A machine learning approach for identifying and delineating agricultural fields and their multi-temporal dynamics using three decades of Landsat data

Abstract

High spatial and temporal resolution satellite imagery are essential data for land cover discrimination and mapping of vegetation dynamics, offering insights into the number, extent, and condition of agricultural fields. However, an accurate account of the number, location, and variability of fields can be challenging to obtain in a timely manner, particularly at scale or in regions where ground-supporting data are not available, limiting the capacity for food production and water use assessment and planning. To bridge this capacity gap, a convolutional neural network approach was adopted and combined with two clustering techniques: the density-based spatial clustering of applications with noise (DBSCAN) and spectral clustering, to provide an account of agricultural fields and their extent across a region lacking ground based information. A random forest classification was also employed to discriminate crop types. Using an annual maximum normalized difference vegetation index (NDVI) derived from 2018 Landsat-8 data, the approach was applied to classify the shape and delineate the extent of agricultural fields across an agricultural region in Saudi Arabia that had an area under irrigation exceeding 2,300 km2. When assessed against manually identified center-pivot fields (CPFs), the method achieved 97.4% producer’s and 98.0% user’s accuracies on an object basis, and 81.4% producer’s and 85.4% user’s accuracies on a pixel basis for identifying non-CPFs (i.e., tree crop plantations and other non-woody crops). The over- and under-segmentation error for CPFs was 1.5% and 1.0%, respectively, with intersection over union errors reported as being 3.5%. The framework showed stability when retrospectively applied to Landsat data from the year 2000, returning 97.5% producer’s and 96.6% user’s accuracies for CPF identification. In order to characterize the temporal dynamics of agricultural development over the past three decades, an analysis of field behavior between 1988 and 2020 was subsequently undertaken. The analysis indicated that the number of CPFs in the study region increased from 45 (covering 20 km2) in 1988 to 5,080 CPFs by 2016 (covering 2,368 km2), followed by a recent reduction to 3,700 CPFs in 2020 (covering 1,581 km2). Through the multi-temporal analysis, individual fields were able to be characterized in terms of their expansion, contraction and activity throughout the study period. Overall, the proposed method was simple to train, efficient in dealing with large datasets, relied on limited in-situ records to a very small degree, and has the potential to be applied to larger national scales, providing an ongoing assessment of important agroinformatic metrics.