Investigating potato production in the future by the EU-28 countries using sentinels and EU open datasets

Abstract

Europe is covered by distinct climatic zones which include semiarid, the Mediterranean, humid subtropical, marine, humid continental, subarctic, and highland climates. Land use and land cover change have been well documented in the past 200 years across Europe where land cover grassland and cropland together make up 39%. In recent years, the agricultural sector has been affected by abnormal weather events. Climate change will continue to change weather patterns, for some regions, this will have a positive impact. For other regions, it will be negative. Hail, heavy rainfall, floods, and droughts may lead to a reduction in agricultural land in some regions with an additional impact on biodiversity. For that reason, major climatic and hydrological variables such as temperature, precipitation, vegetation index, and soil moisture must be regularly monitored and the trends of their deviations from the normal must be observed. This research investigates the quantity of potato production in the future in two stages. In the first stage, Autoregressive Integrated Moving Average (ARIMA) models are used for yield and soil moisture forecasting. The models are trained on a dataset acquired in time series from the Soil Moisture and Ocean Salinity (SMOS) satellite from 2010 to 2019 and are compared by statistics on potato production from EUROSTAT at the national scale. In the second stage, the approach uses Support Vector Machine (SVM) architecture of Sentinel 2 Vegetation Index satellite imagery together with weather and Sentinel 1 surface soil moisture information for the EU-28 member countries by season 2018 for single potato fields. Agricultural statistical data such as crop calendar, map, production area, yield, and acreage are collected from Eurostat while vector data collection from Land Use and Land Cover Survey (LUCAS) and Land Cover from national to small scale agriculture (CORINE). The ARIMA output performances measure Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). RMSE for soil moisture forecasting is 0.3792, 0.2816, 0.2679, 0.2110, and 0.1791 for East, West, South, North, and Centre EU respectively. MSE for potato yield forecasting is 0.0954, 0.081, 0.0589, 0.0447, and 0.0199 for North, Centre, East, South, and West EU respectively. The number of known crop field areas and the distance between them are different for each country and this alters the dimension of the classification input. Two countries of EU-28 do not contain information on potato location while the input dimensions of another two are too big to perform. Therefore, the sensitivity analysis of SVM has been implemented for a total of 24 states. The classification accuracies for kernel Radial Basis function are 98.53% in Austria, 99.39% in Centre of UK, 99.64% in Italia, 99.12% in Bulgaria and Greece, 99.43% in Sweden for Centre, West, South, East, and North EU, respectively, with the Sentinel Vegetation Index. The classification accuracies change with the amount of ground-truth data. The results show that some geographical and socio-economic biases like the chemical composition of the soil or the degree of automation in the agricultural sector are influencing in the statistical analysis.