Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets

Accurate estimation and monitoring of rice phenology is necessary for the management and yield prediction of rice. The radar backscattering coefficient, one of the most direct and accessible parameters has been proved to be capable of retrieving rice growth parameters. This paper aims to investigate the possibility of monitoring the rice phenology (i.e., transplanting, vegetative, reproductive, and maturity) using the backscattering coefficients or their simple combinations of multi-temporal RADARSAT-2 datasets only. Four RADARSAT-2 datasets were analyzed at 30 sample plots in Meishan City, Sichuan Province, China. By exploiting the relationships of the backscattering coefficients and their combinations versus the phenology of rice, HH/VV, VV/VH, and HH/VH ratios were found to have the greatest potential for phenology monitoring. A decision tree classifier was applied to distinguish the four phenological phases, and the classifier was effective. The validation of the classifier indicated an overall accuracy level of 86.2%. Most of the errors occurred in the vegetative and reproductive phases. The corresponding errors were 21.4% and 16.7%, respectively

Cartografía de alta resolución de la cubierta del suelo y clasificación de los cultivos en la cuenca del Loukkos (norte de Marruecos): Un enfoque que utiliza las series temporales de SAR Sentinel-1

[EN] Remote  sensing  has  become  more  and  more  a  reliable  tool  for  mapping  land  cover  and  monitoring  cropland. Much of the work done in this field uses optical remote sensing data. In Morocco, active remote sensing data remain under-exploited despite their importance in monitoring spatial and temporal dynamics of land cover and crops even during cloudy weather. This study aims to explore the potential of C-band Sentinel-1 data in the production of a high-resolution land cover mapping and crop classification within the irrigated Loukkos watershed agricultural landscape in northern Morocco. The work was achieved by using 33 dual-polarized images in vertical-vertical  (VV)  and  vertical-horizontal  (VH)  polarizations.  The  images  were  acquired  in  ascending  orbits  between  April 16 and October 25, 2020, with the purpose to track the backscattering behavior of the main crops and other land  cover  classes  in  the  study  area.  The  results  showed  that  the  backscatter  increased  with  the  phenological  development  of  the  monitored  crops  (rice,  watermelon,  peanuts,  and  winter  crops),  strongly  for  the  VH  and  VV  bands, and slightly for the VH/VV ratio. The other classes (water, built-up, forest, fruit trees, permanent vegetation, greenhouses, and bare lands) did not show significant variation during this period. Based on the backscattering analysis and the field data, a supervised classification was carried out, using the Random Forest Classifier (RF) algorithm.  Results  showed  that  radiometric  characteristics  and  6  days  time  resolution  covered  by  Sentinel-1  constellation gave a high classification accuracy by dual-polarization with Radar Ratio (VH/VV) or Radar Vegetation Index and textural features (between 74.07% and 75.19%). Accordingly, this study proves that the Sentinel-1 data provide useful information and a high potential for multi-temporal analyses of crop monitoring, and reliable land cover mapping which could be a practical source of information for various purposes in order to undertake food security issues.[ES] La teledetección se ha convertido en una herramienta cada vez más fiable para cartografiar la cubierta vegetal y controlar las tierras de cultivo. Gran parte de los trabajos realizados en este campo utilizan datos ópticos de teledetección. Además, en Marruecos, los datos de teledetección activa siguen estando infrautilizados, a pesar de su importancia para el seguimiento de la dinámica espacial y temporal de la cubierta vegetal y de los cultivos, incluso con tiempo nublado. Este estudio tiene como objetivo explorar el potencial de los datos de la banda C de Sentinel-1 en la producción de una cartografía de alta resolución de la cubierta del suelo y la clasificación de los cultivos dentro del paisaje agrícola de la cuenca del Loukkos de regadío en el norte de Marruecos. Este trabajo se ha realizado utilizando 33 imágenes de doble polarización vertical-vertical (VV) y vertical-horizontal (VH). Las imágenes fueron adquiridas en órbitas ascendentes entre el 16 de abril y el 25 de octubre de 2020, con el propósito de rastrear el comportamiento de retrodispersión de los principales cultivos y otras clases de cobertura del suelo en el área de estudio. Los gráficos obtenidos muestran que la retrodispersión aumenta con el desarrollo fenológico de los tres cultivos monitorizados (arroz, sandía, cacahuetes, cultivos de invierno), fuertemente para las bandas VH y VV, y ligeramente para el ratio VH/VV. Las otras clases (agua, edificado, bosque, árboles frutales, vegetación permanente, invernaderos y tierras desnudas) no muestran una variación significativa durante este periodo. A partir del análisis de retrodispersión y de los datos de campo, se llevó a cabo una clasificación supervisada, utilizando el  algoritmo  Random Forest Classifier (RF). Los resultados muestran que las características radiométricas y la resolución temporal para los 6 días cubiertos por la constelación Sentinel-1 dan una alta precisión de clasificación por polarización dual con Ratio de Radar (VH/VV) o Índice de Vegetación de Radar y características de la textura (entre  74,07%  y  75,17%).  En  consecuencia,  este  estudio  demuestra  que  los  datos  de  Sentinel-1  proporcionan  información útil y un alto potencial para los análisis multitemporales de seguimiento de los cultivos, así como una cartografía fiable de la cubierta terrestre que debería ser una fuente de información práctica para para varios propósitos a fin de acometer cuestiones de seguridad alimentaria.Nizar, EM.; Wahbi, M.; Ait Kazzi, M.; Yazidi Alaoui, O.; Boulaassal, H.; Maatouk, M.; Zaghloul, MN.... (2022). High Resolution Land Cover Mapping and Crop Classification in the Loukkos Watershed (Northern Morocco): An Approach Using SAR Sentinel-1 Time Series. Revista de Teledetección. (60):47-69. https://doi.org/10.4995/raet.2022.17426OJS47696

Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data

Sentinel-1 Synthetic Aperture Radar (SAR) data have provided an unprecedented opportunity for crop monitoring due to its high revisit frequency and wide spatial coverage. The dual-pol (VV-VH) Sentinel-1 SAR data are being utilized for the European Common Agricultural Policy (CAP) as well as for other national projects, which are providing Sentinel derived information to support crop monitoring networks. Among the Earth observation products identified for agriculture monitoring, indicators of vegetation status are deemed critical by end-user communities. In literature, several experiments usually utilize the backscatter intensities to characterize crops. In this study, we have jointly utilized the scattering information in terms of the degree of polarization and the eigenvalue spectrum to derive a new vegetation index from dual-pol (DpRVI) SAR data. We assess the utility of this index as an indicator of plant growth dynamics for canola, soybean, and wheat, over a test site in Canada. A temporal analysis of DpRVI with crop biophysical variables (viz., Plant Area Index (PAI), Vegetation Water Content (VWC), and dry biomass (DB)) at different phenological stages confirms its trend with plant growth dynamics. For each crop type, the DpRVI is compared with the cross and co-pol ratio (σVH0/σVV0) and dual-pol Radar Vegetation Index (RVI = 4σVH0/(σVV0 + σVH0)), Polarimetric Radar Vegetation Index (PRVI), and the Dual Polarization SAR Vegetation Index (DPSVI). Statistical analysis with biophysical variables shows that the DpRVI outperformed the other four vegetation indices, yielding significant correlations for all three crops. Correlations between DpRVI and biophysical variables are highest for canola, with coefficients of determination (R2) of 0.79 (PAI), 0.82 (VWC), and 0.75 (DB). DpRVI had a moderate correlation (R2≳ 0.6) with the biophysical parameters of wheat and soybean. Good retrieval accuracies of crop biophysical parameters are also observed for all three crops.This work was supported by the Spanish Ministry of Science, Innovation and Universities, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P

Crop Phenology Estimation in Rice Fields Using Sentinel-1 GRD SAR Data and Machine Learning-Aided Particle Filtering Approach

Monitoring crop phenology is essential for managing field disasters, protecting the environment, and making decisions about agricultural productivity. Because of its high timeliness, high resolution, great penetration, and sensitivity to specific structural elements, synthetic aperture radar (SAR) is a valuable technique for crop phenology estimation. Particle filtering (PF) belongs to the family of dynamical approach and has the ability to predict crop phenology with SAR data in real time. The observation equation is a key factor affecting the accuracy of particle filtering estimation and depends on fitting. Compared to the common polynomial fitting (POLY), machine learning methods can automatically learn features and handle complex data structures, offering greater flexibility and generalization capabilities. Therefore, incorporating two ensemble learning algorithms consisting of support vector machine regression (SVR), random forest regression (RFR), respectively, we proposed two machine learning-aided particle filtering approaches (PF-SVR, PF-RFR) to estimate crop phenology. One year of time-series Sentinel-1 GRD SAR data in 2017 covering rice fields in Sevilla region in Spain was used for establishing the observation and prediction equations, and the other year of data in 2018 was used for validating the prediction accuracy of PF methods. Four polarization features (VV, VH, VH/VV and Radar Vegetation Index (RVI)) were exploited as the observations in modeling. Experimental results reveals that the machine learning-aided methods are superior than the PF-POLY method. The PF-SVR exhibited better performance than the PF-RFR and PF-POLY methods. The optimal outcome from PF-SVR yielded a root-mean-square error (RMSE) of 7.79, compared to 7.94 for PF-RFR and 9.1 for PF-POLY. Moreover, the results suggest that the RVI is generally more sensitive than other features to crop phenology and the performance of polarization features presented consistent among all methods, i.e., RVI＞VV＞VH＞VH/VV. Our findings offer valuable references for real-time crop phenology monitoring with SAR data

The integration of freely available medium resolution optical sensors with Synthetic Aperture Radar (SAR) imagery capabilities for American bramble (Rubus cuneifolius) invasion detection and mapping.

Doctoral Degree. University of KwaZulu- Natal, Pietermaritzburg.The emergence of American bramble (Rubus cuneifolius) across South Africa has caused severe ecological and economic damage. To date, most of the efforts to mitigate its effects have been largely unsuccessful due to its prolific growth and widespread distribution. Accurate and timeous detection and mapping of Bramble is therefore critical to the development of effective eradication management plans. Hence, this study sought to determine the potential of freely available, new generation medium spatial resolution satellite imagery for the detection and mapping of American Bramble infestations within the UNESCO world heritage site of the uKhahlamba Drakensberg Park (UDP). The first part of the thesis determined the potential of conventional freely available remote sensing imagery for the detection and mapping of Bramble. Utilizing the Support Vector Machine (SVM) learning algorithm, it was established that Bramble could be detected with limited users (45%) and reasonable producers (80%) accuracies. Much of the confusion occurred between the grassland land cover class and Bramble. The second part of the study focused on fusing the new age optical imagery and Synthetic Aperture Radar (SAR) imagery for Bramble detection and mapping. The synergistic potential of fused imagery was evaluated using multiclass SVM classification algorithm. Feature level image fusion of optical imagery and SAR resulted in an overall classification accuracy of 76%, with increased users and producers’ accuracies for Bramble. These positive results offered an opportunity to explore the polarization variables associated with SAR imagery for improved classification accuracies. The final section of the study dwelt on the use of Vegetation Indices (VIs) derived from new age satellite imagery, in concert with SAR to improve Bramble classification accuracies. Whereas improvement in classification accuracies were minimal, the potential of stand-alone VIs to detect and map Bramble (80%) was noteworthy. Lastly, dual-polarized SAR was fused with new age optical imagery to determine the synergistic potential of dual-polarized SAR to increase Bramble mapping accuracies. Results indicated a marked increase in overall Bramble classification accuracy (85%), suggesting improved potential of dual-polarized SAR and optical imagery in invasive species detection and mapping. Overall, this study provides sufficient evidence of the complimentary and synergistic potential of active and passive remote sensing imagery for invasive alien species detection and mapping. Results of this study are important for supporting contemporary decision making relating to invasive species management and eradication in order to safeguard ecological biodiversity and pristine status of nationally protected areas

Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data

In the last decade, suboptimal Bayesian filtering (BF) techniques, such as Extended Kalman Filtering (EKF) and Particle Filtering (PF), have led to great interest for crop phenology monitoring with Synthetic Aperture Radar (SAR) data. In this study, a novel approach, based on the Grid-Based Filter (GBF), is proposed to estimate crop phenology. Here, phenological scales, which consist of a finite number of discrete stages, represent the one-dimensional state space, and hence GBF provides the optimal phenology estimates. Accordingly, contrarily to literature studies based on EKF and PF, no constraints are imposed on the models and the statistical distributions involved. The prediction model is defined by the transition matrix, while Kernel Density Estimation (KDE) is employed to define the observation model. The approach is applied on dense time series of dual-polarization Sentinel-1 (S1) SAR images, collected in four different years, to estimate the BBCH stages of rice crops. Results show that 0.94 ≤ R2 ≤ 0.98, 5.37 ≤ RMSE ≤ 7.9 and 20 ≤ MAE ≤ 33.This research was funded in part by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development (EFRD) under Projects TEC2017-85244-C2-1-P and PID2020-117303GB-C22, and in part by the University of Alicante (ref. VIGROB-114)

Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles

Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition’s geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with R2CV = 0.67 and RMSECV = 0.88 m2 m−2. The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloud-prone agri-environments.EEA Hilario AscasubiFil: Caballero, Gabriel. Technological University of Uruguay (UTEC). Agri-Environmental Engineering; UruguayFil: Caballero, Gabriel. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Casella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Sanchez Angonova, Paolo Andres. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Orden, Luciano. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Orden, Luciano. Universidad Miguel Hernández. Centro de Investigación e Innovación Agroalimentaria y Agroambiental. GIAAMA Reseach Group; EspañaFil: Berger, Katja. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Berger, Katja. Mantle Labs GmbH; AustriaFil: Verrelst, Jochem. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Delegido, Jesús. Universidad de Valencia. Image Processing Laboratory (IPL); Españ

Rice Phenology Monitoring by means of SAR Polarimetry at X-Band

The feasibility of retrieving the phenological stage of rice fields at a particular date by employing coherent copolar dual-pol X-band radar images acquired by the TerraSAR-X sensor has been investigated in this paper. A set of polarimetric observables that can be derived from this data type has been studied by using a time series of images gathered during the whole cultivation period of rice. Among the analyzed parameters, besides backscattering coefficients and ratios, we have observed clear signatures in the correlation (in magnitude and phase) between channels in both the linear and Pauli bases, as well as in parameters provided by target decomposition techniques, like entropy and alpha from the eigenvector decomposition. A new model-based decomposition providing estimates of a random volume component plus a polarized contribution has been proposed and employed in interpreting the radar response of rice. By exploiting the signatures of these observables in terms of the phenology of rice, a simple approach to estimate the phenological stage from a single pass has been devised. This approach has been tested with the available data acquired over a site in Spain, where rice is cultivated, ensuring ground is flooded for the whole cultivation cycle, and sowing is carried out by randomly spreading the seeds on the flooded ground. Results are in good agreement with the available ground measurements despite some limitations that exist due to the reduced swath coverage of the dual-pol HHVV mode and the high noise floor of the TerraSAR-X system.This work was supported by the Spanish Ministry of Science and Innovation (MICINN) and EU FEDER under Projects TEC2008-06764-C02-02 and TEC2011-28201-C02-02, by the National Program of Human Resources Movability under Grant PR2009-0364, by the University of Alicante under Project GRE08J01, and by Generalitat Valenciana under Projects GV/2009/079 and ACOMP/2010/082

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices