Evapotranspiration (ET) plays a significant role in understanding the water necessities of crops during their growing season, and hence, aids to make a decision in agriculture (planting time, applying fertilizer, irrigation, yield prediction and etc.). In this context, over the past few years, a wide range of research studies have been implemented for learning field-level ET from low-resolution ET products by downscaling and/or data fusion strategies. Unlike these previous studies, this research aims to leverage deep learning based models to learn ET from temporally and spatially dense imaging data; Sentinel-1 and climate data; ERA-5, both provided by Copernicus Climate Change Service. The model is formed by weak supervision from high spatial resolution Sentinel-1 coupled with climate data and analysis ready ET product as target. We evaluated the framework across two geographically distributed regions, namely; The Balkans and The Aegean in order to understand how well weak supervision estimates ET over croplands in different ecosystems.The code for the SAR2ET model is publicly available at https://github.com/Agcurate/SAR2ET, where you can access all the details regarding the model.

Crop yield estimation at the national scale is on the rise with a clear increase in freely available satellite images; providing field-level crop masks and their corresponding Earth Observation (EO) data-based predictors. In line with increasing EO data, the yield estimation problem is moved from a univariate to a multivariate time series analysis problem. Indeed, most of the state of the art methods applied to yield estimation are enabled to increase the accuracy of the yield estimation model using these multisource EO data. However, one of the major drawbacks of these methods is the lack of explainability of the yield. In this study, we will try to understand what EO-based time series data says about the agricultural practices differences among the geographically distributed fields and which kinds of dissimilarities exist among these multi-source EO data, and what regional/global factors cause these dissimilarities. In order to understand this, the study will go through the shape based and feature based time series similarity metrics that often highlight different characteristics of the time series data. While the temperature’s parameters (i.e., the incident solar radiation and 2 m dewpoint temperature), not the temperature itself, highlight geographical variation in yield data and behave as a geotag, these climate variables are not the only cause, but are the contributing ones, driving the yield variation distribution patterns of nationwide data.

Earth Observation (EO)-based crop yield estimation, which focuses on leveraging crop conditions at any time t has recently played a critical role in the development of nationwide crop monitoring. Following the developments in open data policy in remote sensing, the high-resolution freely available EO data provides annual crop masks and enables the understanding of more complex patterns of the agricultural practices at field level. Despite the detailed information provided by EO-imaging data at the field level, the resolution of the target variable, commune-level yield, restricts the effective usage of EO imaging data and, in turn, imposes limitations on leveraging the large amount of EO data, which could provide accurate yield estimation by a physics aware data-driven estimation models. In this paper, we explore the challenges of the uncertainties associated with data-driven yield estimation using the Turkey cotton dataset, which comprises EO-based time series from Sentinel-1 and Sentinel-2 imaging satellites, together with climate variables and soil properties. These uncertainties can arise from discrepancies in the EO-based crop mask data, an unreliable statistical dataset, and the variance in the spatio-temporal characteristics of descriptive features and low-resolution yield statistics that are used in the estimation.

In this study, we examined the effectiveness of integrating satellite-based crop biophysical parameters, meteorological conditions, and soil properties for the end and mid-season cotton yield prediction in the continental United States (CONUS) region. We employed six machine learning algorithms: decision tree (DT), random forest (RF), adaptive boosting (Ad-aBoost), gradient boosting (GB), light gradient boosting machine (LightGBM), and extreme gradient boosting machine (XGBoost). By employing this rigorous approach to hyperparameter tuning based on Bayesian optimization, the XGBoost method was found as the best method for both mid-season and end-season cotton yield prediction. Furthermore, we investigated the global importance of temporal and static features using the Shapley Additive Global importancE (SAGE) method to understand the driving factors of cotton yield prediction. As a result of global feature importance analysis, precipitation (P), enhanced vegetation index (EVI), and leaf area index (LAI) were found as the most important temporal features, while silt and pH were found as the most important soil properties.

Cotton, a vital crop in the global textile industry, faces challenges from climate and ecosystem changes. Accurate cotton yield prediction is crucial for the economy and environmental sustainability, and it requires a deep understanding of the complex relationship between its parameters and yield. To achieve this, a comprehensive approach integrating climatic factors, soil parameters, and biophysical parameters observed through high-resolution remote sensing satellites was employed. This study utilized a multisource dataset to develop a predictive model for cotton yield over Turkiye, allowing accurate yield estimation and understanding the impact of the Earth Observation (EO)-based yield predictors on the model. Specifically, we utilized the Explainable Boosting Machine (EBM) algorithm to model and predict cotton yield while offering insights into selecting EO predictors. Additionally, we conducted a performance evaluation of our proposed approach in comparison to popular boosting-based algorithms like eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and Light gradient boosting (Light-GBM).

Earth observation (EO) sensors and remote sensing play a crucial role in the detection and analysis of environmental hazards. Among these hazards, monitoring and understanding marine pollution remain challenging problems due to the complex and dynamic spatio-temporal characteristics of pollution and water. The recent outbreak of marine mucilage (or sea-snot) in the inland Sea of Marmara in the Spring of 2021 is a striking example of a dynamic environmental pollution, This work investigates temporal analysis of mucilage by unmixing based change detection on temporal hyperspectral satellite data acquired with the recently launched PRISMA sensor. The proposed unmixing-based approach enables detecting temporal changes in terms of endmembers and abundances, and hence provides information on the nature of the change, in an unsupervised manner without any training step.

This study aimed to assess the influence of Earth observation (EO) time series data, specifically soil properties, climate variables, and Enhanced Vegetation Index, on predicting cotton yield using an explainable artificial intelligence model. By utilizing statistical yield data acquired at the commune level in Turkey between 2019-2021, we developed a model for predicting cotton yield. The model employed the Long Short-Term Memory (LSTM) architecture and incorporated the SHapley Additive exPlanations (SHAP) method as a post-hoc method to explain how EO features impact the cotton yield and to interpret the relationship between these features and the variations in yield data.

Examination of deformations in bridges is an important source of information in terms of giving an idea about the health status of bridges. Bridges are often exposed to more than one external force, the vibrations caused by the forces they are exposed to can affect the quality of use of the bridge and its safety during use of the structure. Reliable description of the behavior of the bridge under load requires the use of a method that allows simultaneous observation of many points. Therefore, the measurement technique of these forces that bridges are exposed to must provide with high accuracy and high frequency displacement observation. It is also desirable that the measuring device does not need to have direct access to the observed object. For these reasons, this project offers the opportunity to evaluate the health status of bridges by matching the data obtained by satellite-based Interferometric Synthetic-aperture Radar (InSAR) technique on three-dimensional models via Unmanned Aerial Vehicle (UAV) images. The InSAR technique enables the detection of deformations occurring on the earth with millimeter precision by using Synthetic-aperture Radar (SAR) data collected by radar satellites in earth orbit. Sentinel-1A and Sentinel-1B satellites, which are currently in orbit, offer both detection and continuous observation over large areas, since their data is made public by the European Space Agency (ESA) and data is collected by passing through the same region every six days. In this way, deformations occurring on the bridge can be evaluated periodically to detect abnormal displacements at any position of the bridge. For this purpose, it was aimed to use two independent Sentinel-1 SAR datasets covering the years 2016-2021, containing 240 images in ascending orbit and 169 images in descending orbit, respectively. With the deformation maps created from these time series, the displacements on the bridges were determined in a short time and at low cost. These displacements were visualized by integrating them into the three-dimensional infrared - thermography (IRT) model obtained from the UAV images.

Cotton is under the threat of climate and ecosystem change, and has an essential role in the global textile industry. This makes its yield prediction essential for both economics and sustainability. The potential cotton yield can be predicted by integrating climatic factors, soil parameters, and biophysical parameters observed by high temporal & spatial resolution remote sensing satellites. This study used a multisource dataset to create an explainable and accurate predictive model for cotton yield prediction over the continental US (CONUS). A recently proposed glass-box method called Explainable Boosting Machine (EBM), which provides transparency, reliability, and ease of interpretation, was implemented. Accuracy performance was compared with common machine learning (ML) methods for predicting cotton yields. The EBM showed higher accuracy against other glass-box methods and competitive results with black-box models. With the help of the EBM, the importance of individual features and their pairwise interactions was revealed without applying any post-hoc methods. The study findings showed that the precipitation (P), enhanced vegetation index (EVI), and leaf area index (LAI) are the three most important dynamic features. The dynamic features are the driver of the created model with 78% of the overall feature importance, followed by pairwise interactions of the features with 16% contribution. Lastly, static features contribute 6% to the overall feature importance. The study highlights the importance of using multi-source data and interactions of the input features and providing an interpretable model to understand the inner dynamics of cotton yield predictions.

The mucilage outbreak in the Sea of Marmara in the spring of 2021 has once again emphasized the importance of addressing climate and pollution associated hazards. Although multispectral images have traditionally been used for such purposes, an analysis of marine mucilage, with its spectral similarity to marine debris, and its spectral variations due to composition and/or sediment or bacterial aggregation, is a prime candidate to benefit from the advantages of hyperspectral data. The recently launched PRISMA mission provides an important opportunity to this end. This work proposes the use of unmixing on PRISMA datasets in order to analyze the spectral characteristics, the variation due to aggregation, and the spatial distribution, of marine mucilage. The proposed approach provides consistent and relevant information on two different datasets, with the potential to benefit cleaning and understanding efforts for marine mucilage. In addition, unlike the previous studies with supervised classification, the proposed approach does not require a training step, and the abundance fraction maps obtained using unmixing are easy to interpret and analyze for mucilage aggregation.

In recent years, the efficiency of Terrestrial Laser Scanner (TLS) data for dendrometry has been widely tested and it becomes more widely accepted in small and large scale forests due to its millimeter accuracy. Although TLS products provide high accurate measurements, the high costs of field data collection could be a challenge in some forest stand. Unmanned Aerial Vehicle (UAV) imagery could be an alternative for modeling and monitoring of the dendrometric parameters, specifically on sparse forest. For this purpose, the potential of UAV imagery for diameter at breast height (DBH) estimation, one of the key forest tree parameters, is discussed against TLS based DBH measurements in a Stage D(4) low stem density Oak stand, using point cloud data (PCD)-based ellipse fit. No significant difference was seen between the DBH measurements in regards to the stem diameter. However, the UAVbased DBH measurements had a high variance in the ellipse fit process.

The latest sea-snot, i.e. mucilage, outbreak in the Sea of Marmara hit all the headlines in Turkey in the Spring-Summer of 2021. Its slimy mucus characteristic was seen on the sea surface, but marine researchers warned that it spread down to 30 metres below the surface and could cause serious water-borne diseases, in addition to its detriment to the economy. Prevention and clean-up measures have been started and are ongoing. In this context, using remote sensing approaches can provide a significant advantage for understanding not only its spatial distribution throughout the Sea of Marmara but also its spectral and biochemical properties. In this work, the ag-gregation and spatial distribution of mucilage are investigated in the Sea of Marmara, Turkey, using hyperspectral data acquired by the PRISMA sensor.

Fast soil moisture content (SMC) mapping is necessary to support water resource management and to understand crop growth, quality, and yield. Therefore, earth observation (EO) plays a key role due to its ability of almost real-time monitoring of large areas at a low cost. This study aimed to explore the possibility of taking advantage of freely available Sentinel-1 (S1) and Sentinel-2 (S2) EO data for the simultaneous prediction of SMC with cycle-consistent adversarial network (CycleGAN) for time-series gap filling. The proposed methodology, first, learns latent low-dimensional representation of the satellite images, then learns a simple machine learning (ML) model on top of these representations. To evaluate the methodology, a series of vineyards, located in South Australia ’s Eden valley are chosen. Specifically, we presented an efficient framework for extracting latent features from S1 and S2 imagery. We showed how one could use S1 to S2 feature translation based on CycleGAN using S1 and S2 time series when there are missing images acquired over an area of interest. The resulting data in our study is then used to fill gaps in time-series data. We used the resulting latent representations to predict SMC with various ML tools. In the experiments, CycleGAN and the autoencoders were trained with data randomly chosen around the site of interest, so we could augment the existing dataset. The best performance was demonstrated with random forest (RF) algorithm, whereas linear regression model demonstrated significant overfitting. The experiments demonstrate that the proposed methodology outperforms the compared state-of-the-art methods if there are missing optical and synthetic-aperture radar (SAR) images.

Predicting biophysical parameters with high accuracy and fast speed based on remote sensing-based modeling is an attractive topic. In this context, the revisit time, coverage, and illumination condition in-dependency make the Polarimetric Synthetic Aperture Radar (PoISAR) data is an attractive tool. In this paper, one of the most studied biophysical parameters, Leaf Area Index (LAI), is chosen to assess Polynomial Chaos Expansion (PCE) regression, commonly used metamodeling due to its precise and rapid approximation performance. Experimental analysis based on AgriSAR 2009 campaign, including oat and canola, is given to validate the PCE in the regression. According to the accuracy analysis, the Pearson correlation of 88% and 95% for oat and canola, respectively, were achieved.

Water management and up-to-date soil moisture (SM) information are crucial to ensure agricultural activities in dry-land farming regions. In this context, remote sensing imagery coupled with machine learning techniques can provide large scale SM information if there is enough data for training, which is really limited in reality. In this paper, we explored the potential of cycle-consistent Generative Adversarial Network (GAN) for data augmentation for training machine learning algorithms, which try to model spatial and temporal dependencies between the SM prediction (output) and the remote sensing imagery (input features). Specifically, the freely available SAR (Sentinel-1) and optical (Sentinel-2) time series data were evaluated together to predict SM using GANs. The experiments demonstrate that the proposed methodology outperforms the compared state-of-the-art methods if there is not enough data to train a regression convolutional neural networks (CNN) to predict SM content.

It is significant to monitor the phenological stages of agricultural crops with accurate and up-to-date information. In monitoring the phenological phases of some crops, optical remote sensing data offers significant spectral information and outstanding feature identification. However, a continuous time series of optical remote sensing data is difficult to obtain due to the weather dependency of optical acquisitions. In this paper, the feasibility of transfer learning between the features of Sentinel-1 and Sentinel-2 is evaluated to reduce these difficulties. A feature translation based on deep learning (DL) method, namely Cycle-Consistent Generative Adversarial Networks (cycle-GAN), was applied between Sentinel-1 and Sentinel-2 data. In order to evaluate the effect of the cycle-GAN method on crop type mapping and identification, Random Forest classification was applied to four different cases (Real SAR, Fake Optical + Real SAR, Real Optical, and Real Optical + Real SAR).

The role of global sensitivity analysis (GSA) is to quantify and rank the most influential features for biophysical variable estimation. In this letter, an approximation model, called high-dimensional model representation (HDMR), is utilized to develop a regression method in conjunction with a GSA in the context of determining key input drivers in the estimation of crop biophysical variables from polarimetric synthetic aperture radar data. A multitemporal Radarsat-2 data set is used for the retrieval of three biophysical variables of barley: leaf area index, normalized difference vegetation index, and Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie stage. The HDMR technique is first adopted to estimate a regression model with all available polarimetric features for each biophysical parameter, and sensitivity indices of each feature are then derived to explain the original space with a smaller number of features in which a final regression model is established. To evaluate the applicability of this methodology, root-mean square and coefficient of determination were performed under different amounts of samples. Results highlight that HDMR can be used effectively in biophysical variable estimation for not only reducing computational cost but also for providing a robust regression.

Crop phenology monitoring using Synthetic Aperture Radar (SAR) data is gaining popularity within the remote sensing community due to SAR's all weather and large coverage imaging capability. This paper introduces a polynomial chaos expansion (PCE) based regression algorithm to retrieve BBCH scale of crops, which identifies the phenology of crops in a standardized system. The impact and applicability of the proposed methodology is successfully illustrated using the TerraSAR-X dual-Pol imagery that was acquired over the cultivation period of paddy-rice fields located in Turkey. To assess the applicability of the methodology, root mean square and correlation analysis were performed under different amount of training data and number of inputs.

Tracking crop's biophysical parameters using temporal Pol-SAR (Polarimetric Synthetic Aperture Radar Data) data is an active research topic in precision agriculture due to the sensitivity of PolSAR acquisition to canopy's physical and geometrical structure. Reconstruction of polarimetric features from collection of SAR data is computationally expensive, and more important, the inter-features correlations cause decreased performance in regression based biophysical parameter estimation. With the scope of operational crop monitoring, this study provides key variables to drive Leaf Area Index (LAI) from polarimetric data based on global sensitivity analysis (GSA) addressing the ranking of the most influential features. We applied variance-based GSA for temporal fully-polarimetric RadarSAt-2 images acquired through the cultivation period of two crops; canola and barley. Among 20 polarimetric features, anisotropy and correlation magnitude between co-polar channels were found to be the most influential polarimetric features for canola and barley, respectively.

Space-borne interferometric SAR has advanced significantly in the last decades, with many successful Earth monitoring applications. The key point of this success lies in the fact that interferometric SAR supplies unprecedented phase and amplitude information characterizing target's physical parameters. This paper presents the role of interferometric SAR, specifically considering the bistatic mission TanDEM-X, for shallow lake water level estimation by using sediment storage.

Satellite remote sensing provides a means of quantifying water budget variables over regions where in-situ measurements are scarce. This paper investigates the use of remote sensing products and WGHM (WaterGAP Global Hydrological Model) on water budget closure in Cyprus (35N-33W). The remote sensing products relate the terrestrial water storage change (ΔS) derived from Gravity Recovery and Climate Experiment (GRACE) with precipitation (P) derived from Tropical Rainfall Measuring Mission (TRMM) and evapotranspiration (ET) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). Their joint potential to close the water budget is examined over an 11-year period (2003–2013) by neglecting the effect of runoff (Q) in the equation. Q component is eliminated from the equation due to the short flow time of streams in Cyprus. WGHM results are also compared with the remote sensing results. Although WGHM does not contain deep groundwater, It generally shares same patterns with GRACE results and the correlation coefficient between them is 0.65. GRACE derived ΔS has higher amplitude than WGHM derived ΔS. This can be attributed to the contribution of deep groundwater and the infiltration from neighboring regions. Also, the amplitude of GRACE derived total water storage change is smaller than TRMM-MODIS because of the existence of poor discharge flowing out of the region. The total change in storage values generally vary between −60 and 150 mm/month except January of 2004. Unusual precipitation was seen at this month and the discharge due to precipitation should be accounted in the balance equation. This study also shows that the equivalent water height (EWH) in Cyprus faced with a significant decreasing trend with an average rate of −1.56 mm/a and −1.96 mm/a from GRACE and WGHM respectively.

Polarimetric SAR interferometry has been recently applied with TanDEM-X data to the retrieval of vegetation height in rice fields, which constitutes the first demonstration of this technique applied to agricultural crops with satellite data [1]. In this work we extend that study by including an analysis of the effect of incidence angle and baseline on the retrieval of height. The study is based on the exploitation of 6 time series of TanDEM-X acquisitions during its science phase: 3 over Sevilla (Spain), with incidence angles of 22, 30 and 39 degrees, and 3 over Ipsala (Turkey), with incidence angles of 30, 36 and 44 degrees.

This paper presents a study of the sensibility of the incoherent (electromagnetic backscattering model) and coherent (DInSAR and PolInSAR inversion) crop height estimation methods of SAR imaging. The methods were compared for paddy-rice crop height monitoring with a TanDEM-X dataset. For this, rice-cultivated agricultural fields located in Northern Turkey were selected. Intensive ground data collection during the cultivation period in 2015 was carried out. The accuracy analysis showed that the requirement of external (vegetation-free) DEM in DInSAR-based crop height estimation decreases its performance compared to the PolInSAR and backscattering inversion methods.

Synthetic Aperture Radar (SAR) remote sensing techniques play a significant role in modern agricultural crop monitoring by relating the plant structure (height, biomass, yield and growth-stage) to the backscattering behavior of the vegetative canopy. The current trend in crop monitoring is towards precision agriculture, which needs detailed morphology information. By predicting the physical structure, one can just determine the under and overgrowth conditions. In this study, we propose a probabilistic inversion algorithm for a Radiative Transfer Theory (RTT) model which relates the backscattering response of a canopy to its physical structure. The outcomes of the inversion provided promising results by estimating the dimensions of the primary structures with a small bias.

The results of a two-year precision agriculture project have clearly demonstrated that TanDEM-X can successfully classify crops morphology through cultivation period. It has been found that TanDEM-X mission is capable of tracking the crop height, and the accuracy of the height estimation depends on the crop morphology, which causes a diversity between canopy top and acquisition phase center. In this work, in addition to interferometry with single polarized channels, polarimetric-interferometric acquisitions have been employed to figure out phase center diversity. The analysis showed that there is a diversity between height estimations from HH and VV polarized interferometric channels, which can reach to 10 cm in the reproductive stages of the crops.

The crop phenology retrieval on precision agriculture has been an important research area with the increasing demand on crops. Remotely sensed Synthetic Aperture Radar (SAR) data provides a simple possibility for automatic monitoring of agricultural fields due to the its inherit all-weather monitoring capability. Most of the studies rely on morphology based modelling of the electromagnetic backscattering which requires Monte Carlo simulations. In this paper, instead of modelling the backscattering of the signals for monitoring the crop fields, a classification scheme was implemented on the data acquired by TerraSAR-X by using the features extracted from backscattering coefficients with the machine learning algorithms which are Support Vector Machines, k-Nearest Neighbor and Regression Tree.

Remote sensing techniques with Synthetic Aperture Radar (SAR) provides detailed information of the electromagnetic scattering behaviour of their targets. It is broadly used for agricultural monitoring due to their all-weather acquisition possibility. Based on the nature of SAR systems, they are known to be sensitive to physical changes in the targets, such as plant growth. An uncertainty analysis for a plant morphology based electromagnetic scattering model is assessed in this study. The global sensitiveness of the model to the input variables are tested with respect to their total Sobol' indices. This helps to understand the parameters for each growth stage and polarization channel, which can be used to reduce the complexity of the scattering model.

It has been recently shown that the TanDEM-X mission is capable of tracking the plant growth of rice paddies. The precision of the elevation measure depends on the physical interaction between the synthetic aperture radar (SAR) signal and the canopy. In this letter, this interaction is studied by considering the signal polarization. In particular, the vertical and horizontal wave polarizations are compared, and their performance in the temporal mapping of the crop height is analyzed. The temporal elevation difference analysis shows a monotonically increasing trend within the reproductive stage of the canopy, with maximum height discrepancies between polarizations of about 9 cm. From an operational point of view of InSAR-based vegetation height measurements, this letter demonstrates that the oriented structure of the canopy shall be considered not only in polarimetric InSAR studies but also in the interpretation of bistatic spaceborne interferometric elevation models.

Precision agriculture research, which aims to monitor agricultural fields and to manage agricultural practice by considering overall environmental impacts, has gained momentum with the recent improvements in the remote sensing area. The objective of this letter, as a part of precision farming, is to implement Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale assignment in plant growth monitoring by means of SAR. The proposed approach copes with structural heterogeneity in agricultural fields by grouping together similar morphologies. For this, densely cultivated paddy rice fields are analyzed using TerraSAR-X (TSX) co-polar SAR data. For generating structurally similar groups, K-means clustering is used in a polarimetric feature vector space, which is composed of backscattering intensities and polarimetric phase differences. This step is followed by a preliminary classification approach based on the temporal separability of the explanatory parameters. In the last step of the proposed methodology, assigned classes are updated based on the biological principles that are followed in rice cultivation. This letter provides the results of the proposed algorithm and compares them to the standard threshold-based approach in two independent agricultural areas. The results show the superiority of the feature-clustering-based classification compared with the standard approach in handling field heterogeneity.

This paper evaluates the potential of spaceborne bistatic interferometric synthetic aperture radar images for the monitoring of biophysical variables in wetlands, with a special interest on paddy rice. The assessment is made during the rice cultivation period, from transplanting to harvesting time (May to October) for fields around Gala lake (Turkey), one of the largest and most productive paddy rice planting area in the country. Detailed ground truth measurements describing biophysical parameters are collected in a dedicated campaign. A stack of 16 dual-pol TanDEM-X images is used for the generation of 32 digital elevation models (DEMs) over the studied area. The quality of the data allows the use of the interferometric phase as a state variable capable to estimate crop heights for almost all the growing stages. The early vegetative rice stage, which is characterized by flooded fields, cannot be represented by the interferometric phase due to a low signal-to-noise ratio but can be easily detected by amplitude and interferometric coherence thresholding. A study on the impact of the polarization in the signal backscatter is also performed. An analysis of the differences between HH and VV DEMs shows the varying signal penetration for the two polarizations at different growing stages. The validation with reference data demonstrates the capability to establish a direct relationship between interferometric phase and rice growth. The very high coherence of TanDEM-X data yields elevation estimates with root-mean-square error in a decimetric level, supporting temporal change analysis on a field-by-field basis.

Paddy rice is a staple food that feeds more than half of the world's population. As such, monitoring paddy rice with Synthetic Aperture Radar (SAR) image is a critical area of research. Many possible measures of rice growth as canopy height, LAI, biomass and etc. are considered in the previous works. Among them, canopy height is the most direct measurement and has direct relationship with growth rate. In this study, to monitor paddy rice fields canopy heights are estimated by SAR images containing phase and amplitude information. These two inherent properties of SAR images are examined to retrieve canopy height by a canopy scattering and by a differential interferometric method at X-band. Accuracy analysis showed that differential interferometric technique gives very precise results in terms of canopy height if the canopy is fresh. However, in the case of dry canopy layer, the X-band canopy backscattering model gives much more precise results than the interferometric method as the X-band radar signals penetrate more into canopy in dry case. Even though interferometric techniques do not give detailed information about the physical structure of the canopy as backscattering model do, in this work it is shown that they can be used in operational monitoring.

A strategy to evaluate rice plant growth from TanDEM-X data is assessed in this paper. Single fields are segmented exploiting their early vegetative stage, when they are flooded. Height is then extracted from the bistatic interferometric phase in a field-by-field basis and temporal change maps useful to production estimation are generated. The accuracy of the plant height estimation is in a decimetric level.

Polarimetric synthetic aperture radar (PolSAR) images are widely used for agricultural fields monitoring and change detection applications due to their all-weather acquisition possibilities and inherent properties including phase and amplitude information. The techniques used for such temporal applications can be cast in two groups: polarimetric (incoherent) and polarimetric-interferometric ( coherent), being represented in this letter by the Kullback-Leibler distance and the mutual information, respectively. The goal of this letter is to characterize these two kinds of different information sources in terms of ground measurement parameters of the agricultural fields and to figure out the relationship between temporal trends of the similarity measures versus temporal trends of the physical parameters without dealing with inverse problems. For this purpose, multitemporal fully polarimetric SAR images, which are acquired in the frame of the AgriSAR 2006 campaign with synchronous ground surface measurements over a whole vegetation period, are analyzed. The results have clearly demonstrated that the coherent measures have a strong relationship with wet biomass of crops. Although incoherent measures would be the preferred ones due to their simplicity in implementation, they showed to be very sensitive to changes in precipitation, causing misleading temporal interpretation at longer wavelength in some cases.

The contribution of polarimetric synthetic aperture radar (PolSAR) images compared with that of single-channel SAR images in terms of temporal scene characterization has been found and described to add valuable information in the literature. However, despite a number of recent studies focusing on single-polarized glacier monitoring, the potential of polarimetry to estimate the surface velocity of glaciers has not been explored due to the complex mechanism of polarization through glacier/snow. In this paper, a new approach to the problem of monitoring glacier surface velocity is proposed by means of temporal PolSAR images, using a basic concept from information theory, i.e., mutual information (MI). The proposed polarimetric tracking method applies the MI to measure the statistical dependence between temporal polarimetric images, which is assumed to be maximum if the images are geometrically aligned. Since the proposed polarimetric tracking method is very powerful and general, it can be implemented into any kind of multivariate remote sensing data such as multichannel optical and single-channel SAR images. The proposed polarimetric tracking is then used to retrieve the surface velocity of the Aletsch Glacier in Switzerland and the Inylchek Glacier in Kyrgyzstan with two different SAR sensors: the Experimental SAR airborne L-band (fully polarimetric) and Envisat C-band (single-polarized) systems, respectively. The effect of the number of channels (polarimetry) into tracking investigations demonstrated that the presence of snow, as expected, affects the location of the phase center in different polarization and frequency channels, as for the glacier tracking with temporal HH compared to temporal VV channels. In this paper, it is shown how it is possible to optimize these two different contributions, considering the multichannel SAR statistics.

Polarimetric Synthetic Aperture Radar (PolSAR) images are widely used for agricultural fields monitoring and change detection applications due to their all-weather acquisition possibilities and inherent properties including phase and amplitude information. The techniques used for such temporal applications can be cast in two groups: polarimetric (incoherent) and polarimetric-interferometric (coherent) being represented in this work by the KL-distance and the Mutual Information, respectively. The goal of this work is to characterize these two kinds of different information sources in terms of ground measurement parameters of the agricultural fields, and to figure out the relationship between temporal trends of the similarity measures versus temporal trends of the physical parameters without dealing with inverse problems. For this purpose multi-temporal fully polarimetric SAR images, acquired in the frame of the AgriSAR 2006 campaign with synchronous ground surface measurements over a whole vegetation period are analyzed.

The contribution of Polarimetric Synthetic Aperture Radar (PolSAR) images compared with the single-channel SAR in terms of temporal scene characterization has been found and described to add valuable information in the literature. Recently, a new PolSAR tracking algorithm has been proposed for glacier surface velocity monitoring. The proposed polarimetric tracking method applies Mutual Information (MI) to measure the statistical dependence between temporal polarimetric images, which is assumed to be maximum if the images are geometrically aligned. In this paper, its implementation of interest will be investigated.

This paper proposes a new method for a measure of coherent similarity between temporal multichannel synthetic aperture radar (SAR) images and its implementation to change detection application. The method is based on mutual information (MI) from information theory. The MI measures the amount of information in common between coherent temporal multichannel SAR acquisitions. In order to develop an algorithm for all kinds of SAR images, such as interferometric SAR, polarimetric-interferometric SAR (PolInSAR), and partial PolInSAR, first, the joint density function of temporal multichannel images based on their second-order statistics has been derived. Then, the derived joint density function is used to calculate an analytical expression for the MI between temporal images, which is assumed to be maximal if the temporal images are identical. Although, in this paper, a new coherent similarity measure has analytically been derived for temporal polarimetric SAR images based on complex Wishart process in time, since the mathematical formulation is general, it can equally well be implemented into any kind of multivariate remote sensing data, such as multispectral optical and interferometric images after small continuation. This derived quantity has been implemented for change detection application whose aim is to characterize the temporal behavior of the acquisitions. A comparison between the proposed and the other well-known change detection methods by means of scene characterization is shown, describing the advantages due to the fact that the proposed change detector involves almost every facet of applied change detection.

The contribution of Polarimetric Synthetic Aperture Radar (PolSAR) images compared with the single-channel SAR in terms of temporal scene characterization has been found and described to add valuable information in the literature. In this paper, a new PolSAR change detector which makes use of Kullback-Leibler divergence is described. Kullback-Leibler divergence (KL-divergence) measures the amount of information in common between coherent temporal multi-channel polarimetric SAR acquisitions. Although in this paper KL-divergence measure has analytically been derived for temporal polarimetric SAR images based on complex Wishart process in time, since the mathematical formulation is general, it can be implemented into any kind of multivariate remote sensing data such as multi-spectral optical and interferometric images. KL-divergence measure is also simplified to give an explicit expressions for temporal single channel SAR images. The new results are simple, easy to be used, and superior in providing detailed structure.

This paper proposes a new method for change detection measurement including whole SAR imaging modes such as PolIn- SAR, partial PolInSAR and InSAR in a set of multi-temporal multidimensional SAR images. The method is based on the special case of Kullback-Leibler (KL-divergence) test, known as Mutual Information. In order to develop an algorithm, firstly the joint distribution of PolInSAR data set, based on the second order statistics has been derived. Such a derivation accounts for the whole multi-temporal SAR images. Then the mutual information is used to measure the difference between the joint density of multi-temporal PolSAR data sets and their marginal density known as complex Wishart distribution. A comparison between the proposed and the other well-known change detection (e.g. cross correlation) technique is shown by means of real data, describing the advantages due to the fact that the proposed change detector involves almost every facet of the applied change detection.

In this paper, the accuracy assessment of the recently proposed Maximum Likelihood (ML) texture tracking algorithm is discussed. Its comparison with the well known texture tracking technique, i.e., Normalized Incoherent Cross Correlation (NICC), has also been investigated in the case of the presence of multiplicative noise structure.

The performance of a tracking algorithm considering remotely sensed data strongly depends on a correct statistical description of the data, i.e., its noise model. The objective of this paper is to introduce a new intensity tracking algorithm for synthetic aperture radar (SAR) data, considering its multiplicative speckle/noise model. The proposed tracking algorithm is discussed regarding the measurement of glacier velocities. Glacier monitoring exhibits complex spatial and temporal dynamics including snowfall, melting, and ice flows at a variety of spatial and temporal scales. Due to these complex characteristics, most traditional methods based on SAR suffer from speckle decorrelation that results in a low signal-to-noise ratio. The proposed tracking technique improves the accuracy of the classical intensity tracking technique by making use of the temporal speckle structure. Even though a new intensity-based matching algorithm is proposed, particularly for incoherent data sets, the analysis of the proposed technique was also performed for correlated data sets. As it is demonstrated, the velocity monitoring can be continuously performed by using the maximum likelihood (ML) texture tracking without any assumption concerning the correlation of the data set. The ML texture tracking approach was tested on ENVISAT-ASAR data acquired during summer 2004 over the Inyltshik glacier in Kyrgyzstan, representing one of the largest alpine glacier systems of the world. It will be demonstrated that the proposed technique is capable of robustly and precisely detecting the surface velocity field and velocity changes in time.

A grown importance in long-term operational glacier monitoring has emerged, mainly due to the connection of glacier recession to climate changes. Up to now, mainly two types of methods have been used for the estimation of glacier flow velocities: Image matching and differential interferometry (DInSAR). Although the principal potential of DInSAR for glacier velocity estimation has been shown in several case studies, its successful application is often limited by phase noise, described by the coherence. Additionally, the glacier velocity is often too large to be analysed by means of DInSAR since this method can be too sensitive to correctly track the large displacements occurring during a typical data acquisition interval of one month. SAR amplitude images are not limited by phase stability problems like in DInSAR and can reliably be acquired on a regular basis. In this work, a novel algorithm for computing the velocity field and motion parameters from a sequence of SAR amplitude images are presented. The algorithm is based on the vector relaxation combined with standardized cross- covariance matrix information and cross-correlation techniques. The cross-correlation is used to indicate the candidate motion vectors for each pixel. After this step, by a relaxation operation local smoothness constraints are introduced into the estimated flow pattern, leading to a more homogeneous velocity estimation. In order to handle fast motion and reduce the mismatches, the mentioned algorithms are applied in different scales and linked using anisotropic diffusion equation in case of multiscale cross-correlation. This significantly improves the reliability of the motion detection in the presence of noise, inherent in case of SAR data.

An interesting, but rarely used technique in polarimetric SAR interferometry is the enhancement of interferometric coherence by projection into an optimal polarimetric state. In particular, newly developed methods for polarimetric optimisation of multi-baseline coherences provide the possibility of simultaneous constrained coherence optimisation for more than one baseline. This technique can significantly improve the usefulness of long-term interferometric pairs and time-series, and appears, therefore, of interest to various fields of application. The aim of this paper is to discuss the correct derivation of multi-baseline differential interferograms with polarimetrically optimised coherence and to outline several possible areas of application, particularly in the field of differential interferometry and permanent scatterers.