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

Irrigated grassland monitoring using a time series of terraSAR-X and COSMO-skyMed X-Band SAR Data

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOSInternational audienceThe objective of this study was to analyze the sensitivity of radar signals in the X-band in irrigated grassland conditions. The backscattered radar signals were analyzed according to soil moisture and vegetation parameters using linear regression models. A time series of radar (TerraSAR-X and COSMO-SkyMed) and optical (SPOT and LANDSAT) images was acquired at a high temporal frequency in 2013 over a small agricultural region in southeastern France. Ground measurements were conducted simultaneously with the satellite data acquisitions during several grassland growing cycles to monitor the evolution of the soil and vegetation characteristics. The comparison between the Normalized Difference Vegetation Index (NDVI) computed from optical images and the in situ Leaf Area Index (LAI) showed a logarithmic relationship with a greater scattering for the dates corresponding to vegetation well developed before the harvest. The correlation between the NDVI and the vegetation parameters (LAI, vegetation height, biomass, and vegetation water content) was high at the beginning of the growth cycle. This correlation became insensitive at a certain threshold corresponding to high vegetation (LAI ~2.5 m2/m2). Results showed that the radar signal depends on variations in soil moisture, with a higher sensitivity to soil moisture for biomass lower than 1 kg/m². HH and HV polarizations had approximately similar sensitivities to soil moisture. The penetration depth of the radar wave in the X-band was high, even for dense and high vegetation; flooded areas were visible in the images with higher detection potential in HH polarization than in HV polarization, even for vegetation heights reaching 1 m. Lower sensitivity was observed at the X-band between the radar signal and the vegetation parameters with very limited potential of the X-band to monitor grassland growth. These results showed that it is possible to track gravity irrigation and soil moisture variations from SAR X-band images acquired at high spatial resolution (an incidence angle near 30°)

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

Monitoring crops water needs at high spatio-temporal resolution by synergy of optical/thermal and radar observations

L'optimisation de la gestion de l'eau en agriculture est essentielle dans les zones semi-arides afin de préserver les ressources en eau qui sont déjà faibles et erratiques dues à des actions humaines et au changement climatique. Cette thèse vise à utiliser la synergie des observations de télédétection multispectrales (données radar, optiques et thermiques) pour un suivi à haute résolution spatio-temporelle des besoins en eau des cultures. Dans ce contexte, différentes approches utilisant divers capteurs (Landsat-7/8, Sentinel-1 et MODIS) ont été developpées pour apporter une information sur l'humidité du sol (SM) et le stress hydrique des cultures à une échelle spatio-temporelle pertinente pour la gestion de l'irrigation. Ce travail va parfaitement dans le sens des objectifs du projet REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) qui visent à estimer l'humidité du sol dans la zone racinaire (RZSM) afin d'optimiser la gestion de l'eau d'irrigation. Des approches innovantes et prometteuses sont mises en place pour estimer l'évapotranspiration (ET), RZSM, la température de surface du sol (LST) et le stress hydrique de la végétation à travers des indices de SM dérivés des observations multispectrales à haute résolution spatio-temporelle. Les méthodologies proposées reposent sur des méthodes basées sur l'imagerie, la modélisation du transfert radiatif et la modélisation du bilan hydrique et d'énergie et sont appliquées dans une région à climat semi-aride (centre du Maroc). Dans le cadre de ma thèse, trois axes ont été explorés. Dans le premier axe, un indice de RZSM dérivé de LST-Landsat est utilisé pour estimer l'ET sur des parcelles de blé et des sols nus. L'estimation par modélisation de ET a été explorée en utilisant l'équation de Penman-monteith modifiée obtenue en introduisant une relation empirique simple entre la résistance de surface (rc) et l'indice de RZSM. Ce dernier est estimé à partir de la température de surface (LST) dérivée de Landsat, combinée avec les températures extrêmes (en conditions humides et sèches) simulée par un modèle de bilan d'énergie de surface piloté par le forçage météorologique et la fraction de couverture végétale dérivée de Landsat. La méthode utilisée est calibrée et validée sur deux parcelles de blé situées dans la même zone près de Marrakech au Maroc. Dans l'axe suivant, une méthode permettant de récupérer la SM de la surface (0-5 cm) à une résolution spatiale et temporelle élevée est développée à partir d'une synergie entre données radar (Sentinel-1) et thermique (Landsat) et en utilisant un modèle de bilan d'énergie du sol. L'approche développée a été validée sur des parcelles agricoles en sol nu et elle donne une estimation précise de la SM avec une différence quadratique moyenne en comparant à la SM in situ, égale à 0,03 m3 m-3. Dans le dernier axe, une nouvelle méthode est développée pour désagréger la MODIS LST de 1 km à 100 m de résolution en intégrant le SM proche de la surface dérivée des données radar Sentinel-1 et l'indice de végétation optique dérivé des observations Landsat. Le nouvel algorithme, qui inclut la rétrodiffusion S-1 en tant qu'entrée dans la désagrégation, produit des résultats plus stables et robustes au cours de l'année sélectionnée. Dont, 3,35 °C était le RMSE le plus bas et 0,75 le coefficient de corrélation le plus élevé évalués en utilisant le nouvel algorithme.Optimizing water management in agriculture is essential over semi-arid areas in order to preserve water resources which are already low and erratic due to human actions and climate change. This thesis aims to use the synergy of multispectral remote sensing observations (radar, optical and thermal data) for high spatio-temporal resolution monitoring of crops water needs. In this context, different approaches using various sensors (Landsat-7/8, Sentinel-1 and MODIS) have been developed to provide information on the crop Soil Moisture (SM) and water stress at a spatio-temporal scale relevant to irrigation management. This work fits well the REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) project objectives, which aim to estimate the Root Zone Soil Moisture (RZSM) for optimizing the management of irrigation water. Innovative and promising approaches are set up to estimate evapotranspiration (ET), RZSM, land surface temperature (LST) and vegetation water stress through SM indices derived from multispectral observations with high spatio-temporal resolution. The proposed methodologies rely on image-based methods, radiative transfer modelling and water and energy balance modelling and are applied in a semi-arid climate region (central Morocco). In the frame of my PhD thesis, three axes have been investigated. In the first axis, a Landsat LST-derived RZSM index is used to estimate the ET over wheat parcels and bare soil. The ET modelling estimation is explored using a modified Penman-Monteith equation obtained by introducing a simple empirical relationship between surface resistance (rc) and a RZSM index. The later is estimated from Landsat-derived land surface temperature (LST) combined with the LST endmembers (in wet and dry conditions) simulated by a surface energy balance model driven by meteorological forcing and Landsat-derived fractional vegetation cover. The investigated method is calibrated and validated over two wheat parcels located in the same area near Marrakech City in Morocco. In the next axis, a method to retrieve near surface (0-5 cm) SM at high spatial and temporal resolution is developed from a synergy between radar (Sentinel-1) and thermal (Landsat) data and by using a soil energy balance model. The developed approach is validated over bare soil agricultural fields and gives an accurate estimates of near surface SM with a root mean square difference compared to in situ SM equal to 0.03 m3 m-3. In the final axis a new method is developed to disaggregate the 1 km resolution MODIS LST at 100 m resolution by integrating the near surface SM derived from Sentinel-1 radar data and the optical-vegetation index derived from Landsat observations. The new algorithm including the S-1 backscatter as input to the disaggregation, produces more stable and robust results during the selected year. Where, 3.35 °C and 0.75 were the lowest RMSE and the highest correlation coefficient assessed using the new algorithm

Multi-Annual Evaluation of Time Series of Sentinel-1 Interferometric Coherence as a Tool for Crop Monitoring

Interferometric coherence from SAR data is a tool used in a variety of Earth observation applications. In the context of crop monitoring, vegetation indices are commonly used to describe crop dynamics. The most frequently used vegetation indices based on radar data are constructed using the backscattered intensity at different polarimetric channels. As coherence is sensitive to the changes in the scene caused by vegetation and its evolution, it may potentially be used as an alternative tool in this context. The objective of this work is to evaluate the potential of using Sentinel-1 interferometric coherence for this purpose. The study area is an agricultural region in Sevilla, Spain, mainly covered by 18 different crops. Time series of different backscatter-based radar vegetation indices and the coherence amplitude for both VV and VH channels from Sentinel-1 were compared to the NDVI derived from Sentinel-2 imagery for a 5-year period, from 2017 to 2021. The correlations between the series were studied both during and outside the growing season of the crops. Additionally, the use of the ratio of the two coherences measured at both polarimetric channels was explored. The results show that the coherence is generally well correlated with the NDVI across all seasons. The ratio between coherences at each channel is a potential alternative to the separate channels when the analysis is not restricted to the growing season of the crop, as its year-long temporal evolution more closely resembles that of the NDVI. Coherence and backscatter can be used as complementary sources of information, as backscatter-based indices describe the evolution of certain crops better than coherence.This research work was supported by the the European Space Agency under Project SEOM-S14SCI-Land (SInCohMap), and by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development (Project PID2020-117303GB-C22)

Coupling SAR C-band and optical data for soil moisture and leaf area index retrieval over irrigated grasslands

International audienceThe objective of this study was to develop an approach for estimating soil moisture and vegetation parameters in irrigated grasslands by coupling C-band polarimetric Synthetic Aperture Radar (SAR) and optical data. A huge dataset of satellite images acquired from RADARSAT-2 and LANDSAT-7/8, and in situ measurements were used to assess the relevance of several inversion configurations. A neural network (NN) inversion technique was used. The approach for this study was to use RADARSAT-2 and LANDSAT-7/8 images to investigate the potential for the combined use of new data from the new SAR sensor SENTINEL-1 and the new optical sensors LANDSAT-8 and SENTINEL-2. First, the impact of SAR polarization (mono-, dual- and full-polarizations configurations) and the Normalized Difference Vegetation Index (NDVI) calculated from optical data for the estimation error of soil moisture and vegetation parameters was studied. Next, the effect of some polarimetric parameters (Shannon entropy and Pauli components) on the inversion technique was also analyzed. Finally, configurations using in situ measurements of the fraction of absorbed photosynthetically active radiation (FAPAR) and the fraction of green vegetation cover (FCover) were also tested.The results showed that HH polarization is the SAR polarization most relevant to soil moisture estimates. An RMSE for soil moisture estimates of approximately 6 vol.% was obtained even for dense grassland cover. The use of in situ FAPAR and FCover only improved the estimate of the leaf area index (LAI) with an RMSE of approximately 0.37 m²/m². The use of polarimetric parameters did not improve the estimate of soil moisture and vegetation parameters. Good results were obtained for the biomass (BIO) and vegetation water content (VWC) estimates for BIO and VWC values lower than 2 and 1.5 kg/m², respectively (RMSE is of 0.38 kg/m² for BIO and 0.32 kg/m² for VWC). In addition, a high under-estimate was observed for BIO and VWC higher than 2 and 1.5 kg/m², respectively (a bias of -0.65 kg/m² on BIO estimates and -0.49 kg/m² on VWC estimates). Finally, the estimation of vegetation height (VEH) was carried out with an RMSE of 13.45 cm

Retrieval of biophysical parameters from multi-sensoral remote sensing data, assimilated into the crop growth model CERES-Wheat

This study investigated the possibilities and constraints for an integrated use of a crop growth model (CERES-Wheat) and earth observation techniques. The assimilation of information derived from earth observation sensors into crop growth models enables regional applications and may also help to improve the profound knowledge of the different involved processes and interactions. Both techniques can contribute to improved use of resources, reduced crop production risks, minimised environmental degradation, and increased farm income. Up to now, crop growth modelling and remote sensing techniquices mostly have been used separately for the assessment of agricultural applications. Crop growth models have made valuable contributions to, e.g., yield forecasting or to management decision support systems. Likewise, remote sensing techniques were successfully utilized in classification of agricultural areas or in the quantification of vegetation characteristics at various spatial and temporal scales. Multisensoral remote sensing approaches for the quantification biophysical variables are rarely realized. Normally the fusion of the data sources is based on the use of one sensor for classification purposes and the other one for the extraction of the desired parameters, based on the map classified previously. Pixel-based fusions between multispectral and SAR data is seldom realised for the assessment of quantitative parameters. The integration of crop growth models and remote sensing techniques by assimilating remotely sensed parameters into the models, is also still an issue of research. Especially, the integration of, e.g., multi-sensor biophysical parameter time-series for the improvement of the model performance, might feature a high potential. The starting point of the presented study was the question, if it is possible to derive the values of important crop variables from various remote sensing data? For the retrieval of these quantitative parameters by the use of various multispectral remote sensing sensors, intercalibration issues between the different retrieved vegetation indices had to be taken into account, in order to assure the comparability. Features influencing the vegetation indices are, e.g., the sensor geometry (like viewing- and solar-angle), atmospherical conditions, topography and spatial or radiometric resolution. However, the factors taken into account within this study are the spectral characteristics of the different sensors, like band position, bandwidth and centre wavelengths, which are described by the relative spectral response functions. Due to different RSR functions of the sensor bands, measured spectral differences occur, because the sensors record different components of the reflectance’s spectra from the monitored targets. These are then also introduced into the derived vegetation indices. The chosen cross-calibration method, intercalibrated the assessed Normalized Difference Vegetation Index and the Weighted Difference Vegetation Index between the various sensor pairs by regression, based on simulated multispectral sensors. Differences between the various assessed remote sensing sensors decreased form around 7% to below 1%. The intercalibration also had a positive impact on the later biophysical retrieval performance, producing sounder retrieval results. For the retrieval of the biophysical parameters empirical and semi-empirical models were assessed. The results indicate that the semi-empirical CLAIR model outperforms the empirical approaches. Not only for the Leaf Area Index retrieval, but also in the cases of all other assessed parameters. Concerning the other remote sensing data type used, the SAR data, it was analysed what potential different polarizations and incidence angles have for the extraction of the quantitative parameters. It became obvious that especially high incidence angles, as provided by the satellite Envisat ASAR, produce sounder retrieval results than lower incidence angles, due to a smaller amount of received soil signal. In the context of the assessed polarizations, sound results for the VV polarization could only be achieved for the retrieval of fresh biomass and the plant water content. For the ASAR sensor modelling fresh biomass and LAI using the HV polarization or the dry biomass using the ratio (HH/HV) was appropriate. As roughness aspects also have an influence on the retrieval performance from biophysical parameters using SAR data, the impact of soil surface and vegetation roughness was additionally considered. Best results were achieved, when also considering roughness features, however due to the need of regional modelling it is more appropriate not to consider them. For the calibration and re-tuning of crop growth models information about important phenological events such as heading/flowering is rather important. After this stage reproductive growth begins, whereby the number of kernels per plant is often calculated from plant weight at flowering and kernel weight is calculated from time and temperature available for dry matter distribution. By the use of the SAR VV time-series this important stage could be successfully extracted. Further methods for pixel-based fused biophysical parameter estimations, using SAR and multispectral data were analysed. By this approach the different features, being monitored of the two systems, are combined for sounder parameter retrieval. The assessed method of combining the multi-sensoral information by linear regression did not bring sound results and was outperformed by single sensor use, only taking into account the multispectral information. Only for the parameter fresh biomass, modelling based on the NDIV and the ASAR ratio slightly outperformed the single sensor modelling approaches. The complex combined modelling by the use of the CLAIR and the Water Cloud Model featured no valid results. For the combination, by using the CLAIR model and multiple regression slight improvements, in contrast to the single multispectral sensor use, were achieved. Especially, during late phenological stages, the assessed VV information improved the modelling results, in comparison to only using the CLAIR model. All the findings could finally be successfully applied for regional estimations. Only the roughness features could not be applied, due to the fact, that it is hard to regionally assess this needed model input parameter. Regional parameter on the basis of remote sensing data, is the major advantage of this technique, due to the large spatial overview given. The second main question was, if it is possible to integrate the crop variables gained from multisensoral data into a crop growth model, increasing the final yield estimation accuracy. Thus far, beneficial linkages between both techniques have been often limited to land use classification via remote sensing for choosing the adequate model and quantification of crop growth and development curves using biophysical parameters derived from remote sensing images for model calibration. Only a few studies actually considered the potentials of remote sensing for model re-initialization of growth and development characteristics of a specific crop, as the here studied winter wheat. Overall, the integration of remotely sensed variables into the crop growth model CERES-Wheat led to an improved final yield estimation accuracy in comparison to an automatic input parameter setting. The assessed final yield bias for the automatic input parameter setting summed up to 6.6%. When re-initializing the most sensitive input parameters (sowing date and fertilizer application date) by the use of remotely sensed biophysical variables the biases ranged from 0.56% overestimation to 5.4% understimation, in dependence of the data series used for assimilation. Whereby, it was assessed that the combined dense data series, considering SAR and multispectral information, slightly outperformed the performance of the full multispectral data series. However, when analysing the assimilation of the multispectral data series in further detail, it became clear that the actually information from the phenological stage ripening declines the modelling performance and thus the final yield estimation accuracy. When neglecting the information from this phenological stage the reduced multispectral data series performed as sound as the dense data series containing SAR and multispectral information. Thus, when the appropriate phenological stages are monitored by multispectral data, additional SAR information does not lead to a model improvement. However, when important dates are not monitored by multispectral images, e.g., due to cloud coverage, the additionally considered SAR information was not able to appropriatly fill these important multispectral time gaps. They even had a more negeative influence on the modelling performance. Overall, the best results could be obtained by assimilating a multispectral data series, covering the crop development during the important phenological stages stem elongation and flowering (without ripening stage), into the CERES-Wheat model. Finally, the integration of remote sensing data in the point-based crop growth model allowed it‘s spatial application for prediction of wheat production at a more regional scale. This approach also outperformed another evaluated method of direct multi-sensoral regional yield estimation. This study has demonstrated that biophysical parameters can be retrieved from remote sensing data and led, when assimilated into a crop growth model, to an improved final yield estimation. However, overall the SAR information did not really have a significant positive effect on the multi-sensoral biophysical parameter retrieval and on the later assimilation process. Thus, overall SAR information should only be considered, when multispectral data acquisitions are tremendously hampered by cloud coverage. The assessed assimilation of remote sensing information into a crop growth model had a positive effect on the final yield estimation performance. The analysed method, combining remote sensing and crop growth model techniques, was succsessfully demonstrated and will gain even more importance in the future for, e.g., decision support systems fine-tuning fertilizer regimes and thus contributing to more environmentally sound and sustained agricultural production

Parameterizing the modified water cloud model to improve soil moisture data retrieval using vegetation models

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Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought