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.
Highlights
The increasing world population coupled with the high demand for agricultural resources [1]require reliable data on agricultural lands for decision making and planning towards the future [2].The knowledge on available croplands is fundamental to food security [3], sustainable cropping [4]and the maximization of food production [5]
The Integrated Administration and Control System (IACS) data used as reference data to calibrate and validate the developed models showed data used as reference dataand to calibrate validatemaize, the developed showed different field numbers, average field size, area for and grassland, summer models crops, and winter different field numbers, average field size, and area for grassland, maize, summer crops, and winter crops (Figure 5)
This study used a field-based approach to test the usefulness of IACS data in calibrating an random forest (RF)-based model to predict crop types from satellite images, that are not from the same year as the calibration year
Summary
The increasing world population coupled with the high demand for agricultural resources [1]require reliable data on agricultural lands for decision making and planning towards the future [2].The knowledge on available croplands is fundamental to food security [3], sustainable cropping [4]and the maximization of food production [5]. Require reliable data on agricultural lands for decision making and planning towards the future [2]. The knowledge on available croplands is fundamental to food security [3], sustainable cropping [4]. Information about the spatial distribution of crops and the spatial extent of croplands are essential to ascertain the impact of any human activity on croplands [6]. Reliable and accurate information about agricultural lands requires an efficient and precise approach, which remote sensing (RS) can offer [7,8,9]. The advent of satellite-based optical RS has revolutionized large-scale cropland mapping and has been used in many local, regional, and global agricultural projects [4,13,14,15]
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