Yield Prediction Model for Potato Using Landsat Time Series Images Driven Vegetation Indices

Abstract

Crop growth monitoring and its yield forecasting is a pre-requisite task to ensure food security and to assess the economic return from agriculture. The application of remote sensing techniques in agricultural sector enhanced the potentiality of crop growth monitoring and estimation of crop yields. Over the last few years, potato cultivation in Bangladesh has promisingly increased. On the contrary, proper distribution capability and the storage capacity is still under the desirable condition due to lack of management system. In this context, remote sensing image analysis techniques were employed in this study to predict potato yield before harvesting to improve management system. This study used 16-day high resolution (~ 30 m) Landsat surface reflectance data for the year of 2010–2011 to detect the maximum normalized difference vegetation index (NDVI) value of a potato growing season. The NDVI will be the maximum after the 63 days of the plantation for Munshiganj District of Bangladesh. Afterward, six satellite images were selected from six growing seasons: 2010–2011, 2011–2012, 2012–2013, 2013–2014, 2014–2015, and 2015–2016 based on the day of the maximum NDVI. A total of 35 potato fields were identified from the filed investigation over the study area to extract the NDVI values from satellite images. Regression analysis was performed between NDVI values and filed level potato yields. The maximum coefficient of correlation (R2) of yield prediction equation was found to be 0.81 between the mean NDVI and potato yield. The equation was validated by using data from 2016 to 2017 growing season, and the result revealed that the difference between predicted and actual filed yield is about 10.4%. It has been found that the high-resolution Landsat images can be an effective means for early estimation of potato yield.