Analysis of Optimum Growth Stages for Winter Crop Separability using Multi-Temporal NDVI Profiles in Vijapur Taluka, Gujarat State-India

Analysis of Optimum Growth Stages for Winter Crop Separability using Multi-Temporal NDVI Profiles in Vijapur Taluka, Gujarat State-India

In the present study analysis of growth stages of different winter crops grown in Vijapur Taluka was carried for determining optimumgrowth period for crop separability using multi-temporal NDVI profiles. The multi-temporal Sentinel-2 multi-spectral data coveringstudy area for the winter crop period from November-2018 to March-2019 was analysed. The spectral behavior of wheat, cotton,potato, fennel and castor crops during active growth stages was studied and it was observed that the spectral response of these cropsis quite distinct. However, wheat and potato have almost matching spectral response during the month of January when wheat was atflag leaf to flowering stage and potato was at maximum vegetative growth stage. This suggests that, single-date Sentinel-2multispectral digital data of active vegetative growth stages of these two crops during the month of January is not adequate anduseful for unique discrimination of wheat and potato. The Normalized Difference Vegetation Index (NDVI) profiles of these majorwinter crops were generated to monitor and identify the optimum growth stages of the winter crops for their unique separability. Theresults indicated that, during second fortnight of February, wheat was at grain filling and milk stage and potato was at maximumvegetative stage with tuber maturity and at the same time other crops namely cotton, castor and fennel were at the maturity stages,have quite distinct NDVI values. During the period of second fortnight of February major winter crops with different growth stageshad quite distinct spectral behaviour. This indicates that the period of second fortnight of February is quite good and unique fordiscriminating these winter crops using single date satellite data.

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  • Boken, V., Shaykewich, C.F. (2002). Improving an Operational Wheat Yield Model using Phenological Phase-based Normalized Difference Vegetation Index, International Journal of Remote Sensing, 23(20):4155-4168
  • Cai, Y.P.; Guan, K.Y.; Peng, J.; Wang, S.W.; Seifert, C., Wardlow, B.; Li, Z. (2018). A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach. Remote Sens. Environ. 210, 35– 47.
  • Chauhan, K., Patel, J., Shukla, S., Kalubarme, M. (2021). Monitoring Water Spread and Aquatic Vegetation using Spectral Indices in Nalsarovar, Gujarat State-India. International Journal of Environment and Geoinformatics, 8(1), 49-56. doi. 10.30897/ijegeo.790839.
  • Das, S., Choudhury, M., Gandhi̇ , S., Joshi̇ , V. (2016). Application of Earth Observation Data and Standardized Precipitation Index Based Approach for Meteorological Drought Monitoring, Assessment and Prediction Over Kutch, Gujarat, India. International Journal of Environment and Geoinformatics, 3(2), 24-37. doi. 10.30897/ijegeo.306468.
  • Esetli̇ li̇ , M , Bektas Balci̇ k, F, Bali̇ k Sanli̇ , F, Kalkan, K, Ustuner, M., Goksel, C., Gazi̇ oğlu, C., Kurucu, Y. (2018). Comparison of Object and Pixel-Based Classifications For Mapping Crops Using Rapideye Imagery: A Case Study Of Menemen Plain, Turkey. International Journal of Environment and Geoinformatics, 5(2), 231-243. doi. 10.30897/ ijegeo.442002.
  • Foerster, S.; Kaden, K.; Foerster, M.; Itzerott, S. (2012). Crop type mapping using spectral–temporal profiles and phenological information. Comput. Electron. Agric. 89, 30–40.
  • Gao, F.; Jin, Y.; Schaaf, C.B.; Strahler, A.H. (2002). Bidirectional NDVI and atmospherically resistant BRDF inversion for vegetation canopy. IEEE Trans. Geosci. Remote Sens. 40, 1269–1278.
  • Groten S.M.E. (1993). NDVI- monitoring and early yield assessment of Burkina Faso, International Journal of Remote Sensing, 14(8), 1495-1515.
  • Jensen, J.R. (1996). Introductory Digital Image Processing: A Remote Sensing Perspective, 2nd ed.; Prentice Hall: Upper Saddle River, NJ, USA, 1996. Labus, M.P., Nielsen, G.A., Lawrence, R.L., Engel, R.,
  • Long, D.S. (2002). Wheat Yield Estimates using Multi-temporal NDVI Satellite Imagery, International Journal of Remote Sensing, 23(20): 4169-4180.
  • Maus, V.; Camara, G.; Cartaxo, R.; Sanchez, A.; Ramos,
  • F.M.; de Queiroz, G.R. (2016). A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 3729–3739.
  • Mehta, D, Shukla, S, Kalubarme, M. (2021). Winter Crop Growth Monitoring using Multi-Temporal NDVI Profiles in Kapadvanj Taluka, Gujarat State. International Journal of Environment and Geoinformatics, 8(1), 33-38. doi. 10.30897/ ijegeo.773860.
  • Meng, Shiyao, Zhong, Yanfei, Luo, Chang, Hu, Xin, Wang, Xinyu, and Huang, Shengxiang. (2020). Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China. Remote Sens. 12, 226; doi: 10.3390/rs12020226. Palchowdhuri, Y.; Valcarce-Diñeiro, R.; King, P.;
  • Sanabria-Soto, M. (2018). Classification of multitemporal spectral indices for crop type mapping: A case study in Coalville, UK. J. Agric. Sci. 156, 24– 36.
  • Pan, Y.Z.; Li, L.; Zhang, J.S.; Liang, S.L.; Zhu, X.F.;
  • Sulla-Menashe, D. (2012). Winter wheat area estimation from MODIS-EVI time-series data using the crop proportion phenology index. Remote Sens. Environ. 119, 232–242.
  • Patel, J. H., and Oza, M.P (2014). Deriving Crop Calendar using NDVI Time-Series. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-8, 2014 ISPRS Technical Commission VIII Symposium, 09 – 12 December 2014, Hyderabad, India.
  • Quarmby, N.A., Milnes, M., Hindle, T. L., Silleos, N. (1993). The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction, International Journal of Remote Sensing, 14(2), 199-210.
  • Rajak, D., Ram, J., Rajesh K., and Ray, S. S. (2016). Early estimation of crop sown area by integrating multi-source data. Journal of Geomatics, 10(1), 80 – 88.
  • Sakamoto, T.; Wardlow, B.D.; Gitelson, A.A.; Verma, S.B.; Suyker, A.E.; Arkebauer, T.J. (2010). A twostep filtering approach for detecting maize and soybean phenology with time-series MODIS data. Remote Sens. Environ 114, 2146–2159
  • Sutari̇ ya, S, Hi̇ rapara, A, Meherbanali̇ , M, Ti̇ wari̇, M, Singh, V, Kalubarme, M. (2021). Soil Moisture Estimation using Sentinel-1 SAR Data and Land Surface Temperature in Panchmahal District, Gujarat State. International Journal of Environment and Geoinformatics, 8(1), 65-77. doi. 10.30897/ ijegeo.777434.
  • Tian, H.; Huang, N.; Niu, Z.; Qin, Y.; Pei, J.; Wang, J. (2019). Mapping Winter Crops in China with MultiSource Satellite Imagery and Phenology-Based Algorithm. Remote Sens. 11, 820.
  • Tucker C.J., Elgin J.H., McMurtrey (1980). Relationship of red and photographic infrared spectral data to alfalfa biomass, canopy cover and drought stress, International Journal of Remote Sensing, 1(1), 69 – 79.
  • Ülker, D., Ergüven, O., Gazioğlu, C. (2018). Socioeconomic impacts in a Changing Climate: Case Study Syria. International Journal of Environment and Geoinformatics, 5(1), 84-93. doi.10.30897/ ijegeo.406273.
  • Vuolo, F.; Neuwirth, M.; Immitzer, M.; Atzberger, C.; Ng, W.T. (2018). How much does multi-temporal Sentinel-2 data improve crop type classification? Int. J. Appl. Earth Obs. Geoinf. 72, 122–130.