Monitoring Landsat vegetation indices for different crop treatments and soil chemistry

Abstract

ABSTRACTThe timing and quantity of fertilizer and herbicide applications in agricultural systems are critical where maximizing vigour and yield is the ultimate goal. While fertilizers are applied to the soil to promote plant growth, herbicides are commonly used to control weeds in order to reduce the weeds’ competition for nutrients. Satellite imagery is frequently used to monitor agricultural activities and vegetation indices (VIs) are widely applied in temporal analysis of crop status. This study considers monitoring Landsat VIs for the period between 5 June and 27 October 2014 in agricultural systems under four different management treatments at the Kellogg Biological Station (KBS), in Michigan, USA. The results show that (1) fine-tuning conventional treatments by intense early herbicide applications in combination with no-tilled soil results in significantly higher VIs during the early growth stage, a more rapid maturity rate, and the highest crop yield; (2) nitrogen uptake from nitrate-based rather than from ammonium-based fertilizers might be more beneficial in terms of crop vigour and yield return; (3) organic treatments, with organic corn and no agricultural chemicals, keep higher VIs longer in the season at the cost of lower yield; and (4) genetically modified (GM) breeds under conventional or reduced-chemical treatments have synchronized early senescence. A positive correlation between VIs during the early growth stage and yield is observed for conventional no-till treatment (coefficient of determination, R2 = 0.70). The correlation becomes gradually weaker with each month from late June to October (29 June: R2 = 0.70; 16 August: R2 = 0.61; 17 September: R2 = 0.44; 27 October: R2 = 0.01). The analysis of variance (ANOVA)–Tukey–Kramer approach suggests significant differences in VIs between organic and GM corn (treated conventionally or with reduced chemicals) for the preharvest season (27 October 2014). The leave-out-one cross-validation analysis confirms the predictive accuracy of the model (mean square error (MSE) = 0.0014). The rapid evolution of herbicide-resistant weeds requires constant refinement of chemical inputs to agricultural systems, thus making the monitoring of (Landsat) VIs important in the years to come.