The use of NDVI profiles for assesment quality of arable lands (exemplified by the Baksan Region in Kabardino-Balkaria)
I. Savin1, 2, E. Tanov2,
1V. V. Dokuchaev Soil Science Institute, 119017 Moscow, Pyzhevskii, 7, bld. 2
2Peoples’ Frendship University of Russia, 117198, Moscow, Miklukho-Maklaya, Str. 6
3Kabardino-Balkarsky NIISH, 360024, Russia
A new approach for assessing the quality of arable lands was developed as based upon MODIS-derived satellite data. The essence of the approach consists in an expert analysis of NDVI curves derived separately for different crop groups in the last 10–12 years as well as the inter-annual variability of the NDVI seasonal maximum, whose value was used as an indicator for the crop status and yield on different plots. The nature of NDVI curves allowed expertly classifying the groups of winter, early spring and late spring crops. The approach to estimating the quality of arable lands was approved on the example of the Baksan region in Kabardino-Balkaria. All the arable lands have been comprehensively analyzed in the region, the mask of which was created by visual interpretation of field boundaries using LANDSAT satellite imagery. The temporary NDVI profiles were obtained by the satellite service “VEGA”. Based upon the given method all the plots in the region were classified according to the quality of arable lands. The obtained data may be used in cadastre surveys for objective estimate of lands and optimal arrangement of the main agricultural crops in this Republic, being applicable in the other regions of the Russian Federation.
Keywords: land evaluation, NDVI, satellite service “VEGA”, arable lands, Kabardino-Balkaria.
REFERENCES
1. Lupyan E.A., Savin I.Yu., Bartalev S.A., Tolpin V.A., Balashov I.V., Plotnikov D.E. Satellite service of monitoring the state of vegetation (“Vega”), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No. 1, pp. 190–198.
2. Pochvennaya karta Kabardino-Balkarskoy SSR. Maschtab 1 : 200000 (Soil Map of Kabardino-Balkarskaya SSR, Scale 1 : 200000, Moscow, GUGK, 1985.
3. Savin I.Yu. Open soil surface brightness as a direct sign for soil recognition on LANDSAT images, Bulleten Pochvennogo instituta im. V.V. Dokuchaeva, 2013, No. 71, pp. 52–64.
4. Savin I.Yu., Simakova M.S. Satellite technologies for soil inventorying and monitoring in Russia, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 5, pp. 104–115.
5. Savin I.Yu., Bartalev S.A., Lupyan E.A., Tolpin V.A., Khvostikov S.A. Crop yield forecasting based on satellite data: possibilities and perspectives, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2010, Vol. 7, No. 3, pp. 275–285.
6. Tolpin V.A., Balashov I.V., Lupyan E.A., Savin I.Yu. Satellite service “Vega”, Zemlia iz Kosmosa, 2011, Release 9, spring, pp. 32–37.
7. Fomin N.P., Sapozhnikov P.M. Novye podhody k gosudarstvennoi kadastrovoi ozenke zemel selskohoziaystvennogo naznachenia New approaches to the state cadaster estimation of the earth of agricultural designation [the date of publication 20.10.2010]), http://www.valnet.ru/m7.phtml.
8. Bala S.K., Islam A.S. Correlation between potato yield and MODIS-derived vegetation indices, International J. of Remote Sensing, 2009, Vol. 30, Iss. 10, pp. 2491–2507.
9. Baret F., Guyot G. Potentials and limits of vegetation indices for LAI and APAR assessment, Remote Sensing of Environment, 1991, Vol. 35, pp. 161– 173.
10. Benedetti R., Rossinni P. On the use of NDVI profiles as a tool for agricultural statistics: the case study of wheat yield estimate and forecast in Emilia Romagna, Remote Sensing of Environment, 1993, Vol. 45. pp. 311–326.
11. Bouman B.A.M., Uenk D., and Haverkort A.J. Estimation of ground cover of potato by reflectance measurements, Potato Research, 1992, Vol. 35. pp. 111–125.
12. Elvidge C.D., and Lyon R.J.P. Influence of rock-soil spectral variation on assessment of green biomass, Remote Sensing of Environment, 1985, Vol. 17, pp. 265–279.
13. Huete A.R., Jackson R.D., and Post D.F. Spectral response of a plant canopy with different soil backgrounds, Remote Sensing of Environment, 1985, V. 17, pp. 37–53.
14. Groten S.M.E. NDVI crop monitoring and early yield assessment of Burkina Faso, International J. Remote Sensing, 1993, Vol. 14(8), pp. 1495– 1515.
15. Liu W.T., and Kogan F. Monitoring Brazilian soybean production using NOAA/AVHRR based vegetation condition indices, International J. of Remote Sensing, 2002, V. 23(6), pp. 1161–1179.
16. Medvedeva M.A., Savin I.Yu., and Isaev V.A. Determination of Area of Drought-Affected Crops Based on Satellite Data (Exemplified by Crops in Chuvashia in 2010), Russian Agricultural Sciences, Vol. 38, No 2, 2012, pp. 121–125.
17. Quarmby, N.A., Milnes M., Hindle T.L., and Silicos N. The use of multitemporal NDVI measurements from AVHRR data for crop yield estimation and prediction, International J. of Remote Sensing, 1993, Vol. 14, pp.199– 210.
18. Rasmussen, M.S. Operational Yield forecast using AVHRR NDVI data: reduction of environmental and inter-annual variability, International J. Remote Sensing, 1997, Vol. 18(5). pp. 1059–1077.
19. Rembold F., Atzberger C., Savin I., Rojas O. Using low resolution satellite imagery for yield prediction and yield anomaly detection, Remote Sensing, 2013, Vol. 5, No. 4, pp. 1704–1733.
20. Remote Sensing Support to Crop Yield Forecast and Area Estimates, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVI, No. 8/W48 ISPRS WG VIII/10 Workshop. Stresa, Italy 2006.
21. Saravanan S. Estimating Yield of Irrigated Potatoes Using Aerial and Satellite Remote Sensing, All Graduate Theses and Dissertations, 2011, Paper 1049.
22. Savin I.Yu., and Nègre Т. Agro-meteorological Monitoring in Russia and Central Asian Countries OPOCE EUR 22210EN, Ispra (Italy), 2006, 214 p.
23. Unganai L.S. and Kogan F.N. Drought monitoring and Corn yield estimation in Southern Africa from AVHRR data, Remote Sensing of Environment, 1998, V. 63, pp. 219–232.
24. Yang C., Everitt J.H., Bradford J.M., and Escobar D.E. Mapping grain sorghum growth and yield variations using airborne multispectral digital imagery, Transactions of ASAE, 2000, V. 43(6), pp. 1927–1938.
