L-band SAR Research Articles

Sinking cities in Indonesia: ALOS PALSAR detects rapid subsidence due to groundwater and gas extraction

We use interferometric synthetic aperture radar (InSAR) time-series analysis of ALOS L-band SAR data to resolve land subsidence in western Indonesia with high spatial and temporal resolution. The data reveal significant subsidence in nine areas, including six major cities, at rates up to 22cm/year. Land subsidence is detected near Lhokseumawe, in Medan, Jakarta, Bandung, Blanakan, Pekalongan, Bungbulang, Semarang, and in the Sidoarjo regency. The fastest subsidence occurs in highly populated coastal areas particularly vulnerable to flooding.We correlate the observed subsidence with surface geology and land use. Despite the fact that subsidence is taking place in compressible deposits there is no clear correlation between subsidence and surface geology. In urban areas we find a correlation between rapid, patchy subsidence and industrial land use and elsewhere with agricultural land use. This suggests that the subsidence is primarily caused by ground water extraction for industrial and agricultural use, respectively. We also observe subsidence associated with exploitation of gas fields near Lhokseumawe and in the Sidoarjo regency. A continuation of these high rates of subsidence is likely to put much of the densely populated coastal areas below relative sea level within a few decades.

Landcover classification of the Lower Nhecolândia subregion of the Brazilian Pantanal Wetlands using ALOS/PALSAR, RADARSAT-2 and ENVISAT/ASAR imagery

The Lower Nhecolândia subregion of the Brazilian Pantanal is part of a large continuous tropical wetland that exhibits a high biodiversity of flora and fauna species, and many threatened habitats. The spatial distribution of these habitats influence the abundance and interactions of animal species, and the change or destruction of habitat can cause the disturbance of key biological processes. This study uses multi-temporal L-band ALOS/PALSAR, C-band RADARSAT-2, and ENVISAT/ASAR data to map ecosystems and create a lake distribution map of the Lower Nhecolândia subregion in the Brazilian Pantanal. First, backscattering analysis was conducted on individual training objects to gain a better understanding of the backscattering characteristics of each class. Then, a Level 1 object-based image analysis (OBIA) classification based on hierarchical principles first classified the region into “Lakes” and “Not Lakes”. This was followed by a Level 2 classification defining six vegetation habitats (Forest Woodland, Open Wood Savanna, Open Grass Savanna, Agriculture, Swampy Grassland and Vazantes) which was achieved at an overall accuracy of 83%. A Level 3 classification defined the “Lakes” class into a) Fresh (baías) and Brackish (salinas) lakes (accuracy results of 98%); and a further classification level dividing the fresh lakes, b) Fresh Lakes with floating and emergent vegetation (baías), and Fresh Lakes with the presence of Typha (salobras), and including the Brackish lakes (salinas) (overall accuracy results of 81%). The results of this study provide the first fine spatial resolution classification showing the spatial distribution of terrestrial and aquatic habitats for the entire subregion of Lower Nhecolândia using dual season, dual polarization C and L-band SAR imagery. The produced maps will provide valuable habitat information to help define conservation strategies and aid further research in the area.

The comprehensive deformation retrieval for SAR interferometric data

This study presents a comprehensive deformation retrieval method based on the multi-baseline synthetic aperture radar interferometry (InSAR) technique for ground movement monitoring. Both the displacement pattern in at the large scale and detailed deformation for local areas are investigated by exploiting two sets of data generated at low and full spatial resolution. In particular, we focus on the description and estimation of non-linear deformation in full-resolution signals. A polynomial parameter model is built and the total least squares-singular value decomposition algorithm is proposed to control the overall errors of the system. The ascending Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR) data set on Tianjin China is used for the algorithm experiment and detailed displacement information between July 2008 and August 2010 is obtained. The experimental results are validated by levelling data and field surveys, and the good agreement proves the effectiveness of the algorithm.

Mapping evergreen forests in the Brazilian Amazon using MODIS and PALSAR 500-m mosaic imagery

In this study, we evaluate a methodology that uses dual-polarization L-band SAR 500-m mosaic PALSAR imagery to identify and map forests in the Brazilian Amazon and an algorithm that uses time-series MODIS imagery to map evergreen forest. IKONOS images were used to evaluate forest maps derived from PALSAR and MODIS imagery. A comparison between the PALSAR forest map and IKONOS forest maps shows that 91.4% of PALSAR-derived forest pixels had greater than 60% IKONOS-derived forest area. We also compared the PALSAR-derived forest map with the MODIS-derived evergreen forest map. Out of 1999 evergreen forest pixels in the MODIS evergreen forest map (the areas covered by the 11 IKONOS imagery), 1934 pixels were identified as forest by the PALSAR forest map, approximately 96.7% agreement. The results of this study highlight the potential of combining PALSAR and MODIS data for identifying and mapping evergreen forests in the Amazon.

Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa

The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset.

Estimation of the Repeat-Pass ALOS PALSAR Interferometric Baseline Through Direct Least-Square Ellipse Fitting

The precise estimation of the baseline is a crucial procedure in repeat-pass interferometric synthetic aperture radar (InSAR) applications. Using the ephemeris of the satellite, a polynomial regression algorithm can fit the satellite orbit at the third or higher order with a main shortcoming that the mutual constraints among the three dimensions defining the orbit are missed. In this paper, a new approach is presented to fit the satellite orbit based on the assumption that the satellite orbit is a 3-D ellipse, which retains the relations among the three dimensions. Considering the complexity of 3-D ellipse parameters estimation, the 3-D orbit is first transformed into three 2-D ellipses. Then, the parameters of these 2-D ellipses are estimated with a direct least-square ellipse fitting method (DLS-EFM). These two orbit fitting algorithms are tested with ten sets of advanced land observation satellite phased array L-band SAR data, which were acquired in north Toronto, Ontario, Canada, from September, 2008 to January, 2009. Moreover, two of them acquired with an adjacent period were chosen to form a repeat-pass InSAR, and the corresponding baseline is calculated with the proposed method as an example. The experimental results show that the error of the satellite position using DLS-EFM is at a submetric level, which is less than one-tenth of that of the polynomial regression algorithm. Consequently, the proposed method is appropriate for the baseline estimation in spaceborne InSAR applications.

Four-Component Scattering Power Decomposition Algorithm with Rotation of Covariance Matrix Using ALOS-PALSAR Polarimetric Data

The present study introduces the four-component scattering power decomposition (4-CSPD) algorithm with rotation of covariance matrix, and presents an experimental proof of the equivalence between the 4-CSPD algorithms based on rotation of covariance matrix and coherency matrix. From a theoretical point of view, the 4-CSPD algorithms with rotation of the two matrices are identical. Although it seems obvious, no experimental evidence has yet been presented. In this paper, using polarimetric synthetic aperture radar (POLSAR) data acquired by Phased Array L-band SAR (PALSAR) on board of Advanced Land Observing Satellite (ALOS), an experimental proof is presented to show that both algorithms indeed produce identical results.

Monitoring of active tectonic deformations in the Longitudinal Valley (Eastern Taiwan) using Persistent Scatterer InSAR method with ALOS PALSAR data

This paper presents new observation of the interseismic deformation along the Longitudinal Valley (Eastern Taiwan) that represents a major tectonic boundary of the Taiwan collision zone. We investigate the southern part of the Valley from Rueisuei to Taitung (latitude 23.5°N–22.7°N), which is the part of the Valley where interseismic surface creep has already been observed at some points of the Longitudinal Valley Fault (LVF). A Persistent Scatterer SAR interferometry approach (StaMPS) is applied using ten L-band SAR images from ALOS satellite acquired over the period 2007–2010. Interferograms from L-Band data show a dramatic improvement of coherence in comparison to previous studies using C-Band ERS data. The density of measurement resulting from StaMPS processing is the highest achieved so far in the area (about 40–55 points per km2 for a total of 77,000 points) allowing a continuous view of the deformation along the Valley and also giving information on its borders (Central Range and Coastal Range). The most striking feature of the resulting mean velocity map is a clear velocity discontinuity localized in a narrow band (0.1–1km) along the LVF and responsible for up to 3cm/yr velocity offset along the radar line of sight, which is attributed to shallow interseismic creep. InSAR results are in good agreement with continuous GPS measurements over the same period (0.3cm/yrrms). The density of measurement allows us to improve fault trace map along the creeping section of the LVF (with accuracy of about 100m) and to find new field evidences of the fault activity. In some places, our trace differs significantly (hundreds of meters) from previous published traces. The creep rate shows significant variations along the fault. At the southern end of the valley the deformation is distributed on several structures, including the Luyeh Strand, and drops significantly south of the Peinanshan. However there are discrepancies with previous studies made from ERS data over the period 1993–1999 that remain to be investigated. The mean velocity for each point of measure and the improved faults' trace are provided as Supplementary data.

Interaction between permafrost and infrastructure along the Qinghai–Tibet Railway detected via jointly analysis of C- and L-band small baseline SAR interferometry

The Qinghai–Tibet Railway (QTR) is the highest and longest plateau linear structure in the world. The embankment is prone to instability, primarily due to the freezing and thawing of the permafrost overlying active layer. The infrastructure construction changes environmental conditions and in turn has influenced permafrost evolutions. In this study, we have investigated the feasibility of extracting surface motions in permafrost regions by means of satellite SAR interferometry. The relationship between surface deformation, permafrost active layer and other environmental conditions were further analyzed. In total, 19 L-band ALOS PALSAR SLC images (acquired from 21 June 2007 to 30 December, 2010) and 38 C-band Envisat ASAR SLC images (acquired from 18 November 2004 to 17 December, 2009) were employed to cover the Beiluhe experimental site, Qinghai, China. A hybrid persistent scatterer interferometry model based on interferometric point target analysis (IPTA) and small baseline strategy was firstly developed for surface motion estimation, minimizing the seasonal decorrelation effect. Then, the results from C- and L-band data were analyzed and compared, based on a statistical calibration model. Ground leveling data acquired from Fenghuo Mount Tunnel front with a one month interval were used for InSAR-derived results validation, revealing good agreement in displacement rates as well as time series. The QTR has been in operation since 2006. Our study has observed distinct surface motions along the embankment, primarily in the range of −20 to +20mm/yr, implying the necessity of continued satellite-based surveillance along the QTR.

Analysis of Urban Areas Affected by the 2011 Off the Pacific Coast of Tohoku Earthquake and Tsunami With L-Band SAR Full-Polarimetric Mode

The 2011 off the Pacific coast of Tohoku earthquake was observed using phased array type L-band synthetic aperture radar (PALSAR) and polarimetric and interferometric airborne synthetic aperture radar (PiSAR) full-polarimetric data. Representative polarimetric parameters were calculated from full-polarimetric data for urban areas, where most of the buildings were destroyed by the subsequent tsunami, in order to identify the radar scattering mechanism in these areas. These parameters were compared with the ones observed before the disaster. The full-polarimetric data analysis shows that the affected areas were represented by surface scattering with high entropy, indicating that a complex scattering mechanism with nonreflection symmetry is involved. The coherence between HH and VV and that between RR and LL are the most important factors in distinguishing the disaster areas from the data. Alpha angle and anisotropy are also important factors in this respect; however, anisotropy derived from PiSAR data does not show the difference between areas with collapsed and still-standing buildings. This may be because the azimuth slope angle for the target urban area is different before and after the disaster for both PALSAR and PiSAR data. Owing to the double-bounce scattering from azimuthally rotated targets in the urban areas, the power estimated from the four-component decomposition model is distributed within a wide range not only for double-bounce scattering but also for volume and surface scatterings. Additionally, the model does not show a systematic change between before and after the disaster, and σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> for four polarizations with 30-m resolution does not show a systematic difference.

Potential of texture measurements of two-date dual polarization PALSAR data for the improvement of forest biomass estimation

The recently available space-borne SAR sensor, PALSAR, is more promising than its predecessor JERS-1 for biomass estimation because of its long wavelength (L-band), and its ability to provide data with different polarizations, varying incidence angles and higher spatial resolutions. This research investigates the potential of two-date dual polarization (HH and HV) SAR imagery for biomass estimation using different kinds of texture processing and different combinations of single and dual polarization ratios. The investigation is conducted in a mountainous, sub-tropical study area where biomass levels are far beyond the previously recognized saturation levels for L-band SAR images, and forest is a mixture of native and non-native species and plantations.We analyzed two-date SAR data with four steps of image processing, including raw data processing in various combinations, texture measurement parameters of HH and HV polarizations, texture measurement parameters of HH and HV together (both jointly and as a ratio), and a ratio of two-date texture parameters along with a single and two-date ratio. When the processed images were compared with ground data from 50 plots, the performance from raw data processing was low, with adjusted r2=0.22, but after all four processing steps, promising model accuracy (adjusted r2=0.90 and RMSE=28.58t/ha) and validation accuracy (using the Leave-One-Out-Cross-Validation) with adjusted r2=0.88 and RMSE=35.69t/ha, were achieved from the combination of single- and two-date polarization ratios of texture parameters.The strong performance achieved indicates that L-band dual-polarization (HH and HV) SAR data from PALSAR has great potential for biomass estimation, far beyond the previously reported L-band saturation point for biomass. This result is attributed to the synergy among texture processing and dual polarization on the one hand, which were able to average out random speckle noise, and the use of ratio instead of absolute quantities, due to its well known ability to reduce forest structural and terrain effects. The additional use of two-date SAR data with these processing techniques was able to add complementary information derived from biomass response in both wet and dry seasons. Thus overall, undesirable image noise and terrain effects were reduced.

Patterns of Aboveground Biomass Regeneration in Post-Fire Coastal Scrub Communities

An evaluation of ALOS PALSAR (Phased Array type L-band Synthetic Aperture Radar) data for shrub height and aboveground biomass (AGB) estimation has been performed in scrub-dominated ecosystems of central coastal California. Comparison between AGB field measurements and SAR estimations showed a correlation coefficient of R2 = 0.53 for the HV polarization. Post-fire AGB regeneration was examined two years after the Big Sur Basin Complex fire of 2008. In 2009, the average patch size of AGB density classes between 20-40 Mg ha-1 and 50-70 Mg ha-1 was 0.49 ha and 0.09 ha, respectively. In 2010, patch sizes in these same two AGB density classes increased to averages of 0.8 ha to 0.15 ha, respectively. No strong saturation in the SAR-AGB relationship was found, which indicated the potential for more advanced applications of L-band SAR data for coastal ecosystem AGB mapping.

Fire Damage Assessment in Sardinia: the use of ALOS/PALSAR data for post fire effects management

Fires in the Sardinia Island are one of the most important environmental factors controlling the ecosystem's function and structure. The evaluation of fire effects by means of remote sensing is economically and practically the best way to assess fire damage, before going to the field. The use of alternative techniques for fire effects assessment is needed, in particular to characterize the biomass loss at the regional level. Radar remotely sensed data can provide great advantages with respect to optical sensors. The paper is devoted to show the results obtained by applying a semi-automatic algorithm to the images of the L-band SAR sensor PALSAR, on board of the ALOS satellite, for the estimate of the burned area. To assess the quality of the estimate, the radar based results have been compared with those obtained from optical data and ground based information.

ASSESSMENT OF L-BAND SAR DATA AT DIFFERENT POLARIZATION COMBINATIONS FOR CROP AND OTHER LANDUSE CLASSIFICATION

In the present study evaluation of L-band SAR data at difierent polarization combinations in linear, circular as well as hybrid polarimetric imaging modes for crop and other landuse classiflcations has been carried out. Full-polarimetric radar data contains all the scattering information for any arbitrary polarization state, hence data of any combination of transmit and receive polarizations can be synthesized, mathematically from full-polarimetric data. Circular and various modes of hybrid polarimetric data (where the transmitter polarization is either circular or orientated at 45 - , called …=4 and the receivers are at horizontal and vertical polarizations with respect to the radar line of sight) were synthesized (simulated) from ALOS- PALSAR fullpolarimetric data of 14th December 2008 over central state farm central latitude and longitude 29 - 15 0 N/75 - 43 0 E and bounds for northwest corner is 29 - 24 0 N/75 - 37 0 E and southeast corner is 29 - 07 0 N/75 - 48 0 E in Hisar, Haryana (India) Supervised classiflcation was conducted for crops and few other landuse classes based on ground truth measurements using maximum-likelihood distance measures derived from the complex Wishart distribution of SAR data at various polarization combinations. It has been observed that linear full- polarimetric data showed maximum classiflcation accuracy (92%) followed by circular-full (89%) and circular-dual polarimetric data (87%), which was followed by hybrid polarimetric data (73{75%) and then linear dual polarimetric data (63{71%). Among the linear dual polarimetric data, co-polarization complex data showed better

Discrimination of coastal wetland environments in the Amazon region based on multi-polarized L-band airborne Synthetic Aperture Radar imagery

This study assessed the use of multi-polarized L-band images for the identification of coastal wetland environments in the Amazon coast region of northern Brazil. Data were acquired with a SAR R99B sensor from the Amazon Surveillance System (SIVAM) on board a Brazilian Air Force jet. Flights took place in the framework of the 2005 MAPSAR simulation campaign, a German-Brazilian feasibility study focusing on a L-band SAR satellite. Information retrieval was based on the recognition of the interaction between a radar signal and shallow-water morphology in intertidal areas, coastal dunes, mangroves, marshes and the coastal plateau. Regarding the performance of polarizations, VV was superior for recognizing intertidal area morphology under low spring tide conditions; HH for mapping coastal environments covered with forest and scrub vegetation such as mangrove and vegetated dunes, and HV was suitable for distinguishing transition zones between mangroves and coastal plateau. The statistical results for the classification maps expressed by kappa index and general accuracy were 83.3% and 0.734 for the multi-polarized color composition (R-HH, G-HV, B-VV), 80.7% and 0.694% for HH, 79.7% and 0.673% for VV, and 77.9% and 0.645% for HV amplitude image. The results indicate that use of multi-polarized L-band SAR is a valuable source of information aiming at the identification and discrimination of distinct geomorphic targets in tropical wetlands.

Movement estimate of the Dongkemadi Glacier on the Qinghai–Tibetan Plateau using L-band and C-band spaceborne SAR data

We measured the complex motion of the Dongkemadi Glacier on Tanggula Mountain, Qinghai–Tibetan Plateau, using two-pass differential synthetic aperture radar interferometry (InSAR) with satellite L-band and C-band SAR data. We derived detailed motion patterns of the Dongkemadi Glacier for the winter seasons of 1996, 2007 and 2008 using a European Remote sensing Satellite-1/2 (ERS-1/2) tandem InSAR pair acquired from descending orbit and a 46-day-separation Advanced Land Observing Satellite (ALOS) InSAR pair acquired from ascending orbit. In this article, we focus on an analysis of the glacier's surface motion features and a validation of the results from the InSAR using Global Positioning System (GPS) survey data. The experimental results show that the glacier flow distribution displays strong spatial variations depending on elevation. The glacier is divided into four clearly defined fast-flowing units in terms of spatial variability of the glacier speed, with evidence from both ERS and ALOS/PALSAR InSAR pairs (palsar – Phased Array type L-band Synthetic Aperture Radar). Among the four fast-flowing units, three are on the Dadongkemadi Glacier (DDG) and one on the Xiaodongkemadi Glacier (XDG). The flow patterns are generally characterized by terrain complexity for both glacier branches. The upper central area of the DDG shows slow movement, maybe due to the convergent and uptaking effect of ice from steep slope areas with opposite flow directions.

A novel super-resolution method of PolSAR images based on target decomposition and polarimetric spatial correlation

The polarimetric synthetic aperture radar (PolSAR) is becoming more and more popular in remote-sensing research areas. However, due to system limitations, such as bandwidth of the signal and the physical dimension of antennas, the resolution of PolSAR images cannot be compared with those of optical remote-sensing images. Super-resolution processing of PolSAR images is usually desired for PolSAR image applications, such as image interpretation and target detection. Usually, in a PolSAR image, each resolution contains several different scattering mechanisms. If these mechanisms can be allocated to different parts within one resolution cell, details of the images can be enhanced, which that means the resolution of the images is improved. In this article, a novel super-resolution algorithm for PolSAR images is proposed, in which polarimetric target decomposition and polarimetric spatial correlation are both taken into consideration. The super-resolution method, based on polarimetric spatial correlation (SRPSC), can make full use of the polarimetric spatial correlation to allocate different scattering mechanisms of PolSAR images. The advantage of SRPSC is that the phase information can be preserved in the processed PolSAR images. The proposed methods are demonstrated with the German Aerospace Center (DLR) Experimental SAR (E-SAR) L-band full polarized images of the Oberpfaffenhofen Test Site Area in Germany, obtained on 30 September 2000. The experimental results of the SRPSC confirms the effectiveness of the proposed methods.1

Crop Classification Using Short-Revisit Multitemporal SAR Data

Classification of crops and other land cover types is an important application of both optical/infrared and SAR satellite data. It is already an import application of present satellite systems, as it will be for planned missions, such as the Sentinels. An airborne SAR data set with a short revisit time acquired by the German ESAR system during the ESA-campaign, AgriSAR 2006, has been used to assess the performance of different polarization modes for crop classification. Both C-and L-band SAR data were acquired over the Demmin agricultural test site in North Eastern Germany on a weekly basis during the growing season. Single-and dual-polarization, and fully polarimetric data have been used in the analysis (fully polarimetric data were only available at L-band). The main results of the analysis are, that multitemporal acquisitions are very important for single-and dual-polarization modes, and that cross-polarized backscatter produces the best results, with errors down to 3%-6% at the two frequencies. There is a trade-off between the polarimetric information and the multitemporal information, where the best overall results are obtained using the multitemporal information. If only a few acquisitions are available, the polarimetric mode may perform better than the single-and dual polarization modes.

Dense Temporal Series of C- and L-band SAR Data for Soil Moisture Retrieval Over Agricultural Crops

This paper investigates the potential of multi-temporal C- and L-band SAR data, acquired within a short revisiting time (1-2 weeks), to map temporal changes of surface soil moisture content (m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</sub> ) underneath agricultural crops. The analysed data consist of a new ground and SAR data set acquired on a weekly basis from late April to early August 2006 over the DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) agricultural site (Northern Germany) during the European Space Agency 2006 AgriSAR campaign. The paper firstly investigates the main scattering mechanisms characterizing the interaction between the SAR signal and crops, such as winter wheat and rape. Then, the relationship between backscatter and soil moisture content temporal changes as a function of different SAR bands and polarizations is studied. Observations indicate that rationing of the multi-temporal radar backscatter can be a simple and effective way to decouple the effect of vegetation and surface roughness from the effect of soil moisture changes, when volume scattering is not dominant. The study also assesses to which extent changes in the incidence angle between subsequent radar acquisitions may affect the radar sensitivity to soil moisture content. Finally, an algorithm based on the change detection technique retrieving superficial soil moisture content is proposed and assessed both on simulated and experimental data. Results indicate that for crops relatively insensitive to volume scattering in the vegetation canopy (as for instance winter wheat at C-band or winter rape and winter wheat at L-band), m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</sub> can be retrieved during the whole growing season, with accuracies ranging between 5% and 6% [m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ]. We also show that low incidence angles (e.g., 20-35 ) and HH polarization are generally better suited to m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</sub> retrieval than VV polarization and higher incidence angles.

Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use

In the context of reducing emissions from deforestation and forest degradation (REDD) and the international effort to reduce anthropogenic greenhouse gas emissions, a reliable assessment of aboveground forest biomass is a major requirement. Especially in tropical forests which store huge amounts of carbon, a precise quantification of aboveground biomass is of high relevance for REDD activities. This study investigates the potential of X- and L-band SAR data to estimate aboveground biomass (AGB) in intact and degraded tropical forests in Central Kalimantan, Borneo, Indonesia. Based on forest inventory data, aboveground biomass was first estimated using LiDAR data. These results were then used to calibrate SAR backscatter images and to upscale the biomass estimates across large areas and ecosystems. This upscaling approach not only provided aboveground biomass estimates over the whole biomass range from woody regrowth to mature pristine forest but also revealed a spatial variation due to varying growth condition within specific forest types. Single and combined frequencies, as well as mono- and multi-temporal TerraSAR-X and ALOS PALSAR biomass estimation models were analyzed for the development of accurate biomass estimations. Regarding the single frequency analysis overall ALOS PALSAR backscatter is more sensitive to AGB than TerraSAR-X, especially in the higher biomass range (> 100 t/ha). However, ALOS PALSAR results were less accurate in low biomass ranges due to a higher variance. The multi-temporal L- and X-band combined model achieved the best result and was therefore tested for its temporal and spatial transferability. The achieved accuracy for this model using nearly 400 independent validation points was r² = 0.53 with an RMSE of 79 t/ha. The model is valid up to 307 t/ha with an accuracy requirement of 50 t/ha and up to 614 t/ha with an accuracy requirement of 100 t/ha in flat terrain. The results demonstrate that direct biomass measurements based on the synergistic use of L- and X-band SAR can provide large-scale AGB estimations for tropical forests. In the context of REDD monitoring the results can be used for the assessment of the spatial distribution of the biomass, also indicating trends in high biomass ranges and the characterization of the spatial patterns in different forest types.