Spatial-temporal transferability assessment of remote sensing data models for mapping agricultural land use

To assess the impact of anthropogenic and natural causes on land use and land use cover change, mapping of spatial and temporal changes is increasingly applied. Due to the availability of satellite image archives, remote sensing (RS) data-based machine learning models are in particular suitable for mapping and analysing land use and land cover changes. Most often, models trained with current RS data are employed to estimate past land cover and land use using available RS data with the assumption that the trained model predicts past data values similar to the accuracy of present data. However, machine learning models trained on RS data from particular locations and times may not be well transferred to new locations and time datasets due to various reasons. This study aims to assess the spatial-temporal transferability of the RS data models in the context of agricultural land use mapping. The study was designed to map agricultural land use (5 classes: maize, grasslands, summer crops, winter crops, and mixed crops) in two regions in Germany (North Hesse and Weser Ems) between the years 2010 and 2018 using Landsat archive data (i.e., Landsat 5, 7, and 8). Three model transferability scenarios were evaluated, a) temporal - S1, b) spatial - S2 and c) spatial-temporal - S3. Two machine learning models (random forest - RF and Convolution Neural Network - CNN) were trained. For each transferability scenario, class-level F1 and macro F1 values were compared between the reference and targeted transferability systems. Moreover, to explain the results of transferability scenarios, transferability results were further explored using dissimilarity index and area of applicability (AOA) concepts. The average macro F1 value of the trained model for the reference scenario (no transferability) was 0.75. For assessed transferability scenarios, the average macro F1 values were 0.70, 0.65 and 0.60, for S1, S2, and S3 respectively. It shows that, when predicting data from different spatial-temporal contexts, the model performance is decreasing. In contrast, the average proportion of the data inside the AOA did not show a clear pattern for different scenarios. In the context of RS data-related model building, spatial-temporal transferability is essential because of the limited availability of the labelled data. Thus, the results from this case study provide an understanding of how model performance changes when the model is transferred to new settings with data from different temporal and spatial domains.