Calculation of Differential Propagation Constant Determined by Plant Morphology Using Polarimetric Measurement

The morphology of vegetation greatly impacts propagation of polarized electromagnetic wave. In order to validate this phenomenon, the mathematical relation between the differential propagation constant of forest vegetation and of its polarized echo is quantitatively derived by using backscattering power profile. The fluctuation of differential propagation constant with frequency is analyzed by combining the morphological characteristics of vegetation. The accurate copolarized data of 3–10 GHz frequency-domain of small trees are obtained by indoor wideband polarimetric measurement system. The results show that morphological characteristics of vegetation at different frequencies can be obtained by the differential propagation constant of polarized electromagnetic wave. At low frequencies, the plants with structural features presented oriented distribution. However, the plants show random distribution of the echoes at higher frequencies, which is mainly from the canopy. The research provides important information to choose the coherence models employed in the parameters retrieval of vegetations

NOCTUA: potenzialità innovative per l'Osservazione della Terra

SAR systems have notable and interesting applications in Earth Observation, in particular in environmental and infrastructure monitoring, in the control of water resources, oceans and coasts and in the control of agricultural and forest resources. Relevant added value of the SAR is to acquire images in all weather conditions to help in the prevention and analysis of disasters due to natural or anthropogenic causes and for all precursor phenomena of environmental disasters. In this scenario NOCTUA, a project carried out by a partnership leaded by D-Orbit and funded by Lombardia Region, offers multiple opportunities, representing a platform with SAR sensor at competitive costs and with excellent acquisition performance on the international scene

Crop yield prediction using multipolarization radar and multitemporal visible / infrared imagery

This paper describes research undertaken on the improvement of within-field late season yield forecasting for crops such as wheat using multi-temporal visible/infrared satellite imagery and multi-polarization radar satellite imagery. Experiments have been carried out using ASAR imagery from Envisat combined with nine bands of ASTER imagery from the NASA Terra satellite. An experimental test site in an agricultural area in the county of Lincolnshire, UK, has been used. The satellite imagery has been integrated using artificial neural networks which have been trained as predictors of the spatial distributions of yield per unit area in a variety of fields. Ground truth data in the form of yield maps from GPS-enabled combine harvesters have been used to train the neural networks and to evaluate accuracy. The results show that the combinations of ASTER and ASAR imagery can provide enhanced yield predictions with overall correlations of up to 0.77 between predicted and actual yield patterns. The results also show that the use of dual polarization radar data alone is not sufficient to give reasonable yield predictions even in a multi-temporal mode. It has also been shown that varying the architectures of the neural networks with ensembles can improve the overall results

Advances in Radar Remote Sensing of Agricultural Crops: A Review

There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field

Coherence estimation in synthetic aperture radar data based on speckle noise modeling

In the past we proposed a multidimensional speckle noise model to which we now include systematic phase variation effects. This extension makes it possible to define what is believed to be a novel coherence model able to identify the different sources of bias when coherence is estimated on multidimensional synthetic radar aperture (SAR) data. On the one hand, low coherence biases are basically due to the complex additive speckle noise component of the Hermitian product of two SAR images. On the other hand, the availability of the coherence model permits us to quantify the bias due to topography when multilook filtering is considered, permitting us to establish the conditions upon which information may be estimated independently of topography. Based on the coherence model, two coherence estimation approaches, aiming to reduce the different biases, are proposed. Results with simulated and experimental polarimetric and interferometric SAR data illustrate and validate both, the coherence model and the coherence estimation algorithms.Peer Reviewe

Evaluating remotely piloted aircraft estimates of crop height and LAI against satellite and crop model outputs

Crop simulation models (CSM) have been a method for decision makers to study the effects of crop management activities for predicting, planning, and improving crop growth for the past several decades. While the applicability and robustness of CSMs had been rapidly evolving, the methods of gathering input and validation data for CSMs has remained predominantly the same. However, the application of remote sensing technologies including remotely piloted aircraft systems (RPAS) and satellites for agricultural purposes has demonstrated the potential for automated rapid and high detail CSM validation data. This study evaluated the accuracy of validation data acquired using RPAS and satellite technologies when compared to CSM outputs and observed crop measurements. Imagery of an agricultural field was acquired throughout a growing season with the use of a multi-sensor RPAS and existing satellite missions. Field work was performed alongside the RPAS imagery acquisitions to collect input data for crop modelling and accuracy assessments. Using the acquired imagery, the crop height and leaf area index (LAI) values of crops in the field were estimated for multiple dates. The LAI was estimated using 1) a regression-based method and 2) a function of the fractional vegetation cover and the leaf angle distribution method. A CSM was run alongside the remote sensing to simulate crop height and LAI values. When the estimated values were compared to observed measurements, showing the RPAS-derived crop height values were significantly more accurate (RMSE=193.6 cm, RMSE=161.3 cm) than the satellite-derived crop heights values (RMSE=223.4 m, RMSE=117.1 m respectively) yet less accurate than the CSM crop heights values. The RPAS-derived LAI value accuracies (RMSE=0.42, RMSE=0.66) and satellite-derived LAI value accuracies (RMSE=0.56, RMSE=0.56) were similar but the RPAS was found to, on average, estimate LAI more accurately than the CSM. Overall, the RPAS methods showed moderate accuracy across both crop height and LAI estimations and was found to perform better than the CSM in some situations. Future work may include additional imagery acquisitions throughout a growing season to further test the accuracies of RPAS-derived estimates as well as integrating estimates directly into CSMs for validation purposes

Retrieval of Biophysical Parameters of Agricultural Crops Using Polarimetric SAR Interferometry

See attachJRC.G.6-Sensors, radar technologies and cybersecurit

Crop Growth Monitoring by Hyperspectral and Microwave Remote Sensing

Methoden und Techniken der Fernerkundung fungieren als wichtige Hilfsmittel im regionalen Umweltmanagement. Um diese zu optimieren, untersucht die folgende Arbeit sowohl die Verwendung als auch Synergien verschiedener Sensoren aus unterschiedlichen Wellenlängenbereichen. Der Fokus liegt auf der Modellentwicklung zur Ableitung von Pflanzenparametern aus fernerkundlichen Bestandsmessungen sowie auf deren Bewertung. Zu den verwendeten komplementären Fernerkundungssystemen zählen die Sensoren EO-1 Hyperion und ALI, Envisat ASAR sowie TerraSAR-X. Für die optischen Hyper- und Multispektralsysteme werden die Reflexion verschiedener Spektralbereiche sowie die Performanz der daraus abgeleiteten Vegetationsindizes untersucht und bewertet. Im Hinblick auf die verwendeten Radarsysteme konzentriert sich die Untersuchung auf Parameter wie Wellenlänge, Einfallswinkel, Radarrückstreuung und Polarisation. Die Eigenschaften verschiedener Parameterkombinationen werden hierbei dargestellt und der komplementäre Beitrag der Radarfernerkundung zur Wachstumsüberwachung bewertet. Hierzu wurden zwei Testgebiete, eines für Winterweizen in der Nordchinesischen Tiefebene und eines für Reis im Nordosten Chinas ausgewählt. In beiden Gebieten wurden während der Wachstumsperioden umfangreiche Feldmessungen von Bestandsparametern während der Satellitenüberflüge oder zeitnah dazu durchgeführt. Mit Hilfe von linearen Regressionsmodellen zwischen Satellitendaten und Biomasse wird die Sensitivität hyperspektraler Reflexion und Radarrückstreuung im Hinblick auf das Wachstum des Winterweizens untersucht. Für die optischen Daten werden drei verschiedene Modelvarianten untersucht: traditionelle Vegetationsindices berechnet aus Multispektraldaten, traditionelle Vegetationsindices berechnet aus Hyperspektraldaten sowie die Berechnung von Normalised Ratio Indices (NRI) basierend auf allen möglichen 2-Band Kombinationen im Spektralbereich zwischen 400 und 2500 nm. Weiterhin wird die gemessene Biomasse mit der gleichpolarisierten (VV) C-Band Rückstreuung des Envisat ASAR Sensors linear in Beziehung gesetzt. Um den komplementären Informationsgehalt von Hyperspektral und Radardaten zu nutzen, werden optische und Radardaten für die Parameterableitung kombiniert eingesetzt. Das Hauptziel für das Reisanbaugebiet im Nordosten Chinas ist das Verständnis über die kohärente Dualpolarimetrische X-Band Rückstreuung zu verschiedenen phänologischen Wachstumsstadien. Hierfür werden die gleichpolarisierte TerraSAR-X Rückstreuung (HH und VV) sowie abgeleitete polarimetrische Parameter untersucht und mit verschiedenen Ebenen im Bestand in Beziehung gesetzt. Weiterhin wird der Einfluss der Variation von Einfallswinkel und Auflösung auf die Bestandsparameterableitung quantifiziert. Neben der Signatur von HH und VV ermöglichen vor allem die polarimetrischen Parameter Phasendifferenz, Ratio, Koherenz und Entropy-Alpha die Bestimmung bestimmter Wachstumsstadien. Die Ergebnisse der Arbeit zeigen, dass die komplementären Fernerkundungssysteme Optik und Radar die Ableitung von Pflanzenparametern und die Bestimmung von Heterogenitäten in den Beständen ermöglichen. Die Synergien diesbezüglich müssen auch in Zukunft weiter untersucht werden, da neue und immer variablere Fernerkundungssysteme zur Verfügung stehen werden und das Umweltmanagement weiter verbessern können