Rice monitoring using ENVISAT-ASAR data: preliminary results of a case study in the Mekong River Delta, Vietnam

Vietnam is one of the world’s largest rice exporting countries, and the fertile Mekong River Delta at the southern tip of Vietnam accounts for more than half of the country’s rice production. Unfortunately, a large part of rice crop growing time coincides with a rainy season, resulting in a limited number of cloud-free optical remote sensing images for rice monitoring. Synthetic aperture radar (SAR) data allows for observations independent of weather conditions and solar illumination, and is potentially well suited for rice crop monitoring. The aim of the study was to apply new generation Envisat ASAR data with dual polarization (HH and VV) to rice cropping system mapping and monitoring in An Giang province, Mekong River Delta. Several sample areas were established on the ground, where selected rice parameters (e.g. rice height and biomass) are periodically being measured over a period of 12 months. A correlation analysis of rice parameters and radar imagery values is then being conducted to determine the significance and magnitude of the relationships. This paper describes a review of the previous research studies on rice monitoring using SAR data, the context of this on-going study, and some preliminary results that provide insights on how ASAR imagery could be useful for rice crop monitoring. More work is being done to develop algorithms for mapping and monitoring rice cropping systems, and to validate a rice yield prediction model for one year cycle using time-series SAR imagery

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF

Robust algorithm for detecting floodwater in urban areas using Synthetic Aperture Radar images

Flooding is a major hazard in both rural and urban areas worldwide, but it is in urban areas that the impacts are most severe. High resolution Synthetic Aperture Radar (SAR) sensors are able to detect flood extents in urban areas during both day- and night-time. If obtained in near real-time, these flood extents can be used for emergency flood relief management or as observations for assimilation into flood forecasting models. In this paper a method for detecting flooding in urban areas using near real-time SAR data is developed and extensively tested under a variety of scenarios involving different flood events and different images. The method uses a SAR simulator in conjunction with LiDAR data of the urban area to predict areas of radar shadow and layover in the image caused by buildings and taller vegetation. Of the urban water pixels visible to the SAR, the flood detection accuracy averaged over the test examples was 83%, with a false alarm rate of 9%. The results indicate that flooding can be detected in the urban area to reasonable accuracy, but that this accuracy is limited partly by the SAR’s poor visibility of the urban ground surface due to shadow and layover

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

Program planning, chapter 5, part D

Recommendations for future activities necessary to support satellite microwave sensing are reported. Areas covered include component development, data processing, calibration, design and fabrication of multifrequency systems, and experimental test programs to establish interactions of electromagnetic waves and sensed parameters

Earth resources: A continuing bibliography with indexes (issue 62)

This bibliography lists 544 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1 and June 30, 1989. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Investigation of antenna pattern constraints for passive geosynchronous microwave imaging radiometers

Progress by investigators at Georgia Tech in defining the requirements for large space antennas for passive microwave Earth imaging systems is reviewed. In order to determine antenna constraints (e.g., the aperture size, illumination taper, and gain uncertainty limits) necessary for the retrieval of geophysical parameters (e.g., rain rate) with adequate spatial resolution and accuracy, a numerical simulation of the passive microwave observation and retrieval process is being developed. Due to the small spatial scale of precipitation and the nonlinear relationships between precipitation parameters (e.g., rain rate, water density profile) and observed brightness temperatures, the retrieval of precipitation parameters are of primary interest in the simulation studies. Major components of the simulation are described as well as progress and plans for completion. The overall goal of providing quantitative assessments of the accuracy of candidate geosynchronous and low-Earth orbiting imaging systems will continue under a separate grant

Monsoon Flooding Response: a Multi-scale Approach to Water-extent Change Detection

This paper has the aim of illustrating an automatic and speditive way for retrieving inundation extent from multispectral and multitemporal satellite data, together with land-cover changes caused by flooding events, which is a fundamental issue for managing a reconstruction plan after the event. A straightforward method to map inundated areas was applied in the North-Eastern region of Bangladesh, heavily struck by monsoonal rains in September 2000. This method in based on the Principal Components Transform (PCT) of multispectral satellite data, in its Spectral-Temporal implementation, followed by logical filtering and image segmentation, in order to reach the needed coherency of the results. The use of multiresolution data (28.5-meters ground resolution Landsat-7/ETM+ and 1,100-meters ground resolution NOAA-14/AVHRR) makes possible to evaluate hazard affected areas at different scales. Comparison to RADARSAT-derived water extension maps assessed an Overall Accuracy between 86.4% (for the flood map derived with NOAA-14/AVHRR data over the whole Bangladesh) and 90.6% (for the flood map derived with Landsat-7/ETM+ data over the North-East part of the country)

Precipitation observations from high frequency spaceborne polarimetric synthetic aperture radar and ground-based radar: theory and model validation

2010 Fall.Includes bibliographical references.Global weather monitoring is a very useful tool to better understand the Earth's hydrological cycle and provide critical information for emergency and warning systems in severe cases. Developed countries have installed numerous ground-based radars for this purpose, but they obviously are not global in extent. To address this issue, the Tropical Rainfall Measurement Mission (TRMM) was launched in 1997 and has been quite successful. The follow-on Global Precipitation Measurement (GPM) mission will replace TRMM once it is launched. However, a single precipitation radar satellite is still limited, so it would be beneficial if additional existing satellite platforms can be used for meteorological purposes. Within the past few years, several X-band Synthetic Aperture Radar (SAR) satellites have been launched and more are planned. While the primary SAR application is surface monitoring, and they are heralded as "all weather'' systems, strong precipitation induces propagation and backscatter effects in the data. Thus, there exists a potential for weather monitoring using this technology. The process of extracting meteorological parameters from radar measurements is essentially an inversion problem that has been extensively studied for radars designed to estimate these parameters. Before attempting to solve the inverse problem for SAR data, however, the forward problem must be addressed to gain knowledge on exactly how precipitation impacts SAR imagery. This is accomplished by simulating storms in SAR data starting from real measurements of a storm by ground-based polarimetric radar. In addition, real storm observations by current SAR platforms are also quantitatively analyzed by comparison to theoretical results using simultaneous acquisitions by ground radars even in single polarization. For storm simulation, a novel approach is presented here using neural networks to accommodate the oscillations present when the particle scattering requires the Mie solution, i.e., particle diameter is close to the radar wavelength. The process of transforming the real ground measurements to spaceborne SAR is also described, and results are presented in detail. These results are then compared to real observations of storms acquired by the German TerraSAR-X satellite and by one of the Italian COSMO-SkyMed satellites both operating in co-polar mode (i.e., HH and VV). In the TerraSAR-X case, two horizontal polarization ground radars provided simultaneous observations, from which theoretical attenuation is derived assuming all rain hydrometeors. A C-band fully polarimetric ground radar simultaneously observed the storm captured by the COSMO-SkyMed SAR, providing a case to begin validating the simulation model. While previous research has identified the backscatter and attenuation effects of precipitation on X-band SAR imagery, and some have noted an impact on polarimetric observations, the research presented here is the first to quantify it in a holistic sense and demonstrate it using a detailed model of actual storms observed by ground radars. In addition to volumetric effects from precipitation, the land backscatter is altered when water is on or near the surface. This is explored using TRMM, Canada's RADARSAT-1 C-band SAR and Level 3 NEXRAD ground radar data. A weak correlation is determined, and further investigation is warranted. Options for future research are then proposed

Variable source areas, soil moisture and active microwave observations at Zwalmbeek and Coët-Dan

This chapter focuses on recent research to identify variable source areas from surface soil moisture dynamics observed at the catchment scale by means of active microwave images from satellites. It is hypothesized that variable source areas can be mapped if we can quantify the temporal variability of the surface soil moisture content. It is difficult to determine the soil moisture content from single synthetic aperture radar images because soil moisture, surface roughness, topography and vegetation all have a great effect on radar backscatter. However, seasonal soil moisture fluctuations can be studied by using multitemporal radar images. Two multitemporal image processing techniques are presented to map variable source areas. The first method computes the temporal standard deviation of radar backscatter. This leads to the definition of the so-called saturation potential index, which compares well with observed saturated areas. The second method makes use of a principal component analysis to separate the dominant effects, like topography, land use and soil moisture, on total radar backscatter. Again this leads to reliable mapping of the spatial patterns of variable source areas. Two humid catchments, the Zwalmbeek in Belgium and the Coët-Dan in France, are used in this study