Open Access
Crop classification based on temporal signatures of Sentinel-1 observations over Navarre province, Spain
(MDPI, 2020) Arias Cuenca, María; Campo-Bescós, Miguel; Álvarez-Mozos, Jesús; Ingeniería; Ingeniaritza
Crop classification provides relevant information for crop management, food security assurance and agricultural policy design. The availability of Sentinel-1 image time series, with a very short revisit time and high spatial resolution, has great potential for crop classification in regions with pervasive cloud cover. Dense image time series enable the implementation of supervised crop classification schemes based on the comparison of the time series of the element to classify with the temporal signatures of the considered crops. The main objective of this study is to investigate the performance of a supervised crop classification approach based on crop temporal signatures obtained from Sentinel-1 time series in a challenging case study with a large number of crops and a high heterogeneity in terms of agro-climatic conditions and field sizes. The case study considered a large dataset on the Spanish province of Navarre in the framework of the verification of Common Agricultural Policy (CAP) subsidies. Navarre presents a large agro-climatic diversity with persistent cloud cover areas, and therefore, the technique was implemented both at the provincial and regional scale. In total, 14 crop classes were considered, including different winter crops, summer crops, permanent crops and fallow. Classification results varied depending on the set of input features considered, obtaining Overall Accuracies higher than 70% when the three (VH, VV and VH/VV) channels were used as the input. Crops exhibiting singularities in their temporal signatures were more easily identified, with barley, rice, corn and wheat achieving F1-scores above 75%. The size of fields severely affected classification performance, with ~14% better classification performance for larger fields (>1 ha) in comparison to smaller fields (<0.5 ha). Results improved when agro-climatic diversity was taken into account through regional stratification. It was observed that regions with a higher diversity of crop types, management techniques and a larger proportion of fallow fields obtained lower accuracies. The approach is simple and can be easily implemented operationally to aid CAP inspection procedures or for other purposes. © 2020 by the authors.
Open Access
Inter-comparison of atmospheric correction methods on Sentinel-2 images applied to croplands
(IEEE, 2018) Sola Torralba, Ion; Álvarez-Mozos, Jesús; González de Audícana Amenábar, María; Ingeniería; Ingeniaritza
Atmospheric correction of high resolution satellite scenery is a necessary preprocessing step for applications where bottom of atmosphere (BOA) reflectances are needed. The selection of the best atmospheric correction method to use on images acquired from new platforms, such as Sentinel-2, is essential to provide accurate BOA reflectances. In this work the performance of three atmospheric correction methods (6S, MAJA and SEN2COR) applied to Sentinel-2 scenes are compared by evaluating the resultant spectral signatures of six crop types on two specific dates, and their NDVI time series along a complete year. Although SEN2COR introduced greater corrections, especially in the infrared bands, the results suggest a varying performance of the methods depending on the land cover and the atmospheric conditions. Further research, particularly incorporating ground truth data, is recommended to rigorously validate the different atmospheric methods.
Open Access
Crop type mapping based on Sentinel-1 backscatter time series
(IEEE, 2018) Arias Cuenca, María; Campo-Bescós, Miguel; Álvarez-Mozos, Jesús; Ingeniería; Ingeniaritza
The high revisit time of Sentinel-1 (S1) observations enables the design of crop type mapping approaches exploiting the backscatter time series observed for the different crops. The objective of this study is to propose a supervised crop classification methodology based on the temporal signature of crops. With this aim 29 dual-pol S1 observations acquired over an agricultural area of Spain, where ground truth was available, were processed. The classification approach was based on the temporal signatures obtained for each polarization channel (VH, VV and the cross-pol ratio) for the different crops. Highest accuracies were obtained when fields were assigned to the class that minimized the RMSE, with an overall accuracy of 79% and best results for rapeseed, sunflower, alfalfa and barley.
Open Access
New methodology for wheat attenuation correction at C-Band VV-polarized backscatter time series
(IEEE, 2022) Arias Cuenca, María; Campo-Bescós, Miguel; Arregui Odériz, Luis Miguel; González de Audícana Amenábar, María; Álvarez-Mozos, Jesús; Agronomia, Bioteknologia eta Elikadura; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Agronomía, Biotecnología y Alimentación; Ingeniería
Wheat is one of the most important crops worldwide, and thus the use of remote sensing data for wheat monitoring has attracted much interest. Synthetic Aperture Radar (SAR) observations show that, at C-band and VV polarization, wheat canopy attenuates the surface scattering component from the underlying soil during a significant part of its growth cycle. This behavior needs to be accounted for or corrected before soil moisture retrieval is attempted. The objective of this paper is to develop a new method for wheat attenuation correction (WATCOR) applicable to Sentinel-1 VV time series and based solely on the information contained in the time series itself. The hypothesis of WATCOR is that without attenuation, VV backscatter would follow a stable long-term trend during the agricultural season, with short-term variations caused by soil moisture dynamics. The method relies on time series smoothing and changing point detection, and its implementation follows a series of simple steps. The performance of the method was compared by evaluating the correlation between backscatter and soil moisture content in six wheat fields with available soil moisture data. The Water Cloud Model (WCM) was also applied as a benchmark. The results showed that WATCOR successfully removed the attenuation in the time series, and achieved the highest correlation with soil moisture, improving markedly the correlation of the original backscatter. WATCOR can be easily implemented, as it does not require parameterization or any external data, only an approximate indication of the period where attenuation is likely to occur.

Álvarez-Mozos, Jesús

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