Potential of X-Band Images from High-Resolution Satellite SAR Sensors to Assess Growth and Yield in Paddy Rice

The comprehensive relationship of backscattering coefficient (σ0) values from two current X-band SAR sensors (COSMO-SkyMed and TerraSAR-X) with canopy biophysical variables were investigated using the SAR images acquired at VV polarization and shallow incidence angles. The difference and consistency of the two sensors were also examined. The chrono-sequential change of σ0 in rice paddies during the transplanting season revealed that σ0 reached the value of nearby water surfaces a day before transplanting, and increased significantly just after transplanting event (3 dB). Despite a clear systematic shift (6.6 dB) between the two sensors, the differences in σ0 between target surfaces and water surfaces in each image were comparable in both sensors. Accordingly, an image-based approach using the “water-point” was proposed. It would be useful especially when absolute σ0 values are not consistent between sensors and/or images. Among the various canopy variables, the panicle biomass was found to be best correlated with X-band σ0. X-band SAR would be promising for direct assessments of rice grain yields at regional scales from space, whereas it would have limited capability to assess the whole-canopy variables only during the very early growth stages. The results provide a clear insight on the potential capability of X-band SAR sensors for rice monitoring

Remote sensing of earth terrain

Abstracts from 46 refereed journal and conference papers are presented for research on remote sensing of earth terrain. The topics covered related to remote sensing include the following: mathematical models, vegetation cover, sea ice, finite difference theory, electromagnetic waves, polarimetry, neural networks, random media, synthetic aperture radar, electromagnetic bias, and others

A 3-D Full-Wave Model to Study the Impact of Soybean Components and Structure on L-Band Backscatter

Microwave remote sensing offers a powerful tool for monitoring the growth of short, dense vegetation like soybean. As the plants mature, changes in their biomass and 3-D structure impact the electromagnetic (EM) backscatter signal. This backscatter information holds valuable insights into crop health and yield, prompting the need for a comprehensive understanding of how structural and biophysical properties of soybeans as well as soil characteristics contribute to the overall backscatter signature. In this study, a full-wave model is developed for simulating L-band backscatter from soybean fields. Leveraging the ANSYS High-Frequency Structure Simulator (HFSS) framework, the model solves for the scattering of EM waves from realistic 3-D structural models of soybean, explicitly incorporating the interplant scattering effects. The model estimates of backscatter match well with the field observations from the SMAPVEX16-MicroWEX and SMAPVEX12, with average differences of 1-2 dB for co-pol and less than 4 dB for cross-pol. Furthermore, the model effectively replicates the temporal dynamics of crop backscatter throughout the growing season. The HFSS analysis revealed that the stems and pods are the primary contributors to HH-pol backscatter, while the branches contribute to VV-pol, and leaves impact the cross-pol signatures. In addition, a sensitivity study with 3-D bare soil surface resulted in an average variation of 8 dB in co- and cross-pol, even when the root mean square height and correlation length were held constant

Remote sensing of earth terrain

In remote sensing, the encountered geophysical media such as agricultural canopy, forest, snow, or ice are inhomogeneous and contain scatters in a random manner. Furthermore, weather conditions such as fog, mist, or snow cover can intervene the electromagnetic observation of the remotely sensed media. In the modelling of such media accounting for the weather effects, a multi-layer random medium model has been developed. The scattering effects of the random media are described by three-dimensional correlation functions with variances and correlation lengths corresponding to the fluctuation strengths and the physical geometry of the inhomogeneities, respectively. With proper consideration of the dyadic Green's function and its singularities, the strong fluctuation theory is used to calculate the effective permittivities which account for the modification of the wave speed and attenuation in the presence of the scatters. The distorted Born approximation is then applied to obtain the correlations of the scattered fields. From the correlation of the scattered field, calculated is the complete set of scattering coefficients for polarimetric radar observation or brightness temperature in passive radiometer applications. In the remote sensing of terrestrial ecosystems, the development of microwave remote sensing technology and the potential of SAR to measure vegetation structure and biomass have increased effort to conduct experimental and theoretical researches on the interactions between microwave and vegetation canopies. The overall objective is to develop inversion algorithms to retrieve biophysical parameters from radar data. In this perspective, theoretical models and experimental data are methodically interconnected in the following manner: Due to the complexity of the interactions involved, all theoretical models have limited domains of validity; the proposed solution is to use theoretical models, which is validated by experiments, to establish the region in which the radar response is most sensitive to the parameters of interest; theoretically simulated data will be used to generate simple invertible models over the region. For applications to the remote sensing of sea ice, the developed theoretical models need to be tested with experimental measurements. With measured ground truth such as ice thickness, temperature, salinity, and structure, input parameters to the theoretical models can be obtained to calculate the polarimetric scattering coefficients for radars or brightness temperature for radiometers and then compare theoretical results with experimental data. Validated models will play an important role in the interpretation and classification of ice in monitoring global ice cover from space borne remote sensors in the future. We present an inversion algorithm based on a recently developed inversion method referred to as the Renormalized Source-Type Integral Equation approach. The objective of this method is to overcome some of the limitations and difficulties of the iterative Born technique. It recasts the inversion, which is nonlinear in nature, in terms of the solution of a set of linear equations; however, the final inversion equation is still nonlinear. The derived inversion equation is an exact equation which sums up the iterative Neuman (or Born) series in a closed form and, thus, is a valid representation even in the case when the Born series diverges; hence, the name Renormalized Source-Type Integral Equation Approach

Monte Carlo simulation model for electromagnetic scattering from vegetation and inversion of vegetation parameters

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 171-185).In this thesis research, a coherent scattering model for microwave remote sensing of vegetation canopy is developed on the basis of Monte Carlo simulations. An accurate model of vegetation structure is essential for the calculation of scattering from vegetations, especially those with closely spaced elements in clusters. The Monte Carlo approach has an advantage over the conventional wave theory in dealing with complex vegetation structures because it is not necessary to find the probability density functions and the pair-distribution functions required in the analytic formulation and usually difficult to obtain for natural vegetation. To achieve a realistic description of the vegetation structure under consideration, two methods may be employed. One method requires the specification of the number of each type of component and the relative orientations of the components. In a structural model which incorporates this method, the detailed features can be preserved to the desired level of accuracy. This structural model is applied to two types of vegetation- --rice crops and sunflowers.(cont.) The developed structural model for rice crops takes into account the coherent wave interactions made prominent by the clustered and closely spaced structure of rice crops, and is validated with the ERS-1 and RADARSAT data. It is utilized to interpret the experimental observations from the JERS-1 data, such as the effects of the structure of rice fields, and to predict the temporal response of rice growth. The structural model developed for sunflowers is validated using the airborne Remote Sensing Campaign Mac-Europe 91 multi-frequency and multi-polarization data acquired for sunflower fields at the Montespertoli test site in Italy. Another method to characterize vegetation structure uses growth rules. This is especially useful in modeling trees, which are structurally more complex. The Lindenmayer systems (L-systems) are utilized to fully capture the architecture of trees and describe their growth. Monte Carlo simulation results of the scattering returns from trees with different structures and at different growth stages are calculated and analyzed. The concept of the "structure factor" which extracts the structural information of a tree and and provides a measure of the spatial distribution of branches is defined, and computed for trees with different architectures.(cont.) After study of the forward scattering problem in which the scattering coefficients are determined on the basis of known physical characteristics of the scattering objects or medium, the inverse scattering problem is considered in which the characteristics of the scattering objects or medium are to be calculated from the scattering data. In this thesis research, neural networks are applied to the inversion of geophysical parameters including soil moisture and surface parameters, sunflower biomass, as well as forest age (or equivalently, forest biomass). They are found to be especially useful for multi-dimensional inputs such as multi-frequency polarimetric scattering data. For the inversion of soil moisture and surface parameters, neural networks are trained with theoretical surface scattering models. To retrieve the sunflower biomass, neural networks are trained with the scattering returns obtained from the developed vegetation scattering model based on the Monte Carlo approach. To assess the performance of the use of experimental data to train the neural networks, the polarimetric radar data acquired by the Spaceborne Imaging Radar-C (SIR-C) over the Landes Forest in France are utilized as the training data to retrieve the forest age. Different combinations of backscattering data are used as input to the neural net in order to determine the combination which yields the best inversion result.by L-i-Fang Wang.Ph.D

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

A Qualitative Study on Microwave Remote Sensing and Challenges Faced in India

Over the past few decades remote sensing has expanded its limits with exponential rise in technology that facilitates accurate data fetching in real time. In view of some of the major problems faced by developing nations, particularly India with its recent advancement in space technology, remote sensing has a vital role to play in resolving many such problems. In the light of recent Global Space Programs where several satellites have been launched for large area mapping using microwave sensors, microwave remote sensing can play a vital role as India experiences a large number of disasters every year. Also, majority of Indian population relies on farming for their livelihood. Microwave remote sensing can have significant effects in both these two scenarios as opposed to its conventional counterpart, optical remote sensing under diverse conditions and facilitate better results in terms of disaster management, prediction and increasing crop yield. The current paper brings out the various details on the work done by using active microwave remote sensing, with specific illustrative examples, for disaster management support, crop management techniques and the challenges associated on carrying out such researches in a diverse terrain like India

Multitemporal Observations of Sugarcane by TerraSAR-X Images

The objective of this study is to investigate the potential of TerraSAR-X (X-band) in monitoring sugarcane growth on Reunion Island (located in the Indian Ocean). Multi-temporal TerraSAR data acquired at various incidence angles (17°, 31°, 37°, 47°, 58°) and polarizations (HH, HV, VV) were analyzed in order to study the behaviour of SAR (synthetic aperture radar) signal as a function of sugarcane height and NDVI (Normalized Difference Vegetation Index). The potential of TerraSAR for mapping the sugarcane harvest was also studied. Radar signal increased quickly with crop height until a threshold height, which depended on polarization and incidence angle. Beyond this threshold, the signal increased only slightly, remained constant, or even decreased. The threshold height is slightly higher with cross polarization and higher incidence angles (47° in comparison with 17° and 31°). Results also showed that the co-polarizations channels (HH and VV) were well correlated. High correlation between SAR signal and NDVI calculated from SPOT-4/5 images was observed. TerraSAR data showed that after strong rains the soil contribution to the backscattering of sugarcane fields can be important for canes with heights of terminal visible dewlap (htvd) less than 50 cm (total cane heights around 155 cm). This increase in radar signal after strong rains could involve an ambiguity between young and mature canes. Indeed, the radar signal on TerraSAR images acquired in wet soil conditions could be of the same order for fields recently harvested and mature sugarcane fields, making difficult the detection of cuts. Finally, TerraSAR data at high spatial resolution were shown to be useful for monitoring sugarcane harvest when the fields are of small size or when the cut is spread out in time. The comparison between incidence angles of 17°, 37° and 58° shows that 37° is more suitable to monitor the sugarcane harvest. The cut is easily detectable on TerraSAR images for data acquired less than two or three months after the cut. The radar signal decreases about 5dB for images acquired some days after the cut and 3 dB for data acquired two month after the cut (VV-37°). The difference in radar signal becomes negligible (<1 dB) between harvested fields and mature canes for sugarcane harvested since three months or more

Electromagnetic Wave Theory and Applications

Contains table of contents for Section 3, reports on six research projects and a list of publications and conference papers.Joint Services Electronics Program Contract DAAL03-89-C-0001National Science Foundation Grant ECS 86-20029Schlumberger- Doll ResearchU.S. Army Research Office Contract DAAL03 88-K-0057U.S. Navy - Office of Naval Research Contract N00014-90-J-1002National Aeronautics and Space Administration Grant NAGW-1617U.S. Navy - Office of Naval Research Grant N00014-89-J-1107National Aeronautics and Space Administration Grant NAGW-1272National Aeronautics and Space Administration Agreement 958461U.S. Army - Corps of Engineers Contract DACA39-87-K-0022U.S. Air Force - Electronic Systems Division Contract F19628-88-K-0013U.S. Navy - Office of Naval Research Grant N00014-89-J-1019Digital Equipment CorporationIBM CorporationU.S. Department of Transportation Contract DTRS-57-88-C-00078Defence Advanced Research Projects Agency Contract MDA972-90-C-002

Recent Advancement of Synthetic Aperture Radar (SAR) Systems and Their Applications to Crop Growth Monitoring

Synthetic aperture radars (SARs) propagate and measure the scattering of energy at microwave frequencies. These wavelengths are sensitive to the dielectric properties and structural characteristics of targets, and less affected by weather conditions than sensors that operate in optical wavelengths. Given these advantages, SARs are appealing for use in operational crop growth monitoring. Engineering advancements in SAR technologies, new processing algorithms, and the availability of open-access SAR data, have led to the recent acceleration in the uptake of this technology to map and monitor Earth systems. The exploitation of SAR is now demonstrated in a wide range of operational land applications, including the mapping and monitoring of agricultural ecosystems. This chapter provides an overview of—(1) recent advancements in SAR systems; (2) a summary of SAR information sources, followed by the applications in crop monitoring including crop classification, crop parameter estimation, and change detection; and (3) summary and perspectives for future application development