# CROpland Mapping Using Earth Observation Derived Phenological Metrics +
###### Abstract  
Satellite Earth observations provide timely and spatially explicit information on crop phenology that can support decision making and sustainable agricultural land management. Accurate classification and mapping of croplands is primary information for agricultural assessments. This study presents a digital agriculture approach that integrates Earth Observation big data analytics based on machine learning technologies to classify and map main crop types. Two supervised machine learning models were calibrated using the Random Forest algorithm from phenological metrics, estimated from time series of NDVI and LAI vegetation indices calculated using Sentinel-2 MSI satellite acquisitions. Models were calibrated for the Toscana region in Italy. The results show a satisfactory overall accuracy (-78%) in cropland classification, and the model calibrated using LAI time series performed slightly better than the model calibrated using NDVI time series. The proposed approach offers the potential to accurately map crop types in a way that is useful to support agricultural land management and monitoring systems for large areas over time.  
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Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.  
Multitemporal satellite images have proven to be successfully used to estimate vegetation's biophysical parameters and to identify phenological patterns [5; 6; 7]. Recently, the Copernicus Sentinel-2 satellite constellation, equipped with an MSI sensor, was able to sense the Earth's surface at high spatial, spectral, and temporal resolutions, showing its potential for the estimation of vegetation parameters, such as phenological metrics (e.g., the start of season, the length of season, or the end of season) [7; 8].  
Many authors have investigated the efficacy of spectral and biophysical time series indices to differentiate crop types [9; 10]. Vegetation spectral indices have been and are still widely used to detect the status of vegetation (e.g., growth, health, and cover), the most popular of which is the normalized difference vegetation index (NDVI) [11]. However, NDVI has saturation as its limit at high values. On the other hand, vegetation's biophysical characteristics, such as the canopy structure and photosynthetic capacity, are well described by the leaf area index (LAI) largely used in agricultural studies in heterogeneous smallholder and fragmented agroecosystems [12; 13].  
Furthermore, the advances in analytical techniques, such as machine learning algorithms, enable us to deal with fast and robust analyses applied to big data. Among these, Random Forest (RF) is an ensemble learning classifier that has been successfully used in vegetation classification applications, including crop mapping [7; 10; 14].  
The aim of this study was to present a digital agriculture approach that integrates EO big data analytics, based on a supervised machine learning model using temporal statistics and phenological metrics estimated from NDVI and LAI time series as predictors, to identify and map the main crop types. The performance of two supervised machine learning models, calibrated using the RF algorithm for a study area in central Italy, are presented and discussed.