Fusion of Multi-Temporal PAZ and Sentinel-1 Data for Crop Classification

Autor:	Benjamín Arias-Pérez, Mario Busquier, Nilda Sánchez, Rubén Valcarce-Diñeiro, Juan M. Lopez-Sanchez, Javier Plaza
Přispěvatelé:	Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal, Universidad de Alicante. Instituto Universitario de Investigación Informática, Señales, Sistemas y Telecomunicación
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Synthetic aperture radar fusion Time series Series (mathematics) Computer science Science X band crop classification synthetic aperture radar time series Radio spectrum law.invention Random forest Crop classification Identification (information) law Teoría de la Señal y Comunicaciones General Earth and Planetary Sciences Radar Fusion Constellation Remote sensing
Zdroj:	Remote Sensing; Volume 13; Issue 19; Pages: 3915 Remote Sensing, Vol 13, Iss 3915, p 3915 (2021) RUA. Repositorio Institucional de la Universidad de Alicante Universidad de Alicante (UA)
ISSN:	2072-4292
DOI:	10.3390/rs13193915
Popis:	The accurate identification of crops is essential to help environmental sustainability and support agricultural policies. This study presents the use of a Spanish radar mission, PAZ, to classify agricultural areas with a very high spatial resolution. PAZ was recently launched, and it operates at X band, joining the synthetic aperture radar (SAR) constellation along with TerraSAR-X and TanDEM-X satellites. Owing to its novelty and its ability to classify crop areas (both taking individually its time series and blending with the Sentinel-1 series), it has been tested in an agricultural area of the central-western part of Spain during 2020. The random forest algorithm was selected to classify the time series under five alternatives of standalone/fused data. The map accuracy resulting from the PAZ series standalone was acceptable, but it highlighted the need for a denser time-series of data. The overall accuracy provided by eight PAZ images or by eight Sentinel-1 images was below 60%. The fusion of both sets of eight images improved the overall accuracy by more than 10%. In addition, the exploitation of the whole Sentinel-1 series, with many more observations (up to 40 in the same temporal window) improved the results, reaching an overall accuracy around 76%. This overall performance was similar to that obtained by the joint use of all the available images of the two frequency bands (C and X). This work was funded by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P.
Databáze:	OpenAIRE
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