Improving Crop Mapping by Using Bidirectional Reflectance Distribution Function (BRDF) Signatures with Google Earth Engine

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 11; p. 2761
• Main Authors: Zhen, Zhijun, Chen, Shengbo, Yin, Tiangang, Gastellu-Etchegorry, Jean-Philippe
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25.05.2023; MDPI
• Subjects: Accuracy; Agricultural production; Albedo; Artificial intelligence; Artificial satellites in remote sensing; Bidirectional reflectance; bidirectional reflectance distribution function (BRDF); Classification; Classification schemes; Classifiers; Comparative analysis; Corn; Crop development; Crop diversification; crop mapping; Crops; Distribution functions; Field study; Global positioning systems; Google Earth Engine (GEE); GPS; Identification and classification; Internet; kernel-driven model; Land cover; Location; Machine learning; Mapping; Measurement; Precipitation; Reflectance; Regression analysis; Remote sensing; Rice; Satellite observation; Satellites; Sciences of the Universe; Signatures; Spectroradiometers; supervised classification; Supervised learning; Support vector machines; vegetation; China; Jilin China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15112761

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 the diversity of crop architectures, we proposed that crop mapping precision may be enhanced by using BRDF signatures. We compared the accuracy of four supervised machine learning classifiers provided by the Google Earth Engine (GEE), namely random forest (RF), classification and regression trees (CART), support vector machine (SVM), and Naïve Bayes (NB), using the moderate resolution imaging spectroradiometer (MODIS) nadir BRDF-adjusted reflectance data (MCD43A4 V6) and BRDF and albedo model parameter data (MCD43A1 V6) as input. Our results indicated that using BRDF signatures leads to a moderate improvement in classification results in most cases, compared to using reflectance data from a single nadir observation direction. Specifically, the overall validation accuracy increased by up to 4.9%, and the validation kappa coefficients increased by up to 0.092. Furthermore, the classifiers were ranked in order of accuracy, from highest to lowest: RF, CART, SVM, and NB. Our study contributes to the development of crop mapping and the application of multi-angle observation satellites.