On the added value of quad-pol data in a multi-temporal crop classification framework based on RADARSAT-2 imagery

Polarimetric SAR images are a rich data source for crop mapping. However, quad-pol sensors have some limitations due to their complexity, increased data rate, and reduced coverage and revisit time. The main objective of this study was to evaluate the added value of quad-pol data in a multi-temporal crop classification framework based on SAR imagery. With this aim, three RADARSAT-2 scenes were acquired between May and June 2010. Once we analyzed the separability and the descriptive analysis of the features, an object-based supervised classification was performed using the Random Forests classification algorithm. Classification results obtained with dual-pol (VV-VH) data as input were compared to those using quad-pol data in different polarization bases (linear H-V, circular, and linear 45º), and also to configurations where several polarimetric features (Pauli and Cloude–Pottier decomposition features and co-pol coherence and phase difference) were added. Dual-pol data obtained satisfactory results, equal to those obtained with quad-pol data (in H-V basis) in terms of overall accuracy (0.79) and Kappa values (0.69). Quad-pol data in circular and linear 45º bases resulted in lower accuracies. The inclusion of polarimetric features, particularly co-pol coherence and phase difference, resulted in enhanced classification accuracies with an overall accuracy of 0.86 and Kappa of 0.79 in the best case, when all the polarimetric features were added. Improvements were also observed in the identification of some particular crops, but major crops like cereals, rapeseed, and sunflower already achieved a satisfactory accuracy with the VV-VH dual-pol configuration and obtained only minor improvements. Therefore, it can be concluded that C-band VV-VH dual-pol data is almost ready to be used operationally for crop mapping as long as at least three acquisitions in dates reflecting key growth stages representing typical phenology differences of the present crops are available. In the near future, issues regarding the classification of crops with small field sizes and heterogeneous cover (i.e., fallow and grasslands) need to be tackled to make this application fully operational

A diachronic analysis of a changing landscape on the Duero river borderlands of Spain and Portugal combining remote sensing and ethnographic approaches

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

Crop classification based on temporal signatures of Sentinel-1 observations over Navarre province, Spain

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.This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (MINECO/FEDER-UE) through a project (CGL2016-75217-R) and a grant (BES-2017-080560). It was also partly founded by project PyrenEOS EFA 048/15, that has been 65% cofinanced by the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra programme (POCTEFA 2014-2020)

Land use and land cover classification and change analysis in the area surrounding the Manglares Churute Ecological Reserve (Ecuador) using Sentinel-1 time series

La gestión de las áreas naturales protegidas frecuentemente obvia la importancia que tiene el territorio que rodea el perímetro del espacio protegido (zona de amortiguación). Estas zonas pueden ser el origen de impactos que amenacen el estado de conservación de los ecosistemas protegidos. En este artículo se describe un caso de estudio centrado en la Reserva Ecológica Manglares Churute (REMCh) de Ecuador, en el que se utilizó una serie temporal de imágenes Sentinel-1 para clasificar los usos y cubiertas del suelo y para analizar los cambios ocurridos en el periodo 2015-2018. Tras procesar la serie de imágenes y delinear el conjunto de zonas de entrenamiento sobre los principales usos y cubiertas se implementó un algoritmo de clasificación Random Forests (RF), cuyos parámetros fueron optimizados mediante una validación cruzada con el conjunto de datos de entrenamiento (70% de la verdad campo). El 30% restante se utilizó para validar la clasificación realizada, logrando una fiabilidad global del 84%, un coeficiente Kappa de 0,8 y unas métricas de rendimiento por clase satisfactorias para los principales cultivos y usos del suelo. Los resultados fueron peores para las clases más heterogéneas y minoritarias, no obstante, se considera que la clasificación fue lo suficientemente precisa para realizar el análisis de cambios perseguido. Entre 2015 y 2018 se constató un aumento en la superficie destinada a usos intensivos como el cultivo de camarón blanco y la caña de azúcar, en detrimento de otros cultivos tradicionales como el arroz o el banano. Aunque estos cambios se produjeron en las zonas que rodean al área natural protegida, pueden causar un deterioro de la calidad del agua debido al uso de fertilizantes y pesticidas, por tanto, se recomienda prestar atención a estas zonas de amortiguamiento a la hora de diseñar políticas e instrumentos adecuados de protección medioambiental.Management practices adopted in protected natural areas often ignore the relevance of the territory surrounding the actual protected land (buffer area). These areas can be the source of impacts that threaten the protected ecosystems. This paper reports a case study where a time series of Sentinel-1 imagery was used to classify the land-use/land-cover and to evaluate its change between 2015 and 2018 in the buffer area around the Manglares Churute Ecological Reserve (REMCh) in Ecuador. Sentinel-1 scenes were processed and ground-truth data were collected consisting of samples of the main land-use/land-cover classes in the region. Then, a Random Forests (RF) classification algorithm was built and optimized, following a five-fold cross validation scheme using the training dataset (70% of the ground truth). The remaining 30% was used for validation, achieving an Overall Accuracy of 84%, a Kappa coefficient of 0.8 and successful class performance metrics for the main crops and land use classes. Results were poorer for heterogeneous and minor classes, nevertheless the performance of the classification was deemed sufficient for the targeted change analysis. Between 2015 and 2018, an increase in the area covered by intensive land uses was evidenced, such as shrimp farms and sugarcane, which replaced traditional crops (mainly rice and banana). Even though such changes only affected the land area around the natural reserve, they might affect its water quality due to the use of fertilizers and pesticides that easily. Therefore, it is recommended that these buffer areas around natural protected areas be taken into account when designing adequate environmental protection measures and polices. © 2020, Universidad Politecnica de Valencia.. All rights reserved.Diana Vélez agradece la financiación recibida por la Fundación Carolina para la realización del Máster Universitario en SIG y Teledetección de la Universidad Pública de Navarra durante el curso 2018-2019

Influence of surface roughness sample size for C-band SAR backscatter applications on agricultural soils

Soil surface roughness determines the backscatter coefficient observed by radar sensors. The objective of this letter was to determine the surface roughness sample size required in synthetic aperture radar applications and to provide some guidelines on roughness characterization in agricultural soils for these applications. With this aim, a data set consisting of ten ENVISAT/ASAR observations acquired coinciding with soil moisture and surface roughness surveys has been processed. The analysis consisted of: 1) assessing the accuracies of roughness parameters s and l depending on the number of 1-m-long profiles measured per field; 2) computing the correlation of field average roughness parameters with backscatter observations; and 3) evaluating the goodness of fit of three widely used backscatter models, i.e., integral equation model (IEM), geometrical optics model (GOM), and Oh model. The results obtained illustrate a different behavior of the two roughness parameters. A minimum of 10-15 profiles can be considered sufficient for an accurate determination of s, while 20 profiles might still be not enough for accurately estimating l. The correlation analysis revealed a clear sensitivity of backscatter to surface roughness. For sample sizes > 15 profiles, R values were as high as 0.6 for s and similar to 0.35 for l, while for smaller sample sizes R values dropped significantly. Similar results were obtained when applying the backscatter models, with enhanced model precision for larger sample sizes. However, IEM and GOM results were poorer than those obtained with the Oh model and more affected by lower sample sizes, probably due to larger uncertainly of l