Google Earth Engine: Application Of Algorithms For Remote Sensing Of Crops In Tuscany (Italy)

Remote sensing has become an important mean to assess crop areas, specially for the identification of crop types. Google Earth Engine (GEE) is a free platform that provides a large number of satellite images from different constellations. Moreover, GEE provides pixel-based classifiers, which are used for mapping agricultural areas. The objective of this work is to evaluate the performance of different classification algorithms such as Minimum Distance (MD), Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART) and Naïve Bayes (NB) on an agricultural area in Tuscany (Italy). Four different scenarios were implemented in GEE combining different information such as optical and Synthetic Aperture Radar (SAR) data, indices and time series. Among the five classifiers used the best performers were RF and SVM. Integrating Sentinel-1 (S1) and Sentinel-2 (S2) slightly improves the classification in comparison to the only S2 image classifications. The use of time series substantially improves supervised classifications. The analysis carried out so far lays the foundation for the integration of time series of SAR and optical data.

[1]  Mrudul Dixit,et al.  Supervised classification of satellite images , 2016, 2016 Conference on Advances in Signal Processing (CASP).

[2]  Tao Zhou,et al.  Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region , 2019, Sensors.

[3]  J. Ndambuki,et al.  Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS , 2017 .

[4]  Muhammad Bilal,et al.  A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data , 2019, Remote. Sens..

[5]  Christopher M. Bishop,et al.  Neural networks and machine learning , 1998 .

[6]  A. Huete,et al.  A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[7]  Claire Marais-Sicre,et al.  Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data—From Temporal Signatures to Crop Parameters Estimation , 2013 .

[8]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[9]  R. Leemans,et al.  Comparing global vegetation maps with the Kappa statistic , 1992 .

[10]  A. Huete,et al.  A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[11]  Giles M. Foody,et al.  Crop classification by support vector machine with intelligently selected training data for an operational application , 2008 .

[12]  Rajendra Prasad,et al.  Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data , 2015 .

[13]  Jinwei Dong,et al.  Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. , 2016, Remote sensing of environment.

[14]  Ning Zhang,et al.  Global Crop Monitoring: A Satellite-Based Hierarchical Approach , 2015, Remote. Sens..

[15]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[16]  Luis Carrasco,et al.  Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine , 2019, Remote. Sens..

[17]  Takeshi Motohka,et al.  Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology , 2010, Remote. Sens..

[18]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[19]  Russell G. Congalton,et al.  Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine , 2017, Remote. Sens..

[20]  Christopher Conrad,et al.  Crop type classification using a combination of optical and radar remote sensing data: a review , 2019, International Journal of Remote Sensing.

[21]  Alan Woodley,et al.  Ensemble Classification Technique for Water Detection in Satellite Images , 2018, 2018 Digital Image Computing: Techniques and Applications (DICTA).

[22]  Takafumi Saito,et al.  Integration of Machine Learning and Open Access Geospatial Data for Land Cover Mapping , 2019, Remote. Sens..

[23]  Alexei Novikov,et al.  Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping , 2017, Front. Earth Sci..

[24]  John A. Gamon,et al.  Assessing leaf pigment content and activity with a reflectometer , 1999 .

[25]  Kristof Van Tricht,et al.  Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium , 2018, Remote. Sens..