Abstract
ABSTRACTAccurate classification and mapping of crops is essential for supporting sustainable land management. Such maps can be created based on satellite remote sensing; however, the selection of input data and optimal classifier algorithm still needs to be addressed especially for areas where field data is scarce. We exploited the intra-annual variation of temporal signatures of remotely sensed observations and used prior knowledge of crop calendars for the development of a two-step processing chain for crop classification. First, Landsat-based time-series metrics capturing within-season phenological variation were preprocessed and analyzed using Google Earth Engine cloud computing platform. The developmental stage of each crop was modeled by fitting harmonic function. The model’s output was further used for the automatic generation of training samples. Second, several classification methods (support vector machines, random forest, decision fusion) were tested. As input data for crop classification, composites based on Sentinel-1 and Landsat images were used. Overall classification accuracies exceeded 80% when the seasonal composites were used. Winter cereals were the most accurately classified, while we observed misclassifications among summer crops. The proposed approach offers a potential to accurately map crops in the areas where in situ field data are scarce or unavailable.
Highlights
Due to a growing world population and decreasing land and water resources, there is a need for enhancing agricultural productivity to ensure food security
According to the approach described in the previous section, crop maps were produced using one of the supervised algorithms (RF, SVM and DF) and different input time series derived by single Landsat or Sentinel-1 sensors and their combination
We discussed the delineation of main crop types in the central region of Ukraine using multisource remote sensing data
Summary
Due to a growing world population and decreasing land and water resources, there is a need for enhancing agricultural productivity to ensure food security. Accurate crop maps from Earth Observation can build the basis for agricultural monitoring and decision-making at wider spatial scales (Kussul, Lemoine, Gallego, Skakun, & Lavreniuk, 2015a; Löw & Duveiller, 2014) to support sustainable agricultural land management. Explicit information on the distribution of croplands and crop types can assist accurate statistical estimation such as yield prediction and crop area estimation (Kogan et al, 2013; Kussul et al, 2015b; Pan et al, 2012) and so support policy making (Davidson, Fisette, Mcnairn, & Daneshfar, 2017). The frequency of data acquisition is nowadays rather high, which allows to discriminate different crops and to assess their growth stage
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