Geospatial Techniques for Paddy Crop Acreage and Yield Estimation
Current Journal of Applied Science and Technology,
Paddy crop acreage and yield estimation using geospatial technology were carried out in North Eastern Dry Zone (Zone-2) covering Shorapur taluk, Yadgir district, Karnataka state, India, during rabi late sown or summer 2016-17 season. The study area is located between 16° 20ꞌ to 17° 45ꞌ north latitude and 76° 04ꞌ to 77° 42ꞌ east longitude, at an elevation of 428 meters above mean sea level. The RESOURCESAT-1 LISS III satellite image of 31st January 2017, 24th February 2017, 20th March 2017 and LANDSAT-8 of 15th April 2017 were used for paddy crop acreage estimation at taluk level. Paddy signatures were identified using ground truth GPS data and then, these temporal imageries were subjected to NDVI classification and estimated the paddy biomass and further validated with the ground-truthing in corresponding to Green Seeker NDVI value. The estimated paddy crop acreage through imagery NDVI were 2145.75 ha, 17602.21 ha, 19838 ha and 23004.01 ha area during Jan-2017, Feb-2017, March-2017 and April-2017 respectively. When these results were compared with acreage estimates as reported by the State Department of Agriculture, shown a relative deviation of 11.41, 35.78, 23.01& 3.89 per cent for Jan-2017, Feb-2017, March-2017 and April-2017 respectively. Therefore, LandSat-8 NDVI paddy acreage has showed significantly on par with the ground truth data at the crop harvest stage. Relative deviation of 10.75 for yield comparison among imagery NDVI biomass yield with the DOA yield estimation infer that NDVI biomass yield estimation would give better result at 90 days after sowing. Positive correlation of NDVI values with estimated acreage and yield, indicates that application of remote sensing techniques for forecasting paddy biomass yield is more accurate, economical and could be beneficial to the policy makers for quick decisions.
- Geo-spatial technology
- paddy crop acreage
- NDVI and yield estimation
How to Cite
Karnieli A. Natural vegetation phenology assessment by ground spectral measurements in two semi-arid environments. Int. J. Biometeoro. 2003;47:179-187.
Xin J, Zhenrong Y, Leeuwenb LV, Driessen PM. Mapping crop key phenological stages in the North China plain using NOAA time series images. Int. J. Appl. Earth Observ. Geoinform. 2002;4:109-117.
Doraiswamy PC, Moulin S, Cook PW, Stern A. Crop yield assessment from remote sensing. Photogrammetric Engg. Remote Sens. 2003;69(6):665-674.
Prasad AKL, Chai RP, Singh M, Kafatos. Crop yield estimation model for Iowa using remote sensing and surface parameters. Int. J. Appl. Earth Obs. Geoinfo. 2006;8:26-33.
Curnel Y, Oger R. Agrophenology indicators from remote sensing: State of the art. In: ISPRS Archives XXXVI-8/W48 Workshop Proceedings: Remote Sens., Support to Crop Yield Forecast and Area Estimates; 2007.
Tiwari SK, Ramana KV, Lakshmi Prasad M, Ramesh KVV. Identification of crops and cropping pattern using resources at-2 temporal AWIFS NDVI data in Andhra Pradesh State. Souvenir Cum Abstract Volume ISPRS WG III/10, GEOGLAM, ISRS International Workshop on Earth Observations for Agricultural Monitoring, IARI, New Delhi. 2019;54.
Liu KL, Li YZ, Hu HW. Predicting ratoon rice growth rhythm based on NDVI at key growth stages of main rice. Chilean Journal of Agricultural Research. 2015,75(4):410-417.
Wani SP, Anantha KH, Garg KK. Soil properties, crop yield and economics under integrated crop management practices in Karnataka, Southern India. World Development. 2017;93:43-61.
Cabrera-Bosquet L, Molero G, Stellacci A, Bort J, Nogues S, Araus J. NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions. Cereal Research Communications. 2011;39(1): 147-159.
Abstract View: 875 times
PDF Download: 391 times