APPLICATIONS OF DNDC MODEL WITH SATELLITE DATA TO SIMULATE RICE YIELD AT REGIONAL SCALE

N.S. SUDARMANIAN¹*, S. PAZHANIVELAN², R. KUMARAPERUMAL³, K. BOOMIRAJ^4
1Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore, 641003, Tamil Nadu, India
²Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore, 641003, Tamil Nadu, India
³Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore, 641003, Tamil Nadu, India
⁴Agricultural College and Research Institute, Kudumiyanmalai, Pudukottai, Tamil Nadu Agricultural University, Coimbatore, 641003, Tamil Nadu, India
* Corresponding Author : sudarnsagri@gmail.com

Received : 03-06-2019     Accepted : 13-06-2019     Published : 15-06-2019
Volume : 11     Issue : 11       Pages : 8618 - 8621
Int J Agr Sci 11.11 (2019):8618-8621

Keywords : Rice, SAR, DNDC, Regional Mode, Yield
Conflict of Interest : None declared
Acknowledgements/Funding : Authors are thankful to Tamil Nadu Agricultural University, Coimbatore, 641003, Tamil Nadu, India
Author Contribution : All authors equally contributed

Rice (Oryza sativa L.) is the primary staple food source for more than half of the world’s population and has a profound influence on the livelihood of farmers. Lowland rice in tropical and subtropical regions can be detected precisely and its crop growth can be tracked effectively through Synthetic Aperture Radar (SAR) imagery, especially where cloud cover restricts the use of optical imagery. The SAR time-series data has been processed by fully automated processing chain in MAPscape-Rice software was used to convert the multi-temporal SAR data into terrain-geocoded σ° values, underwent a series of basic processing steps to generate terrain-geocoded σ° values suitable for analysis. Then the multi-temporal stack was analyzed using a rule-based classifier to detect rice areas. The DNDC model is one of the few agro-ecosystem models which simulate CO2, CH4, N2O, yield etc. The spatial variability in model yields was well detected based on the detailed soil data and an accurate rice area map. Rice yield derived based on rice area using Sentinel-1A SAR data and DNDC model integrates environmental factors and predicts yield depending on all model input data, whereas the RS method mainly considers in-season crop information. Based on the Rice area, the RS-derived yield represents a response to the environmental factors and human activities which may exceed the DNDC capability. The simulated rice yield for the monitoring locations was found to be in the range of 3553 to 4311 kg ha-1 whereas the observed yields were at 3288 to 5472 kg ha-1. The agreement ranges between DNDC simulated and observed yields were 78.2 to 92.8 percent.

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