Index System (ASIS) as a remote sensing based index for crop insurance. Flood and Agriculture Organization of the United Nations.
Masitoh, F., & Rusydi, A. N. (2019, November). Vegetation Health Index (VHI) analysis during drought season in Brantas Watershed. In IOP Conference Series: Earth and Environmental Science (Vol. 389, No. 1, p. 012033). IOP Publishing. https://doi.org/10.1088/1755-1315/389/1/012033
Zeng, J., Zhou, T., Qu, Y., Bento, V. A., Qi, J., Xu, Y., ... & Wang, Q. (2023). An improved global vegetation health index dataset in detecting vegetation drought. Scientific Data, 10(1), 338. https://doi.org/10.1038/s41597-023-02255-3
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), 3907. https://doi.org/10.3390/rs13193907
Ma’Rufah, U., Hidayat, R., & Prasasti, I. (2017). Analysis of relationship between meteorological and agricultural drought using standardized precipitation index and vegetation health index. In IOP conference series: earth and environmental science (Vol. 54, No. 1, p. 012008). IOP Publishing. http://doi.org/10.1088/1755-1315/54/1/012008
Kussul, N., Shelestov, A., Kolotii, A., & Yashchuk, D. (2015). Wheat yield forecasting in Ukraine. In 17th International Conference “Astronomy School for Young Scientists. Problems of Astronomy and Space”, Zhytomyr, Ukraine, 2015, 96–97.
Rahman, A., Roytman, L., Krakauer, N. Y., Nizamuddin, M., & Goldberg, M. (2009). Use of vegetation health data for estimation of Aus rice yield in Bangladesh. Sensors, 9(4), 2968–2975. https://doi.org/10.3390/s90402968
Chakraborty, D., Sehgal, V. K., Ray, M., Dhakar, R., Sahoo, R. N., Das, D. K., ... & Kumar, P. (2018). District-wise statistical yield modelling of wheat using weather and remote sensing inputs. Journal of Agricultural Physics, 18(1), 48–57.
Kussul, N., Kolotii, A., & Shelestov, A. (2014). Estimating relative efficiency of use multisource satellite data for winter wheat yield forecasting in Ukraine. In Earth observation for sustainable development and security: Materials of reports of the Fourth International Conference “GEO-UA 2014“, Kyiv, Ukraine, 62–64.
Kogan, F., Popova, Z., Singh, R., & Alexandrova, P. (2018). Early forecasting corn yield using ground truth data and vegetation health indices in Bulgaria. Bulgarian Journal of Agricultural Science, 24 (Suppl. 2), 57–67.
This work is licensed under a Creative Commons Attribution 4.0 International License.
