Haydee Karszenbaum

Radiometric correction effects in Landsat multi‐date/multi‐sensor change detection studies

Radiometric corrections serve to remove the effects that alter the spectral characteristics of land features, except for actual changes in ground target, becoming mandatory in multi‐sensor, multi‐date studies. In this paper, we evaluate the effects of two types of radiometric correction methods (absolute and relative) for the determination of land cover changes, using Landsat TM and Landsat ETM+ images. In addition, we present an improvement made to the relative correction method addressed. Absolute correction includes a cross‐calibration between TM and ETM+ images, and the application of an atmospheric correction protocol. Relative correction normalizes the images using pseudo‐invariant features (PIFs) selected through band‐to‐band PCA analysis. We present a new algorithm for PIFs selection in order to improve normalization results. A post‐correction evaluation index (Quadratic Difference Index (QD)), and post‐classification and change detection results were used to evaluate the performance of the methods. Only the absolute correction method and the new relative correction method presented in this paper show good post‐correction and post‐classification results (QD index ≈ 0; overall accuracy >80%; kappa >0.65) for all the images used. Land cover change estimations based on uncorrected images present unrealistic change rates (two to three times those obtained with corrected images), which highlights the fact that radiometric corrections are necessary in multi‐date multi‐sensor land cover change analysis.

Mapping wetlands using multi-temporal RADARSAT-1 data and a decision-based classifier

The purpose of this study was to determine the suitability of multi-temporal RADARSAT-1 data and a decision classifier for mapping the Lower Islands of the Paraná Delta wetland in Argentina. The information-extraction strategy was based on identification of the interaction mechanisms occurring between the radar signal and the canopy, considering different vegetation phenology and flood conditions. Such information was used in the design of a decision classifier to obtain a land cover map. In addition, results were compared with those obtained from an iterative optimization clustering procedure (ISODATA algorithm). The quality of the maps obtained was assessed by an error matrix evaluation. The decision classifier was able to discriminate among land cover types with an overall accuracy on the order of 85%, and ISODATA had an overall accuracy of 81%. Two of the available scenes were taken during the high flood of the El Niño event of 1998, and the results obtained also show that RADARSAT-1 data are quite effective not only in delineating the inundation area, but also in identifying the flood condition of each of the land cover types considered. Two main conclusions are derived from this research: (1) the need for multi-temporal SAR data acquired under different environmental conditions for mapping wetlands, and (2) the advantages and flexibility of physically based reasoning classifiers for synthetic aperture radar (SAR) data classification.

Monitoring flood condition in marshes using EM models and Envisat ASAR observations

This paper discusses the contribution of multipolarization radar data in monitoring flooding events in wetland areas of the Delta of the Parana/spl acute/ River, in Argentina. The discussion is based on the comparison between radiative transfer model simulations and ENVISAT Advanced Synthetic Aperture Radar observations of two types of marshes: junco and cortadera. When these marshes are flooded, the radar response changes significantly. The differences in radar response between the flooded and nonflooded condition can be related to changes in the amount of emerged biomass. Based on this, we propose a vegetation-dependent flooding prediction scheme for two marsh structures: nearly vertical cylinders (junco-like) and randomly oriented discs (cortadera-like).

Exploring the capacity of radar remote sensing to estimate wetland marshes water storage.

This paper focuses on the use of radar remote sensing for water storage estimation in wetland marshes of the Paraná River Delta in Argentina. The approach followed is based on the analysis of a temporal set of ENVISAT ASAR data which includes images acquired under different polarizations and incidence angles as well as different environmental conditions (water level, precipitation, and vegetation condition). Two marsh species, named junco and cortadera, were monitored. This overall data set gave us the possibility of studying and understanding the basic interactions between the radar, the soil under different flood conditions, and the vegetation structure. The comprehension of the observed features was addressed through electromagnetic models developed for these ecosystems. The procedure used in this work to estimate water level within marshes combines a direct electromagnetic model, field work data specifically obtained to feed the model, the actual ASAR measurements and a well known retrieval scheme based on a cost function. Results are validated with water level evaluations at specific points. A map showing an estimation of the water storage capacity and its error in junco and cortadera areas for the date where the investigation was done is also presented.

Model investigation about the potential of C band SAR in herbaceous wetlands flood monitoring

Wetlands are areas where the presence of water at or near the soil surface drives the natural system. Imaging radars (SARs) have distinct characteristics which make them of significant value for monitoring and mapping wetland inundation dynamics. The presence or absence of water (which has a much higher dielectric constant than dry or wet soil) in wetlands may significantly alter the signal detected from these areas depending on the dominant vegetation type, density, and height. The objective of this paper is to present our current research efforts to explain and correctly simulate the radar response of wetland vegetation/inundation mixtures, and use simulations as an aid for retrieval applications. The radar response of junco marshes under different flood conditions and vegetation stages is analysed using a set of 13 multipolarization ENVISAT ASAR scenes acquired over the Paraná River Delta marshes during the period 2003–2005. The main aspect of the approach followed is the simulation of SAR wave interactions with vegetation and water, using an adapted and improved version of the EM model developed at Tor Vergata University. The results obtained indicate that with the refined EM model, it is possible to represent with a good accuracy VV and HH SAR responses of junco marshes for a variety of environmental conditions. Further work and data are needed to explain measured HV backscattering. The general agreement obtained between simulations and observations permitted the development of a simple retrieval scheme, and estimates of water level below the canopy were obtained for different environmental conditions. RMS errors of forward simulations and retrievals are reported and discussed.

The Effect of Rain and Flooding Events on AMSR-E Signatures of La Plata Basin, Argentina

The objective of this paper is to describe and explain the effects on selected AMSR-E channels of two strong events, i.e., a rainstorm and a flooding, occurred in the Argentine section of La Plata basin. More specifically, the rainstorm took place within the Chaco region, which is covered by a continuous, moderately dense forest. The flooding affected the terminal part of Parana¿ River. The study is based on monitoring the temporal trends of the polarization indexes at various AMSR-E bands. In the forest, the rainstorm produces an effect on C band channels which is moderate, but well evident. The presence of this effect agrees with model simulations presented in previous papers. In the Parana¿ River, measurements of water level are available. Variations of polarization index at various frequencies are observed in correspondence with variations of water level in four different stations. However, the amount of the effect and the correlation between variables are dependent on the properties of the areas surrounding the stations. The Delta of Parana¿ river, where a land cover map is available, was selected for estimation of fraction of flooded area by using an algorithm available in the literature.

Monitoring Vegetation Moisture Using Passive Microwave and Optical Indices in the Dry Chaco Forest, Argentina

Information about daily variations of vegetation moisture is of widespread interest to monitor vegetation stress and as a proxy to evapotranspiration. In this context, we evaluated optical and passive microwave remote sensing indices for estimating vegetation moisture content in the Dry Chaco Forest, Argentina. The three optical indices analyzed were the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI) and the Normalized Difference Infrared Index (NDII) and, for the microwave region the Frequency Index (FI). All these indices are mainly sensitive to leaf area index (LAI), but NDWI and NDII, and FI are also sensitive to leaf water content (LWC) and Canopy Water Content (CWC) respectively. Using optical and microwave radiative transfer models for the vegetation canopy, we estimated the range of values of LAI, LWC and CWC that can explain both NDWI/NDII and FI observations. Using a combination of simulations and microwave and optical observations, we proposed a two step approach to estimate leaf and canopy moisture content from NDWI, NDII and FI. We found that the short variation of LWC estimated from NDWI and NDII present a dynamic range of values which is difficult to explain from the biophysical point of view, and it is partially related to atmosphere contamination and canopy radiative transfer model limitations. Furthermore, the observed FI short-term variations (~ 8 days) cannot be explained unless significant CWC variations are assumed. The CWC values estimated from FI present a short-term variations possibly related to vegetation hydric stress.

Radiometric corrections and calibration of SAR images

Specific characteristics of SAR images are the result of the imaging radar technique, which produces radiometric and geometric distortions. The main radiometric distortions are due to the spreading loss effect, the non-uniform antenna pattern, possible gain changes, saturation and speckle noise. The main geometric distortions are projection in slant range, foreshortening, layover and shadowing. To remove or reduce the distortions arising from these effects the application of image preprocessing procedures is necessary. This paper presents the procedures for preprocessing ERS/SAR and Radarsat images in terms of the radiometric corrections and calibration. Procedures for estimating the input filtering parameters for speckle reduction are also presented.

L-Band Radar Soil Moisture Retrieval Without Ancillary Information

A radar-only retrieval algorithm for soil moisture mapping is applied to L-band scatterometer measurements from the Aquarius. The algorithm is based on a nonlinear relation between L-band backscatter and volumetric soil moisture and does not require ancillary information on the surface, e.g., land classification, vegetation canopy, surface roughness, etc. It is based on the definition of three limiting cases or end-members: 1) smooth bare soil; 2) rough bare soil; and 3) maximum vegetation condition. In this study, an estimation method is proposed for the end-member parameters that is iterative and only uses space-borne measurements. The major advantages of the algorithm include an analytic formulation (direct inversion possible), and the fact that there is no requirement for ancillary information. Ancillary data often misclassify the surface and the parameterizations linking surface classification to model parameter values are often highly uncertain. The retrieval algorithm is tested using 3 years of space-borne scatterometer observations from the Aquarius/SAC-D. Retrieved soil moisture accuracy is assessed in several ways: comparison of global spatial patterns with other available soil moisture products (two reanalysis modeling products and retrievals based on the Aquarius radiometer), extended triple collocation (ETC) and time series analysis over selected target areas. In general, low bias and standard deviation are observed with levels comparable to independent radiometer-based retrievals. The errors, however, increase across areas with high vegetation density. The results are promising and applicable to forthcoming L-band radar missions such as SMAP-NASA (2015) and SAOCOM-CONAE (2016).

Behavior of multitemporal and multisensor passive microwave indices in Southern Hemisphere ecosystems

This study focused on the time series analysis of passive microwave and optical satellite data collected from six Southern Hemisphere ecosystems in Australia and Argentina. The selected ecosystems represent a wide range of land cover types, including deciduous open forest, temperate forest, tropical and semiarid savannas, and grasslands. We used two microwave indices, the frequency index (FI) and polarization index (PI), to assess the relative contributions of soil and vegetation properties (moisture and structure) to the observations. Optical-based satellite vegetation products from the Moderate Resolution Imaging Spectroradiometer were also included to aid in the analysis. We studied the X and Ka bands of the Advanced Microwave Scanning Radiometer-EOS and Wind Satellite, resulting in up to four observations per day (1:30, 6:00, 13:30, and 18:00 h). Both the seasonal and hourly variations of each of the indices were examined. Environmental drivers (precipitation and temperature) and eddy covariance measurements (gross ecosystem productivity and latent energy) were also analyzed. It was found that in moderately dense forests, FI was dependent on canopy properties (leaf area index and vegetation moisture). In tropical woody savannas, a significant regression (R2) was found between FI and PI with precipitation (R2 > 0.5) and soil moisture (R2 > 0.6). In the areas of semiarid savanna and grassland ecosystems, FI variations found to be significantly related to soil moisture (R2 > 0.7) and evapotranspiration (R2 > 0.5), while PI varied with vegetation phenology. Significant differences (p < 0.01) were found among FI values calculated at the four local times.