Monitoring rainfed alfalfa growth in semiarid agrosystems using Sentinel-2 imagery

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)

Validation of a simplified model to generate multispectral synthetic images

A new procedure to assess the quality of topographic correction (TOC) algorithms applied to remote sensing imagery was previously proposed by the authors. This procedure was based on a model that simulated synthetic scenes, representing the radiance an optical sensor would receive from an area under some specific conditions. TOC algorithms were then applied to synthetic scenes and the resulting corrected scenes were compared with a horizontal synthetic scene free of topographic effect. This comparison enabled an objective and quantitative evaluation of TOC algorithms. This approach showed promising results but had some shortcomings that are addressed herein. First, the model, originally built to simulate only broadband panchromatic scenes, is extended to multispectral scenes in the visible, near infrared (NIR), and short wave infrared (SWIR) bands. Next, the model is validated by comparing synthetic scenes with four Satellite pour l'Observation de la Terre 5 (SPOT5) real scenes acquired on different dates and different test areas along the Pyrenees mountain range (Spain). The results obtained show a successful simulation of all the spectral bands. Therefore, the model is deemed accurate enough for its purpose of evaluating TOC algorithms.The authors gratefully acknowledge the financial support provided by the Public University of Navarre (UPNA). Part of the research presented in this paper is funded by the Spanish Ministry of Economy and Competitiveness in the frame of the ESP2013-48458-C4-2-P project

Multitemporal evaluation of topographic correction methods using multispectral synthetic images

Revista oficial de la Asociación Española de Teledetección[EN] This paper presents a multitemporal evaluation of topographic correction (TOC) methods based on synthetically generated images in order to evaluate the influence of solar angles on the performance of TOC methods. These synthetic images represent the radiance an optical sensor would receive for different periods of the year considering the real topography (SR image), and considering the relief completely horizontal (SH image). The comparison between the corrected image obtained applying a TOC method to a SR image and the SH image of the same area, i.e. considered the ideal correction, allows assessing the performance of each TOC algorithm, quantitatively measured through the Structural Similarity Index (SSIM).[ES] En este trabajo se presentan los resultados de la evaluación multitemporal de varios métodos de corrección topográfica (TOC), cuya bondad se determina de forma cuantitativa mediante el uso de imágenes sintéticas multiespectrales simuladas para diferentes fechas de adquisición a lo largo del año. Para cada fecha se generan dos imágenes sintéticas, una considerando el relieve real (imagen SR), y otra el relieve horizontal (imagen SH). Las imágenes SR se corrigen utilizando distintos TOC y estas imágenes corregidas se comparan con la corrección ideal (imagen SH) mediante el índice de similitud estructural (SSIM). Los valores de SSIM nos permiten evaluar la eficacia de cada corrección para distintas fechas, es decir, para distintos ángulos de elevación solar.Sola, I.; González-Audícana, M.; Álvarez-Mozos, J.; Torres, J. (2014). Evaluación multitemporal de métodos de corrección topográfica mediante el uso de imágenes sintéticas multiespectrales. Revista de Teledetección. (41):71-78. doi:10.4995/raet.2014.2246.SWORD717841Baraldi, A., Gironda, M., & Simonetti, D. (2010). Operational Two-Stage Stratified Topographic Correction of Spaceborne Multispectral Imagery Employing an Automatic Spectral-Rule-Based Decision-Tree Preliminary Classifier. IEEE Transactions on Geoscience and Remote Sensing, 48(1), 112-146. doi:10.1109/tgrs.2009.2028017Civco, D.L. 1989. Topographic Normalization of Landsat Thematic Mapper Digital Imagery. Photogramm. Eng. Remote S., 55: 1303-1309.Dumortier, D. 1998. The satellight model of turbidity variations in Europe. Report for the 6th Satel-Light meeting. Freiburg, Germany.Law, K.H., Nichol, J. 2004. Topographic correction for differential illumination effects on IKONOS satellite imagery. Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci., pp. 641-646.Page, J. 1996. Algorithms for the Satellight programme. Technical Report for the 2nd SATEL-LIGHT meeting. June, 1996, Bergen, Norway.Smith, J.A., Lin, T.L., Ranson, K.J. 1980. The Lambertian Assumption and Landsat Data. Photogrammetric Engineering & Remote Sensing, 46(9): 1183-1189Teillet, P. M., Guindon, B., & Goodenough, D. G. (1982). On the Slope-Aspect Correction of Multispectral Scanner Data. Canadian Journal of Remote Sensing, 8(2), 84-106. doi:10.1080/07038992.1982.10855028Twele, A., Kappas, M., Lauer, J., Erasmi, S. 2006. The effect of stratified topographic correction on land cover classificacion in tropical mountainous regions ISPRS Comm. VII Symp., 8-11 May, Enschede, The Netherlands, pp. 432-437

The added value of stratified topographic correction of multispectral images

Satellite images in mountainous areas are strongly affected by topography. Different studies demonstrated that the results of semi-empirical topographic correction algorithms improved when a stratification of land covers was carried out first. However, differences in the stratification strategies proposed and also in the evaluation of the results obtained make it unclear how to implement them. The objective of this study was to compare different stratification strategies with a non-stratified approach using several evaluation criteria. For that purpose, Statistic-Empirical and Sun-Canopy-Sensor + C algorithms were applied and six different stratification approaches, based on vegetation indices and land cover maps, were implemented and compared with the non-stratified traditional option. Overall, this study demonstrates that for this particular case study the six stratification approaches can give results similar to applying a traditional topographic correction with no previous stratification. Therefore, the non-stratified correction approach could potentially aid in removing the topographic effect, because it does not require any ancillary information and it is easier to implement in automatic image processing chains. The findings also suggest that the Statistic-Empirical method performs slightly better than the Sun-Canopy-Sensor + C correction, regardless of the stratification approach. In any case, further research is necessary to evaluate other stratification strategies and confirm these results.The authors gratefully acknowledge the financial support provided by the Public University of Navarre (UPNA). Part of the research presented in this paper is funded by the Spanish Ministry of Economy and Competitiveness in the frame of the ESP2013-48458-C4-2-P project

A strategy for the verification of CAP declarations using Sentinel-2 images in Navarre

[ES] En junio de 2018, la Comisión Europea aprobó una modificación de la Política Agraria Común (PAC) que, entre otros aspectos, plantea el uso de imágenes del programa Copernicus para la verificar que las declaraciones presentadas por los agricultores son correctas. En los últimos años distintas iniciativas investigadoras han tratado de desarrollar herramientas operativas con este fin, entre estas se encuentra el proyecto Interreg-POCTEFA PyrenEOS. En este artículo se expone la estrategia metodológica propuesta en el proyecto PyrenEOS, que se basa en la identificación del cultivo más probable utilizando el algoritmo Random Forests. Como elemento diferenciador, se propone seleccionar la muestra de entrenamiento a partir de una selección de las declaraciones PAC según su NDVI. Además, se definen una serie de reglas para determinar el grado de incertidumbre en la clasificación y los criterios para categorizar cada recinto del mapa de verificación según un código de colores a modo de semáforo, en el que el verde indica recintos con declaración correcta, el rojo recintos con declaración dudosa y el naranja recintos con una incertidumbre alta en la clasificación. Esta estrategia de verificación se aplica a dos Comarcas Agrarias de Navarra, en una campaña agrícola para la que se contó con inspecciones de campo de aproximadamente el 7% de los recintos declarados. Los resultados de esta validación, con fiabilidades globales en la clasificación próximas al 80% cuando se considera el cultivo más probable predicho por el clasificador y al 90% cuando se consideran los dos cultivos más probables, ponen de manifiesto que es posible identificar los recintos correctamente declarados (recintos verdes) con una tasa de error inferior al 1%. Los recintos naranjas y rojos, que requerirán del análisis y juicio posterior de técnicos de inspección, suponen un porcentaje reducido de las declaraciones (~6% de los recintos) y concentran la mayoría de las declaraciones incorrectas.[EN] In June 2018, the European Commission approved a modification of the Common Agricultural Policy (CAP) that, among other measures, proposed the use of Copernicus data for the verification process of farmers’ declarations. In recent years, several research efforts have been conducted to develop operational tools to accomplish this aim, among this the Interreg-POCTEFA PyrenEOS project. This article describes the methodological strategy proposed in the PyrenEOS project, which is based on the identification of the most probable crop using the Random Forests algorithm. Originally, the strategy builds a training sample from the CAP declarations file based on their NDVI time series. In addition, a series of rules are proposed to establish the level of uncertainty in the classification, and the criteria used to represent each parcel in the verification map with a simple colour coding (traffic light), where green represents correctly declared parcels, red indicates that the declaration is dubious, and orange corresponds to parcels with a high classification uncertainty. This verification strategy has been applied to two Agricultural Regions of Navarre, during an agricultural campaign where valuable field inspections were available, with a sampling intensity of 7% of the declared parcels. The results obtained, report overall accuracies close to 80% when the most probable crop was considered, and 90% when the two most probable crops were considered. This proves it is possible to identify correctly declared parcels (green parcels) with an error below 1%. Orange and red parcels should be considered for further analysis and inspection by technicians from the paying agencies, though they represent a small percentage of declarations (~6% of parcels), and include most of the wrong declarations.Este trabajo se ha financiado con el proyecto PyrenEOS EFA 048/15, cofinanciado al 65% por el Fondo Europeo de Desarrollo Regional (FEDER) a través del programa Interreg V-A España-Francia-Andorra (POCTEFA 2014-2020). Los autores agradecen al Servicio del Organismo Pagador del Departamento de Desarrollo Rural y Medio Ambiente del Gobierno de Navarra la cesión de los ficheros vectoriales de declaraciones e inspecciones PAC utilizadas en el contexto de este trabajo.González-Audícana, M.; López, S.; Sola, I.; Álvarez-Mozos, J. (2020). Estrategia para la verificación de declaraciones PAC a partir de imágenes Sentinel-2 en Navarra. Revista de Teledetección. 0(56):69-88. https://doi.org/10.4995/raet.2020.14128OJS698805