Improving Crop Mapping by Using Bidirectional Reflectance Distribution Function (BRDF) Signatures with Google Earth Engine
نویسندگان
چکیده
Recent studies have demonstrated the potential of using bidirectional reflectance distribution function (BRDF) signatures captured by multi-angle observation data to enhance land cover classification and retrieve vegetation architectures. Considering diversity crop architectures, we proposed that mapping precision may be enhanced BRDF signatures. We compared accuracy four supervised machine learning classifiers provided Google Earth Engine (GEE), namely random forest (RF), regression trees (CART), support vector (SVM), Naïve Bayes (NB), moderate resolution imaging spectroradiometer (MODIS) nadir BRDF-adjusted (MCD43A4 V6) albedo model parameter (MCD43A1 as input. Our results indicated leads a improvement in most cases, from single direction. Specifically, overall validation increased up 4.9%, kappa coefficients 0.092. Furthermore, were ranked order accuracy, highest lowest: RF, CART, SVM, NB. study contributes development application satellites.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2023
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs15112761