Crop Information System: A Case Study of Paddy Monitoring in Bharatpur 13, Nepal

Abstract

Rice as the main food for Nepal, has important role in food security. For planning purposes the information on rice is frequently required. The information on large area can be extracted from satellite images. Monitoring of crop growth and forecasting its yield well before harvest is very important for crop and food management. Remote sensing images are capable of identifying crop health as well as predicting its yield. Normalized Difference Vegetation Index (NDVI) calculated from remote sensing images has been widely used to monitor crop growth and relate to crop yield This paper demonstrates an example on paddy monitoring using Sentinel 2 image for extracting the Normalized Difference Vegetation Index (NDVI) at 5 days interval.A small area of rice cropland i.e. ward 13 of Bharatpur, Nepal has been selected as a case study for understanding NDVI during different phenological stages of rice crop with the land management factors. Google Earth Engine (GEE) cloud platform is used in this study to extract the NDVI of multiple sentinel images, as it reduces the space and time for data acquiring and processing. Using time series NDVI stacks, rice plant growth phases are assessed with the land and management factors. Along with the rice monitoring, rice yield estimation is done using regression. Result indicates that the NDVI stacks are invaluable to detect the cropping pattern throughout the time of surveillance. The result shows that there is significant correlation between NDVI and field level yield(r= 0.414 and r2adj=24.5%. The land and management factors and NDVI combination was accounted for 68.88% of the yield variability. The factors that affect the yield and NDVI are not same.