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Arias Cuenca, María

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https://academica-e.unavarra.es/handle/2454/48581

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Arias Cuenca

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María

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Ingeniería

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IS-FOOD. Research Institute on Innovation & Sustainable Development in Food Chain

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0000-0001-7542-261X

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811586

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2020 - 20235
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Now showing 1 - 5 of 5
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    PublicationOpen Access
    On the influence of acquisition geometry in backscatter time series over wheat
    (Elsevier, 2022) Arias Cuenca, María; Campo-Bescós, Miguel; Álvarez Mozos, Jesús; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ingeniería; Gobierno de Navarra / Nafarroako Gobernua
    Dense time series of Sentinel-1 imagery are an invaluable information source for agricultural applications. Multiple orbits can observe a specific area and their combination could improve the temporal resolution of the time series. However, the orbits have different acquisition geometries regarding incidence and azimuth angles that need to be considered. Furthermore, crops are dynamic canopies and the influence of incidence and azimuth angles might change during the agricultural season due to different phenological stages. The main objective of this letter is to evaluate the influence of different acquisition geometries in Sentinel-1 backscatter time series over wheat canopies, and to propose a strategy for their correction. A large dataset of wheat parcels (∼40,000) was used and 344 Sentinel-1 images from three relative orbits were processed during two agricultural seasons. The first analysis was a monthly evaluation of the influence of incidence angle on backscatter (σ0) and terrain flattened backscatter (γ0). It showed that terrain flattening significantly reduced the backscatter dependence on incidence angle, being negligible in VH polarization but not completely in VV polarization. Incidence angle influence in VV backscatter changed in time due to wheat growth dynamics. To further reduce it, an incidence angle normalization technique followed by an azimuthal anisotropy correction were applied. In conclusion, γ0 enabled a reasonable combination of different relative orbits, that may be sufficient for many applications. However, for detailed analyses, the correction techniques might be implemented to further reduce orbit differences, especially in bare soil periods or winter months.
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    PublicationOpen 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.
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    PublicationOpen 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.
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    PublicationOpen Access
    Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields
    (Elsevier, 2023) Arias Cuenca, María; Notarnicola, Claudia; Campo-Bescós, Miguel; Arregui Odériz, Luis Miguel; Álvarez Mozos, Jesús; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    Soil moisture (SM) is a key variable in agriculture and its monitoring is essential. SM determines the amount of water available to plants, having a direct impact on the development of crops, on the forecasting of crop yields and on the surveillance of food security. Microwave remote sensing offers a great potential for estimating SM because it is sensitive to the dielectric characteristics of observed surface that depend on surface soil moisture. The objective of this study is the evaluation of three change detection methodologies for SM estimation over wheat at the agricultural field scale based on Sentinel-1 time series: Short Term Change Detection (STCD), TU Wien Change Detection (TUWCD) and Multitemporal Bayesian Change Detection (MTBCD). Different methodological alternatives were proposed for the implementation of these techniques at the agricultural field scale. Soil moisture measurements from eight experimental wheat fields were used for validating the methodologies. All available Sentinel-1 acquisitions were processed and the eventual benefit of correcting for vegetation effects in backscatter time series was evaluated. The results were rather variable, with some experimental fields achieving successful performance metrics (ubRMSE ~ 0.05 m3 /m3 ) and some others rather poor ones (ubRMSE > 0.12 m3 / m3 ). Evaluating median performance metrics, it was observed that both TUWCD and MTBCD methods obtained better results when run with vegetation corrected backscatter time series (ubRMSE ~0.07 m3 /m3 ) whereas STCD produced similar results with and without vegetation correction (ubRMSE ~0.08 m3 /m3 ). The soil moisture content had an influence on the accuracy of the different methodologies, with higher errors observed for drier conditions and rain-fed fields, in comparison to wetter conditions and irrigated fields. Taking into account the spatial scale of this case study, results were considered promising for the future application of these techniques in irrigation management.
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    PublicationOpen Access
    Sentinel-1 time series applications over agricultural fields: proposal, evaluation and comparison of different methodologies
    (2023) Arias Cuenca, María; Álvarez Mozos, Jesús; Campo-Bescós, Miguel; Ingeniería; Ingeniaritza
    La monitorización de los cultivos es esencial para diferentes aplicaciones, como el aseguramiento de la seguridad alimentaria, la gestión de los cultivos y la implementación de políticas agrarias. La teledetección proporciona información acerca de las propiedades biofísicas de las plantas y los suelos, así como de la variabilidad espacial en amplias áreas del territorio de forma periódica. El lanzamiento de los satélites radar de apertura sintética (SAR) Sentinel-1 en 2014 y 2016 permitió la adquisición de series temporales densas de imágenes con buena resolución espacial y temporal incluso en zonas cubiertas de nubes. El principal objetivo de esta tesis es la evaluación de diferentes metodologías para aplicaciones agrícolas a escala de parcela usando series temporales Sentinel-1. En primer lugar, se propuso una metodología de clasificación de cultivos supervisada basada en las firmas temporales de Sentinel-1. Se implementó en un caso de estudio con 14 clases de cultivos y un dataset grande de parcelas agrícolas. En segundo lugar, se evaluó la influencia de la geometría de adquisición de las imágenes Sentinel-1 sobre parcelas de trigo. Se evaluó la influencia del ángulo de incidencia en la retrodispersión y los coeficientes de terrain-flattening, y se aplicó una normalización del ángulo de incidencia seguida de una corrección de la anisotropía azimutal en las series temporales de polarización VV. En tercer lugar, se evaluó la atenuación de la retrodispersión producida por las cubiertas de trigo en polarización VV, y un nuevo método de corrección del efecto de la atenuación llamado WATCOR fue propuesto. Finalmente, cuatro técnicas para estimar la humedad del suelo basadas en series temporales de Sentinel-1 en cultivo de trigo fueron evaluadas, proponiendo diferentes alternativas metodológicas para su aplicación a escala de parcela. A pesar de la complejidad de la estimación de la humedad del suelo a escala de parcela únicamente con datos SAR, se obtuvieron estimaciones aceptables. Los resultados de esta tesis demostraron que el análisis y la extracción de información contenida en series temporales SAR es útil para diferentes aplicaciones, augurando interesantes desarrollos futuros en este campo.