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Description
There is a convincing evidence that tillage operations have negative effects on soil, water and organic matter conservation (e.g., Foley, Nature 2011). Particularly in semi-arid regions, they produce an increase of soil erosion and evaporation that may reduce crop production (Aboudrare et al., Agricultural Water Management, 2006). For these reasons, the Food and Agricultural organization (FAO) of the United Nations promotes the Conservative Agriculture, i.e. the maintenance of a permanent soil cover with a minimum soil disturbance (i.e. no tillage). Indeed, Conservative Agriculture is expected to contribute to preserve soil water and nutrient availability in agricultural areas and maintain a sustained crop production (“Strategic work of FAO”, 2017).
Earth observation (EO) can be a cost effective tool to monitor soil tillage practices, which are characterized by a sparse occurrence in space and in time (Hadria et al., Agricultural Water Management, 2009).
The objective of this study is to describe a new technique, based on dense time series of Copernicus Sentinel-1 (S-1) and Sentinel-2 (S-2) data, aimed to identify tillage changes at regional/continental scale and at approximately 100 m resolution.
The technique applies to bare or scarcely vegetated fields identified by setting a threshold to time series of S-2 NDVI data. In order to identify tillage changes, the proposed approach implements a multi-scale change detection that is applied to S-1 VH data. Cross polarized components are exploited due to their high sensitivity to changes of surface roughness and, at the same time, relative insensitivity to anisotropic component of surface roughness (e.g., Wegmuller et al., Remote Sensing of Environment, 2011).
The detection of temporal VH changes is performed at two spatial scales, i.e. 0.1 km (high) and 5.0 km (medium). This is to discriminate between changes that take place only at field scale and, therefore, are likely due to tillage changes and those that take place both a field and medium scale and, therefore, are very likely due to large scale phenomena, such as precipitation events.
A proof of concept of the proposed methodology has been carried out at farm scale using in situ observations and S-1 and SPOT5-Take5 data, acquired over the agricultural Apulian Tavoliere JECAM site (southern Italy) in 2015. Subsequently, a large validation effort involving various sites in Europe and using S-1 and S-2 data has been undertaken in the context of the H2020 Sensagri project. In this paper, ground data collected in 2017 and 2018 over the Apulian Tavoliere site and including information on the tillage status of more than 600 fields has been analysed. First results on data acquired in 2017 indicate that the methodology can identify the tilled/no-tilled classes with an overall accuracy of 83%.
