The Use of Proba-V data for Global Agricultural Monitoring

Abstract:

Land conversion, forest cutting, urban growth, agricultural expansion, take place at scales which are unprecedented in history and at such a pace that they are not only subject of scientific studies but also have a strong economic impact. Understanding and measuring dynamics becomes a prerequisite for companies, governments, agencies, NGO’s, research institutes and society in general. In many of these cases the temporal frequency of the information is a clear requirement to detect phenomena that can occur within a few days (related to crops, forests and other ecosystems) and at a certain geographic scale. For example frequent updates on crop condition and production is needed to stabilize agricultural markets. This is already being picked up by large initiatives like the GEOGLAM AMIS system.

Observations over large areas are available through satellites, however challenges remain;

• on the one hand side obtaining frequent and consistent observations at sufficient level of detail to identify spatial phenomena. At present, no single mission is capable of providing near daily information of any place in the world at scales in which changes in land cover/use can be identified in a consistent manner.
• On the other hand side the need for a historical reference. For agricultural monitoring and early warning purposes the comparison of the actual data with the historical reference is of the utmost importance.

The Proba-V mission is a first attempt to overcome these challenges. From its design and within the GIO-Global Land component a lot of work has been done to ensure the integration of the Proba-V data with the 15 years historical archive of SPOT-VEGETATION. In this respect Proba-V observation will be intercomparable with the SPOT-VGT historical baseline which will ensure the continuation of the standard agricultural monitoring products. Next to this integration with the historical archive, Proba-V also ensures an increase in spatial resolution of the data sets, from 1km to 300m and even 100m (with some loss in the temporal domain).

Within the framework of the FP7 SIGMA project, currently Europe’s largest contribution to the abovementioned GEOGLAM initiative, we have been looking at the use of this 100m data set for agricultural monitoring.

Results of this study will be presented here.