02424nam a2200217 a 450000100080000000500110000800800410001910000120006024501740007226002040024650002870045052013430073765300170208065300170209765300080211465300120212265300210213465300160215565300170217170000180218810614112021-04-09       2020    bl uuuu     u01u1 u    #d1 aCAL, A.  aUnsupervised Methodology to In-Season Mapping of Summer Crops in Uruguay with Modis EVI's Temporal Series and Machine Learning. (Conference-paper)h[electronic resource]  aIEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings, March 2020, Article number 9165614, Pages 183-188. Doi: https://doi.org/10.1109/LAGIRS48042.2020.9165614c2020  aArtilce history: Date of Conference: 22-26 March 2020. Date Added to IEEE Xplore: 12 August 2020. Published in: 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS). INSPEC Accession Number: 19872572. Publisher: IEEE. Conference Location: Santiago, Chile, Chile.  aABSTRACT. This paper presents a new methodology for mapping summer crops in Uruguay, during the season, based on time-series analysis of the EVI vegetation index derived from the MODIS sensor. Time-series were processed with the k-means unsupervised machine learning algorithm. For this algorithm, the ideal number of clusters was estimated using the elbow method. Once the clusters were obtained, for each one, the average phenological signature was adjusted using a nonlinear smoothing spline regression technique. Additionally, using the derivative analysis, the key points of the curve were estimated (minimum, maximum and inflection points). When analyzing the average signature of each cluster, those whose signature follows the seasonal pattern of an agricultural crop (similar to a Gaussian function) were selected to generate a binary map of crops/non-crops. The estimated crop area is 2,336,525 hectares, higher than the official statistics of l,667,400 hectares for the 2014-15 season. This overestimation can be explained by the resolution of the MODIS pixel (250 meters), where each has a different degree of purity; and commission errors. The methodology was validated with 5,317 ground truth points, with a general accuracy of 95.8%, kappa index of 85.6, production and user accuracy of 85.1% and 91.3% for crops/non-crops.  aCROP MAPPING  aELBOW METHOD  aEVI  aK-MEANS  aSMOOTHING SPLINE  aTIME-SERIES  aUNSUPERVISED1 aTISCORNIA, G.