IN-SEASON GROUNDNUT YIELD BEHAVIOUR ASSESSMENT USING TEMPORAL MODIS -NDVI (MOD13Q1-250MT) DATA FOR PRODUCTION FORECAST

S.K. TIWARI¹*, K.V. RAMANA², M.L. PRASAD³, K.V.V. RAMESH^4
1Andhra Pradesh Space Applications Centre (APSAC), Vijayawada, Andhra Pradesh, 520001, India
²National Remote Sensing Centre-ISRO, Hyderabad, 500625, India
³Andhra Pradesh Space Applications Centre (APSAC), Vijayawada, Andhra Pradesh, 520001, India
⁴Andhra Pradesh Space Applications Centre (APSAC), Vijayawada, Andhra Pradesh, 520001, India
* Corresponding Author : sudheeriirs@gmail.com

Received : 04-05-2019     Accepted : 26-05-2019     Published : 30-05-2019
Volume : 11     Issue : 10       Pages : 8430 - 8436
Int J Agr Sci 11.10 (2019):8430-8436

Keywords : Remote Sensing, Groundnut, MODIS, NDVI, Yield, Forecast
Academic Editor : Dr Arshad Bhat
Conflict of Interest : None declared
Acknowledgements/Funding : Authors are thankful to Department of Economics and Statistics (DES) and APSDPS, Planning Department, Govt. of Andhra Pradesh for providing the necessary data for the study.
Author Contribution : All authors equally contributed

Changing extreme weather and climate events directly affect the crop condition and ultimately on crop yield/production. In the recent decades, in-season fluctuation of extreme weather has been increased which affect the crop at their crop growth stages. It is important to develop a fast and inexpensive method to assess crop condition and yield behaviour in a frequent interval at peak crop growth stages to help the policy makers and manage the buffer stock of food grains. This study explored the suitability of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectrometer (MODIS) obtained over ten cropping year (Kharif) from 2008–2017 to understand the behaviour of Groundnut (Arachis hypogaea) yield in mid-season (at peak crop growth stages) and total production. Pre-harvest crop yield forecasting plays a vital role in coping with the challenges of the impacts of climate change on agriculture. NDVI product of MODIS-MOD13Q1-250m [1] of 16 days composite (fortnightly) is a freely available remote sensing product. The product was analysed in predominant groundnut growing mandals of Anantapur district of Andhra Pradesh, India. Since Anantapur is a perennially drought prone district and the major kharif crop is Groundnut, contributing around 6000 km2 area in kharif season per year. It is essential to frequently monitor the Groundnut crop condition within crop growing season and assess the yield prospect. The peak vegetative growth of Groundnut crop is mainly occurs in August and September month in Anantapur district. The average yield of nine years (2008 to 2016) of selected mandals is varying from 136 kg/ha. to 496 kg/ha. The regression models were developed between NDVI and yield for each mandal fortnightly from June to September and the significant relationships were observed between NDVI and yield in selected mandals only in 1st fortnight of August, 2nd fortnight of August and 1st fortnight of September varying from R2 = 0.08 to 0.44, R2 = 0.09 to 0.52 and R2 = 0.05 to 0.52 respectively. The NDVI values of 2017 were used to predict the yield in mid-season and a significant correlation were observed between predicted yield and observed average yield (2008 to 2016) for 1st fortnight of August, 2nd fortnight of August and 1st fortnight of September with R2= 0.74, R2 = 0.82 and R2 = 0.48 respectively. The total production was estimated in mid-season using groundnut actual crop sown year up to 12th September, 2017 at mandal level.

1. Kamel Didan-University of Arizona, Alfredo Huete-University of Technology Sydney and MODAPS SIPS - NASA. (2015) MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid. NASA LP DAAC.
2. Hammer G.L., et al., (2001) Agric.Syst., 70, 515-553.
3. Rao M.V.K., Ayyangar R.S., Rao P.P.N. (1982) In: Machine Processing of Remote Sensed Data Symposium, 1103-1112.
4. Chen Shupeng, Zhao Yingshi. (1990) Ceo-science Analysis of Remote Sensing. The Publishing House of Surveying and Mapping, 1990.
5. Rassmussen M.S. (1997) International Journal of Remote Sensing, 18(5), 1059-1077.
6. Balaghi R., Tychon B., Eerens H., Jlibene M. (2008) Int. J. Appl. Earth Obs. Geoinf., 10(4), 438-52.
7. Chuvieco Salinero E. (2002) Ariel La observacion de la tierra desde el espacio., 315-471.
8. Tucker C.J., Holben BN, Elgin J.H, McMurtrey J.E. (1980) Photogramm Eng Remote Sens., 46(5), 657-66.
9. Becker-Reshef I., Vermote E., Lindeman M., Justice C. (2010) Remote Sens Environ., 114(6), 1312-23.
10. Kouadio L., Duveiller G., Djaby B., El Jarroudi M., Defourny P., Tychon B. (2012) Int J Appl Earth Obs Geoinf., 18, 111-8.
11. Bolton D.K., Friedl M.A. (2013) Agric For Meteorol., 173,74-84.
12. Becker-Reshef I., Vermote E., Lindeman M., Justice C. (2010) Remote Sens Environ., 114(6), 1312-23.
13. Mariano F. Lopresti, Carlos M. Di Bella, Ame´rico J. Degioanni (2015) J Information Processing in Agriculture, 2, 73-84.
14. Bolton D.K., Friedl M.A. (2013) Agric For Meteorol., 173, 74-84.