Autumn-based vegetation indices for estimating nitrate leaching during autumn and winter in arable cropping systems

Abstract

Nitrate leaching losses from arable cropping systems cause severe damage to environmental services worldwide. There is a considerable need for methods that allow rapid, easy and area covering detection of nitrate leaching to guide nitrogen (N) management. We used three years of data from the 5th cycle of a long-term crop rotation experiment in Denmark to quantify the relationships between nitrate leaching in autumn and winter and field conditions in autumn as defined by three vegetation indices [VIs; Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), Ratio Red Edge (RRE)] in autumn. Following cereal crops, cover crops significantly reduced nitrate leaching in autumn and winter, whereas this was only the case for one out of the three years after faba bean. In autumn and winter, a negative relationship was found between nitrate leaching and cover crop aboveground biomass or aboveground N, and the thresholds were ∼2 Mg ha−1 for aboveground cover crop biomass and 60 kg N ha−1 for aboveground cover crop N, above which nitrate leaching was low and stable. A significant positive linear relationship was found between nitrate leaching and soil water nitrate concentration in autumn and winter with a slope of 4.0 kg N ha−1 per mg L−1 of nitrate-N in the soil. Although all three VIs showed significant negative relationships with nitrate leaching during autumn and winter, the sensitivities of RVI and NDVI to estimated nitrate leaching were lost sharply when nitrate leaching exceeded 100 kg N ha−1 and 50 kg N ha−1, while RRE exhibited consistent low noise equivalent values for the nitrate leaching in autumn and winter. These results provide evidence for the scope of assessing risk of nitrate leaching from agricultural soils based on newly launched satellite remote sensing platforms with NDVI and red edge bands. To maximize the application of these technologies it will be necessary to combine remote sensing with information on cropping systems and biophysical conditions.