Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data

Abstract

The spatial distribution and location of crops are necessary information for agricultural planning. The free availability of optical satellites such as Landsat offers an opportunity to obtain this key information. Crop type mapping using satellite data is challenged by its reliance on ground truth data. The Integrated Administration and Control System (IACS) data, submitted by farmers in Europe for subsidy payments, provide a solution to the issue of periodic field data collection. The present study tested the performance of the IACS data in the development of a generalized predictive crop type model, which is independent of the calibration year. Using the IACS polygons as objects, the mean spectral information based on four different vegetation indices and six Landsat bands were extracted for each crop type and used as predictors in a random forest model. Two modelling methods called single-year (SY) and multiple-year (MY) calibration were tested to find out their performance in the prediction of grassland, maize, summer, and winter crops. The independent validation of SY and MY resulted in a mean overall accuracy of 71.5% and 77.3%, respectively. The field-based approach of calibration used in this study dealt with the ‘salt and pepper’ effects of the pixel-based approach.

Combining continuous data on soil properties and land use to explore soil carbon changes and soil structure indicators

“…Several studies have used the IACS database in recent years, e.g., to classify and assess land use 56,57 and facilitate methodology development for the identification of crop types from satellite data 58,59 . For the purposes of this study, we collected and synthesized parcel-level data on crop cultivation for each reported field in Sweden for 18 years (2003 to 2020).…”

Winter Wheat Extraction Using Time-Series Sentinel-2 Data Based on Enhanced TWDTW in Henan Province, China

“…However, it requires vast expert knowledge and is strongly influenced by human subjective factors. To overcome these limitations, machine learning and deep-learning algorithms have been applied to the automatic extraction of planting areas [15][16][17][18][19][20][21]. Machine learning [22] algorithms can automatically extract the rules from known data and use these rules to predict unknown data, which can be applied well to different tasks such as classification, regression, and clustering.…”

Monitoring the Spatio-Temporal Dynamics of Shale Oil/Gas Development with Landsat Time Series: Case Studies in the USA

“…Although the above techniques are easier to implement, they may fail to provide comprehensive information regarding changes [32]. It was found that using feature space transformation operation [33,34] can provide additional textural and spatial features to solve the problem of the homologous spectrum and heterogeneous spectrum brought by spectral interpretation and reduce "salt and pepper effects" [35].…”