Abstract
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).
Highlights
The rapid growth of the world population, which is expected to reach 8.5 billion in 2030 according to the United Nations [1], along with the economic and social importance of the agricultural sector and the uncertainty in the changes of production caused by climate change [2,3], calls for the development of procedures and techniques to control and efficiently manage natural resources
In order to test the performance of different data sets and their combination, the classification has been carried out in five different situations considering the different number of total data in each series (8 for PAZ vs. 40 of S1), which are detailed in the following subsections
In this test we limited the input data set to the 8 S1 images acquired in the dates closest to the PAZ acquisitions in order to perform a fair comparison between sensors, so that the influence of the frequency band (i.e., C band for S1 vs. X band for PAZ) upon the classification would be studied
Summary
The rapid growth of the world population, which is expected to reach 8.5 billion in 2030 according to the United Nations [1], along with the economic and social importance of the agricultural sector and the uncertainty in the changes of production caused by climate change [2,3], calls for the development of procedures and techniques to control and efficiently manage natural resources. Within this context, the classification and identification of agricultural crops is one of the research topics which help to manage the earth’s natural vegetation cover. Optical remote sensing at high-resolution (e.g., Sentinel-2, WorldView, Landsat, etc.) has become one key data source to create crop-type maps [7,10,11,12,13]
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