drought-monitoring-using-modis-derived-indices-and-google-earth-engine-platform-for-vadodara-district,-gujarat-…-–-springer

Drought Monitoring Using MODIS Derived Indices and Google Earth Engine Platform for Vadodara District, Gujarat … – Springer

References

  • Bala, R., Prasad, R., Yadav, V. P., Sharma, J., & INTENSITY in URBAN CITIES USING MODIS SATELLITE DATA. (2019). SPATIAL VARIATION of URBAN HEAT ISLAND. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences – ISPRS Archives, 42(4/W16). https://doi.org/10.5194/isprs-archives-XLII-4-W16-147-2019.

  • Balti, H., Ben Abbes, A., Mellouli, N., Farah, I. R., Sang, Y., & Lamolle, M. (2020). A review of drought monitoring with big data: Issues, methods, challenges and research directions. Ecological Informatics, 60. https://doi.org/10.1016/j.ecoinf.2020.101136.

  • Barker, A., & Timco, G. W. (2017). Maximum pile-up heights for grounded ice rubble. Cold Regions Science and Technology, 135. https://doi.org/10.1016/j.coldregions.2016.12.001.

  • Bento, V. A., Gouveia, C. M., DaCamara, C. C., Libonati, R., & Trigo, I. F. (2020). The roles of NDVI and land surface temperature when using the Vegetation Health Index over dry regions. Global and Planetary Change, 190. https://doi.org/10.1016/j.gloplacha.2020.103198.

  • Bhatt, B., Sharma, S. A., Joshi, J. P., & Patel, S. (2023). Quantifying spatio-temporal land surface temperature and Biophysical Indices for Sustainable Management of Watershed: A study of Vishwamitri Watershed of Gujarat. Journal of Geomatics, 17(1), 109–120. https://doi.org/10.58825/jog.2023.17.1.82.

    Article  Google Scholar 

  • Bhowmik, S., & Bhatt, B. (2023). Spatiotemporal analysis of land surface temperature owing to NDVI: A case study of Vadodara District, Gujarat. Journal of Geomatics, 17(1). https://doi.org/10.58825/jog.2023.17.1.83.

  • Bhuiyan, C., Saha, A. K., Bandyopadhyay, N., & Kogan, F. N. (2017). Analyzing the impact of thermal stress on vegetation health and agricultural drought–a case study from Gujarat, India. GIScience and Remote Sensing, 54(5). https://doi.org/10.1080/15481603.2017.1309737.

  • Bhukya, S., Tiwari, M. K., & Patel, G. R. (2023). Assessment of Spatiotemporal Variation of Agricultural and Meteorological Drought in Gujarat (India) using remote sensing and GIS. Journal of the Indian Society of Remote Sensing, 51(7). https://doi.org/10.1007/s12524-023-01715-y.

  • Central Ground Water Board (CGWB) (2001). DISTRICT GROUND WATER BROCHURE: VADODARA. http://cgwb.gov.in/District_Profile/Gujarat/Vadodara.pdf.

  • Das, N., Sutradhar, S., Ghosh, R., & Mondal, P. (2021). Asymmetric nexus between air quality index and nationwide lockdown for COVID-19 pandemic in a part of Kolkata metropolitan, India. Urban Climate, 36. https://doi.org/10.1016/j.uclim.2021.100789.

  • Daylam, F., Kazemi, H., & Kamkar, B. (2023). Modelling organic farming suitability by spatial indicators of GIS integrated MCDA in Golestan Province, Iran. NJAS: Impact in Agricultural and Life Sciences, 95(1). https://doi.org/10.1080/27685241.2023.2191796.

  • Debarati Guha-Sapir, P. H., & Below, P. W. (2016). and R. Annual Disaster Statistical Review 2016: The numbers and trends. Review Literature And Arts Of The Americas.

  • Didan, K. (2015). MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 global 250m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD13Q1.006.

  • 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). Egyptian Journal of Remote Sensing and Space Science, 18(1). https://doi.org/10.1016/j.ejrs.2015.03.006.

  • 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.

  • Ermida, S. L., Soares, P., Mantas, V., Göttsche, F. M., & Trigo, I. F. (2020). Google earth engine open-source code for land surface temperature estimation from the landsat series. Remote Sensing, 12(9). https://doi.org/10.3390/RS12091471.

  • Fentaw, A. E., Yimer, A. A., & Zeleke, G. A. (2023). Monitoring spatio-temporal drought dynamics using multiple indices in the dry land of the upper Tekeze Basin, Ethiopia. Environmental Challenges, 13. https://doi.org/10.1016/j.envc.2023.100781.

  • Gadgil, S., & Gadgil, S. (2006). The Indian monsoon, GDP and agriculture. Economic & Political Weekly, November 25.

  • Ghorbanian, A., Mohammadzadeh, A., & Jamali, S. (2022). Linear and Non-linear Vegetation Trend Analysis throughout Iran using two decades of MODIS NDVI Imagery. Remote Sensing, 14(15). https://doi.org/10.3390/rs14153683.

  • GIS Geography (2018). What is NDVI (Normalized Difference Vegetation Index)? Web Page GIS Geography.

  • Jabal, Z. K., Khayyun, T. S., & Alwan, I. A. (2022). Impact of climate change on crops Productivity using MODIS-NDVI Time Series. Civil Engineering Journal (Iran), 8(6). https://doi.org/10.28991/CEJ-2022-08-06-04.

  • Jackson, R. D., & Huete, A. R. (1991). Interpreting vegetation indices. Preventive Veterinary Medicine, 11(3–4). https://doi.org/10.1016/S0167-5877(05)80004-2.

  • Jain, L., & Bhatt, B. (2022). A spatio-temporal analysis of changing trends in rainfall patter: A case study of Kutch District. Journal of Geomatics, 16(2). https://doi.org/10.58825/jog.2022.16.2.52.

  • Jalayer, S., Sharifi, A., Abbasi-Moghadam, D., Tariq, A., & Qin, S. (2023). Assessment of Spatiotemporal characteristic of droughts using in situ and remote sensing-based Drought Indices. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16. https://doi.org/10.1109/JSTARS.2023.3237380.

  • 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). https://doi.org/10.1175/2009JCLI2900.1.

  • Khan, R., & Gilani, H. (2021). Global drought monitoring with big geospatial datasets using Google Earth Engine. Environmental Science and Pollution Research, 28(14). https://doi.org/10.1007/s11356-020-12023-0.

  • Kibret, K. S., Marohn, C., & Cadisch, G. (2020). Use of MODIS EVI to map crop phenology, identify cropping systems, detect land use change and drought risk in Ethiopia–an application of Google Earth Engine. European Journal of Remote Sensing, 53(1). https://doi.org/10.1080/22797254.2020.1786466.

  • Kirana, A. P., Ariyanto, R., Ririd, A. R. T. H., & Amalia, E. L. (2020). Agricultural drought monitoring based on vegetation health index in East Java Indonesia using MODIS Satellite Data. IOP Conference Series: Materials Science and Engineering, 732(1). https://doi.org/10.1088/1757-899X/732/1/012063.

  • Kirono, D. G. C., Round, V., Heady, C., Chiew, F. H. S., & Osbrough, S. (2020). Drought projections for Australia: Updated results and analysis of model simulations. Weather and Climate Extremes, 30. https://doi.org/10.1016/j.wace.2020.100280.

  • Kogan, F. N. (1990). Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing, 11(8). https://doi.org/10.1080/01431169008955102.

  • Kogan, F. N. (1995a). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11). https://doi.org/10.1016/0273-1177(95)00079-T.

  • Kogan, F. N. (1995b). Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bulletin – American Meteorological Society, 76(5). Kogan, F. (2002). World droughts in the new millennium from avhrr-based vegetation health indices. Eos, 83(48). https://doi.org/10.1029/2002EO000382.

  • Kogan, F., Gitelson, A., Zakarin, E., Spivak, L., & Lebed, L. (2003). AVHRR-based spectral vegetation index for quantitative assessment of vegetation state and productivity: Calibration and validation. Photogrammetric Engineering and Remote Sensing, 69(8). https://doi.org/10.14358/PERS.69.8.899.

  • Kriegler, F. J., Malila, W. A., Nalepka, R. F., & Richardson, W. (1969). Preprocessing transformations and their effects on multispectral recognition. Proceedings of the 6th International Symposium on Remote Sensing of Environment.

  • Kukunuri, A. N. J., Murugan, D., & Singh, D. (2022). Variance based fusion of VCI and TCI for efficient classification of agriculture drought using MODIS data. Geocarto International, 37(10). https://doi.org/10.1080/10106049.2020.1837256.

  • Kumar, L., & Mutanga, O. (2018). Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10). https://doi.org/10.3390/rs10101509.

  • Kumar, M., Desai, M., V. R., & Manekar, V. (2009). An examination of relationships between vegetation and rainfall using maximum value composite AVHRR-NDVI data. Journal of Atmosphere, 93–101.

  • Kumari, M., Kumar, D., & Vaishnavi (2023). Dynamic drought risk assessment and analysis with multi-source drought indices and analytical hierarchy process. International Journal of Environmental Science and Technology, 20(3). https://doi.org/10.1007/s13762-022-04041-x.

  • Maciel, A. M., Picoli, M. C. A., Vinhas, L., & Camara, G. (2020). Identifying land use change trajectories in Brazil’s agricultural frontier. Land, 9(12). https://doi.org/10.3390/land9120506.

  • Masroor, M., Rehman, S., Avtar, R., Sahana, M., Ahmed, R., & Sajjad, H. (2020). Exploring climate variability and its impact on drought occurrence: Evidence from Godavari Middle sub-basin, India. Weather and Climate Extremes, 30. https://doi.org/10.1016/j.wace.2020.100277.

  • Mishra, V., Aadhar, S., & Mahto, S. S. (2021). Anthropogenic warming and intraseasonal summer monsoon variability amplify the risk of future flash droughts in India. Npj Climate and Atmospheric Science, 4(1). https://doi.org/10.1038/s41612-020-00158-3.

  • Mysterud, A., Tryjanowski, P., Panek, M., Pettorelli, N., & Stenseth, N. C. (2007). Inter-specific synchrony of two contrasting ungulates: Wild boar (Sus scrofa) and roe deer (Capreolus capreolus). Oecologia, 151(2). https://doi.org/10.1007/s00442-006-0584-z.

  • Narayanan, P., Basistha, A., Sarkar, S., & Kamna, S. (2013). Trend analysis and ARIMA modelling of pre-monsoon rainfall data for western India. Comptes Rendus – Geoscience, 345(1). https://doi.org/10.1016/j.crte.2012.12.001.

  • Principal Chief Conservator of Forest & Head of the Forest Force (HoFF), G. of G (2020). Schemes. https://forests.gujarat.gov.in/schemes-details.htm.

  • Qian, X., Liang, L., Shen, Q., Sun, Q., Zhang, L., Liu, Z., Zhao, S., & Qin, Z. (2016). Drought trends based on the VCI and its correlation with climate factors in the agricultural areas of China from 1982 to 2010. Environmental Monitoring and Assessment, 188(11). https://doi.org/10.1007/s10661-016-5657-9.

  • Rimkus, E., Stonevicius, E., Kilpys, J., MacIulyte, V., & Valiukas, D. (2017). Drought identification in the eastern baltic region using NDVI. Earth System Dynamics, 8(3). https://doi.org/10.5194/esd-8-627-2017.

  • Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63(324). https://doi.org/10.1080/01621459.1968.10480934.

  • Senamaw, A., Addisu, S., & Suryabhagavan, K. V. (2021). Mapping the spatial and temporal variation of agricultural and meteorological drought using geospatial techniques, Ethiopia. Environmental Systems Research, 10(1). https://doi.org/10.1186/s40068-020-00204-2.

  • Shafizadeh-Moghadam, A. F. T. H., Sadian, A., Xu, T., & Mohammad Reza Nikoo. (2023). &. Drought-induced vulnerability and resilience of different land use types using time series of MODIS-based indices. International Journal of Disaster Risk Reduction, 91. https://doi.org/10.1016/j.ijdrr.2023.103703.

  • Shah, D., & Mishra, V. (2020). Integrated Drought Index (IDI) for Drought Monitoring and Assessment in India. Water Resources Research, 56(2). https://doi.org/10.1029/2019WR026284.

  • Shapiro, D., Alanna, & Liu Weibo. (2023). Evaluating Land Surface temperature trends and explanatory variables in the Miami Metropolitan Area from 2002–2021. Geomatics, 4(1). https://doi.org/10.3390/geomatics4010001.

  • Sun, D., & Kafatos, M. (2007). Note on the NDVI-LST relationship and the use of temperature-related drought indices over North America. Geophysical Research Letters, 34(24). https://doi.org/10.1029/2007GL031485.

  • Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164. https://doi.org/10.1016/j.isprsjprs.2020.04.001.

  • Testbook Edu Solutions Pvt. Ltd. (2023). Irrigation in Gujarat. https://testbook.com/gujarat-gk/irrigation-in-gujarat.

  • Theil, H. (1950). A rank-invariant method of linear and polynomial regression analysis, 3; confidence regions for the parameters of polynomial regression equations. Indagationes Mathematicae, 1(2).

  • Valdés-Pineda, R., Pizarro, R., Valdés, J. B., Carrasco, J. F., García-Chevesich, P., & Olivares, C. (2016). Spatio-temporal trends of precipitation, its aggressiveness and concentration, along the Pacific coast of South America (36–49°S). Hydrological Sciences Journal, 61(11). https://doi.org/10.1080/02626667.2015.1085989.

  • Wan, Z., & Hook, S. (2015). Hulley, & G. MOD11A2 MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V006 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD11A2.006.

  • Wang, H. S., & Lin, H. (2014). Liu, & D.S. Remotely sensed drought index and its responses to meteorological drought in Southwest China. Remote Sens, 413–422. https://www.tandfonline.com/doi/full/10.1080/2150704X.2014.912768?scroll=top&needAccess=true

  • Wang, F., Lai, H., Men, R., Sun, K., Li, Y., Feng, K., Tian, Q., Guo, W., Du, X., & Qu, Y. (2024). Spatial and temporal evolutions of terrestrial vegetation drought and the influence of atmospheric circulation factors across the Mainland China. Ecological Indicators, 158. https://doi.org/10.1016/j.ecolind.2023.111455.

  • 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). https://doi.org/10.1007/s10661-022-10455-4.

  • worldclim.org (2022). Historical monthly weather data. https://www.worldclim.org/data/index.html.

  • Wu, M., Li, H., Huang, W., Niu, Z., & Wang, C. (2015). Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring. Environmental Sciences: Processes and Impacts, 17(8). https://doi.org/10.1039/c5em00254k.

  • Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J., Congalton, R. G., Yadav, K., & Thau, D. (2017). Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 126. https://doi.org/10.1016/j.isprsjprs.2017.01.019.

  • Xu, Z. X., Gong, T. L., & Li, J. Y. (2008). Decadal trend of climate in the tibetan plateau – Regional temperature and precipitation. Hydrological Processes, 22(16). https://doi.org/10.1002/hyp.6892.

  • Yang, W., Kogan, F., & Guo, W. (2020). An ongoing blended long-term vegetation health product for monitoring global food security. In Agronomy (Vol. 10, Issue 12). https://doi.org/10.3390/agronomy10121936.

  • Yasuda, T., & Furuya, M. (2015). Dynamics of surge-type glaciers in West Kunlun Shan, Northwestern Tibet. Journal of Geophysical Research: Earth Surface, 120(11). https://doi.org/10.1002/2015JF003511.

  • Zambrano, F., Lillo-Saavedra, M., Verbist, K., & Lagos, O. (2016). Sixteen years of agricultural drought assessment of the biobío region in Chile using a 250 m resolution vegetation condition index (VCI). Remote Sensing, 8(6). https://doi.org/10.3390/rs8060530.

  • Zhao, Q., Yang, F., Meng, L., Chen, D., Wang, M., Lu, X., Chen, D., Jiang, Y., & Xing, N. (2020). Lycopene attenuates chronic prostatitis/chronic pelvic pain syndrome by inhibiting oxidative stress and inflammation via the interaction of NF-κB, MAPKs, and Nrf2 signaling pathways in rats. Andrology, 8(3). https://doi.org/10.1111/andr.12747.

  • Zhao, Q., Yu, L., Li, X., Peng, D., Zhang, Y., & Gong, P. (2021). Progress and trends in the application of google earth and google earth engine. In Remote Sensing (Vol. 13, Issue 18). https://doi.org/10.3390/rs13183778.

  • Zou, L., Cao, S., & Sanchez-Azofeifa, A. (2020). Evaluating the utility of various drought indices to monitor meteorological drought in Tropical Dry Forests. In International Journal of Biometeorology (Vol. 64, Issue 4). https://doi.org/10.1007/s00484-019-01858-z.

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