BUSCADOR RECOLECTA

The aim of this study was to assess the utility of Sentinel-2 images in the monitoring of the fractional vegetation cover (FVC) of rainfed alfalfa in semiarid areas such as that of Bardenas Reales in Spain. FVC was sampled in situ using 1 m2 surfaces at 172 points inside 18 alfalfa fields from late spring to early summer in 2017 and 2018. Different vegetation indices derived from a series of Sentinel-2 images were calculated and were then correlated with the FVC measurements at the pixel and parcel levels using different types of equations. The results indicate that the normalized difference vegetation index (NDVI) and FVC were highly correlated at the parcel level (R 2 = 0.712), where as the correlation at the pixel level remained moderate across each of the years studied. Based on the findings, another 29 alfalfa plots (28 rainfed; 1 irrigated) were remotely monitored operationally for 3 years (2017–2019), revealing that location and weather conditions were strong determinants of alfalfa growth in Bardenas Reales. The results of this study indicate that Sentinel-2 imagery is a suitable tool for monitoring rainfed alfalfa pastures in semiarid areas, thus increasing the potential success of pasture management., Andres Echeverria was supported by a predoctoral fellowship from the Government of Navarra. This work was supported by the knowledge transfer contract 2018020023 UPNA-Bardenas Reales Committee with partial collaboration of the project PID2019-107386RB-I00/AEI/10.13039/
501100011033 (MINECO/FEDER-UE).

The Arribes del Duero region spans the border of both Spain and Portugal along the Duero
River. On both sides of the border, the region boasts unique human‐influenced ecosystems. The
borderland landscape is dotted with numerous villages that have a history of maintaining and managing
an agrosilvopastoral use of the land. Unfortunately, the region in recent decades has suffered
from massive outmigration, resulting in significant rural abandonment. Consequently, the oncemaintained
landscape is evolving into a more homogenous vegetative one, resulting in a greater
propensity for wildfires. This study utilizes an interdisciplinary, integrated approach of “bottom
up” ethnography and “top down” remote sensing data from Landsat imagery, to characterize and
document the diachronic vegetative changes on the landscape, as they are perceived by stakeholders
and satellite spectral analysis. In both countries, stakeholders perceived the current changes and
threats facing the landscape. Remote sensing analysis revealed an increase in forest cover throughout
the region, and more advanced, drastic change on the Spanish side of the study area marked by
wildfire and a rapidly declining population. Understanding the evolution and history of this rural
landscape can provide more effective management and its sustainability., This research was supported by a doctoral research fellowship from the Universidad Pública
de Navarra with the Institute for Advanced Social Science Research (I‐COMMUNITAS). This
research was partly funded by the Spanish Research Agency, Ministry for Science and Innovation
through projects PID2019‐104297GB‐I00 and PID2019‐107386RB‐I00 / AEI / 10.13039/501100011033,
and by the Department of Economic Development of the Government of Navarre through project
0011‐1365‐2021‐000072.

Forest monitoring plays a critical role in achieving sustainable forest management practices. The ability to identify ongoing harvesting activities is crucial for developing targeted strategies to maintain forest health. Traditional monitoring methods, which rely on field inventories, are often expensive and time-consuming. Remote sensing offers an interesting alternative, leveraging dense time series of satellite imagery and various algorithms for disturbance detection. This study presents and assesses a novel methodology for identifying forest harvesting practices (clear-cutting and thinning) using Continuous Change Detection and Classification (CCDC) algorithm, available in Google Earth Engine. The methodology comprises two steps. In the first step, performed at the pixel level, the CCDC algorithm was used to detect changes in the vegetation cover by considering Landsat 8 spectral bands, vegetation indices, and different combinations thereof. In the second step, two optimal thresholds were determined to identify forest harvesting practices based on the proportion of pixels flagged as change. This study was conducted in forest stands consisting of different conifer and broadleaf species. Accuracy was assessed using an independent set of photo-interpreted samples. The results indicated that the short-wave infrared 2 was the best individual band for forest harvesting practices identification, with an average F-score of 0.77 ± 0.06, overperforming vegetation indices. The combination of all spectral bands was the most effective to identify both clear-cuts and thinning (F-score = 0.85 ± 0.05). This combination was used to evaluate the accuracy of this approach for identifying harvesting practices over different tree species. Poplar (Populus sp.) had the highest identification rate (F-score = 0.99 ± 0.02), while black pine (Pinus nigra J.F. Arnold) stands had the lowest F-score (0.74 ± 0.05). These results highlight the ability to accurately identify forest harvesting practices even in heterogeneous forests with a high diversity of tree species using dense time series of Landsat imagery., Predoctoral scholarship funded by the Government of Navarre, and grants ReSAg (PID2019-107386RB-I00) and DAMAGE (PID2023-152885OB-I00) funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU.

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., This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (MINECO/FEDER-UE) through projects [CGL2016-75217-R and PID2019-107386RB-I00 / AEI / 10.13039/501100011033] and doctoral grant [BES-2017-080560].

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., This work was supported by the Spanish Ministry of Science and Innovation through project PID2019-107386RB-I00 / AEI / 10.13039/501100011033 and doctoral grant (BES-2017-080560).

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., This work was supported by the Spanish Ministry of Science and
Innovation and the European Regional Development Fund (MICINN/
FEDER-UE) through projects [CGL2016–75217-R and
PID2019–107386RB-I00 / AEI / 10.13039/501100011033] and
doctoral grant [BES-2017–080560]. Open access funding provided by the Public University of Navarre.

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., Crop monitoring is essential for different applications such as food security assurance, crop management, and the design and implementation of agricultural policies. Remote sensing provides information about biophysical properties of plants and soils and their spatial variability on large areas of the territory on a periodic basis. The launch of Sentinel-1 synthetic aperture radar (SAR) satellites in 2014 and 2016 made possible the acquisition of dense time series of images with good spatial resolution and temporal resolution even in cloud-covered areas. The main objective of this thesis is the evaluation of different methodologies for agricultural applications at the field scale using Sentinel-1 time series. First, a supervised crop classification methodology based on time signatures from Sentinel-1 images was proposed and implemented in a case study with 14 crop classes and a large dataset of agricultural fields. Secondly, the influence of acquisition geometry of Sentinel-1 images over wheat fields was assessed. The influence of the incidence angle on backscatter coefficients and terrain-flattened coefficients was evaluated, and an incidence angle normalization followed by an azimuthal anisotropy correction were applied to VV polarized time series. Thirdly, the backscatter attenuation produced by wheat canopy in VV polarization was evaluated, and a new wheat attenuation correction methodology named WATCOR was proposed. Finally, four different soil moisture (SM) estimation techniques based on Sentinel-1 time series were evaluated over wheat fields, proposing different methodological alternatives for their application at the field scale. Despite the complexity of estimating SM at the field scale solely with SAR data, acceptable estimations were obtained. The results of this thesis showed that the analysis and extraction of the information contained in SAR time series is useful for various agricultural applications, foreshadowing exciting future developments in this field., This work was supported by the Spanish Ministry and Competitiveness and the European Regional Development Fund (MINECO/FEDER) through a project (CGL2016-75217-R) and a pre-doctoral grant (BES-2017-080560). It was also supported by the Spanish Ministry of Science and Innovation under Project PID2019-107386RB-I00/ AEI/10.13039/501100011033., Programa de Doctorado en Ciencias y Tecnologías Industriales (RD 99/2011), Industria Zientzietako eta Teknologietako Doktoretza Programa (ED 99/2011)