Improving the estimation of soil-available nutrients at the sub-field scale using time-series UAV observations

Abstract

ABSTRACT Soil-available nutrients (SANs)are essential for crop growth and yield formation. Appropriate variable rate fertilization (VRF) can control SAN at a normal level to avoid unnecessary damage to sustainable production capacity. The precondition of optimizing the application of VRF is obtaining the real-time status of SAN. A new method for SAN estimation has been proposed by integrating modified World Food Studies (WOFOST) and time-series satellite remote sensing (RS) data. This method can provide field scale SAN estimations with high accuracy. However, the estimation accuracy at a subfield scale was low for VRF application because of the poor spatial resolution of common satellite imagery. In this letter, the subfield SAN estimations were optimized to ensure the VRF value. Time-series multispectral images acquired by an unmanned aerial vehicle (UAV) were used to replace common satellite data, and the SAN values for haplic phaeozem in selected spring maize plot in Hongxing Farm (48°09ʹ N, 127°03ʹ E) were estimated. Based on the field SAN data, the estimation accuracies using satellite data and UAV data were analyzed. The results show that the UAV data improved SAN estimations at the subfield scale).