Use Of Synthetic Aperture Radar Data Research Articles

Estimating vegetation indices and biophysical parameters for Central European temperate forests with Sentinel-1 SAR data and machine learning

ABSTRACT Current vegetation indices and biophysical parameters derived from optical satellite data for forest monitoring are widely used in various applications but can be limited by atmospheric effects like clouds. Synthetic aperture radar (SAR) data can offer insightful and systematic forest monitoring with complete time series due to signal penetration through clouds and day and night image acquisitions. This study explores the use of SAR data, combined with ancillary data and machine learning (ML), to estimate forest parameters typically derived from optical satellites. It investigates whether SAR signals provide sufficient information for the accurate estimation of these parameters, focusing on two spectral vegetation indices (Normalized Difference Vegetation Index – NDVI and Enhanced Vegetation Index – EVI) and two biophysical parameters (Leaf Area Index – LAI and Fraction of Absorbed Photosynthetically Active Radiation – FAPAR) in healthy and disturbed temperate forests in Czechia and Central Europe in 2021. Vegetation metrics derived from Sentinel-2 multispectral data were used to evaluate the results. A paired multi-modal time-series dataset was created using Google Earth Engine (GEE), including temporally and spatially aligned Sentinel-1, Sentinel-2, DEM-based features and meteorological variables, along with a forest type class. The inclusion of DEM-based auxiliary features and additional meteorological information improved the results. In the comparison of ML models, the traditional ML algorithms, Random Forest Regressor and Extreme Gradient Boosting (XGB) slightly outperformed the Automatic Machine Learning (AutoML) approach, auto-sklearn, for all forest parameters, achieving high accuracies (R2 between 70% and 86%) and low errors (0.055–0.29 of mean absolute error). XGB was the most computationally efficient. Moreover, SAR-based estimations over Central Europe achieved comparable results to those obtained in testing within Czechia, demonstrating their transferability for large-scale modeling. A key advantage of the SAR-based vegetation metrics is the ability to detect abrupt forest changes with sub-weekly temporal accuracy, providing up to 240 measurements per year at a 20 m resolution.

Inversion of Soil Moisture on Farmland Areas Based on SSA-CNN Using Multi-Source Remote Sensing Data

Soil moisture is a crucial factor in the field of meteorology, hydrology, and agricultural sciences. In agricultural production, surface soil moisture (SSM) is crucial for crop yield estimation and drought monitoring. For SSM inversion, a synthetic aperture radar (SAR) offers a trustworthy data source. However, for agricultural fields, the use of SAR data alone to invert SSM is susceptible to the influence of vegetation cover. In this paper, based on Sentinel-1 microwave remote sensing data and Sentinel-2 optical remote sensing data, a convolution neural network optimized by sparrow search algorithm (SSA-CNN) was suggested to invert farmland SSM. The feature parameters were first extracted from pre-processed remote sensing data. Then, the correlation analysis between the extracted feature parameters and field measured SSM data was carried out, and the optimal combination of feature parameters for SSM inversion was selected as the input data of the subsequent models. To enhance the performance of the CNN, the hyper-parameters of CNN were optimized using SSA, and the SSA-CNN model was built for SSM inversion based on the obtained optimal hyper-parameter combination. Three typical machine learning approaches, including generalized regression neural network, random forest, and CNN, were used for comparison to show the efficacy of the suggested method. With an average coefficient of determination of 0.80, an average root mean square error of 2.17 vol.%, and an average mean absolute error of 1.68 vol.%, the findings demonstrated that the SSA-CNN model with the optimal feature combination had a better accuracy among the 4 models. In the end, the SSM of the study region was inverted throughout four phenological periods using the SSA-CNN model. The inversion results indicated that the suggested method performed well in local situations.

Improving Landslide Detection on SAR Data Through Deep Learning

In this letter, we use deep learning convolutional neural networks (CNNs) to compare the landslide mapping and classification performances of optical images (from Sentinel-2) and synthetic aperture radar (SAR) images (from Sentinel-1). The training, validation, and test zones used to independently evaluate the performance of the CNN on different datasets are located in the eastern Iburi subprefecture in Hokkaido, where, at 03.08 local time (JST) on September 6, 2018, an Mw 6.6 earthquake triggered about 8000 coseismic landslides. We analyzed the conditions before and after the earthquake exploiting multipolarization SAR as well as optical data by means of a CNN implemented in TensorFlow that points out the locations where the landslide class is predicted as more likely. As expected, the CNN runs on optical images proved itself excellent for the landslide detection task, achieving an overall accuracy of 98.96%, while CNNs based on the combination of ground range detected (GRD) SAR data reached overall accuracies beyond 95%. Our findings show that the integrated use of SAR data may also allow for rapid detection even during storms and under dense cloud cover and provides comparable accuracy to classical optical change detection in landslide recognition and detection.

Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia

Earth observation (EO) satellites facilitate hazard monitoring and mapping over large-scale and remote areas. Despite Synthetic Aperture Radar (SAR) satellites being well-documented as a hazard monitoring tool, the uptake of these data is geographically variable, with the Australian continent being one example where the use of SAR data is limited. Consequently, less is known about how these data apply in the Australian context, how they could aid national hazard monitoring and assessment, and what new insights could be gleaned for the benefit of the international disaster risk reduction community. The European Space Agency Sentinel-1 satellite mission now provides the first spatially and temporally complete global SAR dataset and the first opportunity to use these data to systematically assess hazards in new locations. Using the example of Australia, where floods and uncontrolled bushfires, earthquakes, resource extraction (groundwater, mining, hydrocarbons) and geomorphological changes each pose potential risks to communities, we review past usage of EO for hazard monitoring and present a suite of new case studies that demonstrate the potential added benefits of SAR. The outcomes provide a baseline understanding of the potential role of SAR in national hazard monitoring and assessment in an Australian context. Future opportunities to improve national hazard identification will arise from: new SAR sensing capabilities, which for Australia includes a first-ever civilian EO capability, NovaSAR-1; the integration of Sentinel-1 SAR with other EO datasets; and the provision of standardised SAR products via Analysis Ready Data and Open Data Cubes to support operational applications.

Utilization of WRF 3D Meteorological Data to Calculate Slant Total Delay for InSAR Atmospheric Correction

The atmosphere introduces excess delays into the synthetic aperture radar (SAR) signal trajectory, especially in the troposphere. InSAR atmospheric correction methods include the use of SAR data and external water vapor products. The latter is more effective. However, since the removal of atmospheric effects should use atmospheric delay products in the direction of the line of sight (LOS), it is necessary to convert the zenith total delay to slant delay in the LOS direction. Conventionally, the zenith delay is divided by the cosine of the average incident angles to obtain slant phase delays. But this method could cause large errors because it ignores the atmospheric horizontal gradient change and the small-scale vertical structure. These problems can be solved by using three-dimensional atmospheric data simulated by numerical models, especially in the case of intense weather changes or complex terrain. However, few scholars paid attention to the application into InSAR atmospheric correction, because of the computation complexity and low efficiency. As the requirement for higher accuracy and the introduction of large errors caused by increasing incidence angles, it is significantly imperative to make the utmost of this method. Weather Research Forecast (WRF) model can provide the precipitate water vapor (PWV) and refraction index at different levels in the three dimensions, and then the slant total delay can be obtained for removing the atmospheric effect on the InSAR process. The results demonstrate that using 3D data can obtain more accurate slant total delay and improve the accuracy of surface deformation from InSAR technology.

Applicability of the MultiTemporal Coherence Approach to Sentinel-1 for the Detection and Delineation of Burnt Areas in the Context of the Copernicus Emergency Management Service

In the framework of the Copernicus Emergency Management Service (EMS) Mapping Validation, the applicability of the MultiTemporal Coherence (MTC) technique using Sentinel-1 data and the software made available by the European Space Agency (ESA), the Sentinel Application Platform (SNAP), for the detection and delineation of burnt areas was tested. The main purpose of the study was to test a methodology that would benefit from the advantages of delineating burnt areas based on radar data with respect to optical data due to its capacity to acquire data both night and day and to avoid the interference of clouds and/or smoke. Moreover, the study aimed to acheive the delineation of the burnt areas using Sentinel-1 and SNAP in the frame of an emergency mapping where processing time is constrained due to the necessity of giving a quick response to the emergency. Four Sentinel-1 images were acquired over a mountainous area mainly covered by Mediterranean vegetation that suffered from massive forest fires in the summer of 2016. The burnt area delineation was obtained by an object-based image analysis (OBIA) of the resulting MTC image followed by a visual inspection. The effects of the polarization, the acquisition mode, and the incidence angle of the synthetic aperture radar (SAR) imagery were studied in order to assess the contribution of these sensor varaibles on the results. Results of the Sentinel-1 based delineation were compared to those using optical imagery, which is traditionally used for this application. Therefore, the fire delineation that was derived was compared to that derived using three optical images: pre- and post-event Sentinel-2 images and a post-event SPOT 6 image. The first two were used to calculate the differences of the burnt area index (dBAI), used to derive the burnt area delineation by OBIA and photo interpretation with the help of the SPOT 6 image. Results of the comparison showed the feasibility of using the MTC technique for burnt area delineation, as high overall accuracy values were observed when compared to the burnt area delineation derived from optical imagery. The importance of the incidence angle of the Sentinel-1 images was assessed as well, with lower angles resulting in higher overall accuracies. In addition, the availability of double polarization of the Sentinel-1 images, allowed us to give recommendations regarding which polarization gave the best results. The potential for the use of SAR data, obtaining equivalent results to those obtained from optical imagery, is significant in an emergency context given that radar sensors acquire images continuosly and in all weather conditions.

Flood inundation mapping and monitoring in Kaziranga National Park, Assam using Sentinel-1 SAR data.

Satellite-based flood assessment for extent and severity is very crucial input before, during, and after a flood event has occurred. Though optical remote sensing data has been widely used for flood hazard mapping, Synthetic Aperture Radar (SAR) data is preferred for detecting inundated areas and providing reliable information during a flood event due to its capability to operate in all weather and day/night time. Availability of cloud-free optical images during monsoon over north eastern India is a rarity. SAR data also has the advantage of detecting inundation under vegetated areas due to its penetration capabilities and sensitivity to soil moisture. The present study is an attempt to use SAR data for flood monitoring of the Kaziranga National Park (KNP) during monsoon, 2017. Every year, animals are washed away by floods and most of them migrate to higher grounds in order to escape from the rising water levels. Flooding events are common in the study area during the monsoon season due to high rainfall and its close proximity to the Brahmaputra River. Dual polarized (VV and VH) Sentinel-1 SAR images obtained for the entire monsoon period in 2017 were used to create inundation maps of the KNP. Two flood waves were observed in July and August, the second of which is considered to be one of the worst flooding events inundating most areas of the park. The use of SAR data for monitoring of flood events can be very crucial for identifying locations for building animal shelters and finding routes for rescue and relief operations during the disaster.

Suitability Assessment of X-Band Satellite SAR Data for Geotechnical Monitoring of Site Scale Slow Moving Landslides

This work addresses the suitability of using X-band Synthetic Aperture Radar (SAR) data for operational geotechnical monitoring of site scale slow moving landslides, affecting urban areas and infrastructures. The scale of these studies requires high resolution data. We propose a procedure for the practical use of SAR data in geotechnical landslides campaigns, that includes an appropriate dataset selection taking into account the scenario characteristics, a visibility analysis, and considerations when comparing advanced differential SAR interferometry (A-DInSAR) results with other monitoring techniques. We have determined that Sentinel-2 satellite optical images are suited for performing high resolution land cover classifications, which results in the achievement of qualitative visibility maps. We also concluded that A-DInSAR is a very powerful and versatile tool for detailed scale landslide monitoring, although in combination with other instrumentation techniques.

A Multisquint Framework for Change Detection in High-Resolution Multitemporal SAR Images

Change detection from multitemporal synthetic aperture radar (SAR) images enables mapping applications for earth environmental observation, human activity monitoring, and urban studies. We expand the use of SAR data beyond single-look processing to include the spatial response of targets. This information is derived from a multisquint framework similar to beamforming. To preserve changes detected at nominal resolution, a three-stage change detector exploiting single-look and multisquint processing mode is proposed to mitigate false alarms caused by image artifacts typically found in high-resolution SAR imagery and urban scenarios. After applying the proposed method to multitemporal images, the false alarm rate was reduced by a factor 3, while preserving 95% of the detection rate offered by traditional schemes.

Chlorophyll-a Estimation in Turbid Waters Using Combined SAR Data With Hyperspectral Reflectance Data: A Case Study in Lake Taihu, China

The estimation of chlorophyll-a (chl-a) concentration remains a great challenge in turbid waters due to their complex optical conditions. To improve chl-a estimation, this study aims to determine whether combined use of polarimetric synthetic-aperture radar (SAR) data has potential for improving the chl-a estimation from hyperspectral sensing reflectance for turbid waters such as those found in Lake Taihu, China. In situ measurements of hyperspectral reflectance data and water samples were collected over the lake corresponding to ENVISAT ASAR data. Semiempirical (two-band and three-band models) and empirical [multiple linear regression (MLR) and multilayer perceptron network (MLP)] models are compared to estimate the chl-a concentration from in situ hyperspectral reflectance and SAR data. The results show that there is a general underestimation of chl-a for concentrations higher than 26 ug/L, which is probably caused by the large spatial variation of chl-a in the study area. The results also demonstrate that the MLR model performs in a more stable manner than the MLP network does, while MLP underestimates low and high areas of chl-a concentrations in the lake. On the other hand, due to the availability of one scenic SAR data on the same day, our results show that the additional use of SAR data improved chl-a estimation very slightly in this case study, although the performance of vertical/vertical polarization SAR data was better than that of horizontal/horizontal polarization data in chl-a estimation. Potential future work in this subject could explore other measures of mutual information between SAR and hyperspectral optical data beyond the correlation and regression techniques described. Therefore, it is still necessary to apply more SAR data in varied turbid waters in the near future to determine how SAR data can be useful in the improvement of chl-a estimation.

Power Sensitivity Analysis of Multi-Frequency, Multi-Polarized, Multi-Temporal SAR Data for Soil-Vegetation System Variables Characterization

The knowledge of spatial and temporal variability of soil water content and others soil-vegetation variables (leaf area index, fractional cover) assumes high importance in crop management. Where and when the cloudiness limits the use of optical and thermal remote sensing techniques, synthetic aperture radar (SAR) imagery has proven to have several advantages (cloud penetration, day/night acquisitions and high spatial resolution). However, measured backscattering is controlled by several factors including SAR configuration (acquisition geometry, frequency and polarization), and target dielectric and geometric properties. Thus, uncertainties arise about the more suitable configuration to be used. With the launch of the ALOS Palsar, Cosmo-Skymed and Sentinel 1 sensors, a dataset of multi-frequency (X, C, L) and multi-polarization (co- and cross-polarizations) images are now available from a virtual constellation; thus, significant issues concerning the retrieval of soil-vegetation variables using SAR are: (i) identifying the more suitable SAR configuration; (ii) understanding the affordability of a multi-frequency approach. In 2006, a vast dataset of both remotely sensed images (SAR and optical/thermal) and in situ data was collected in the framework of the AgriSAR 2006 project funded by ESA and DLR. Flights and sampling have taken place weekly from April to August. In situ data included soil water content, soil roughness, fractional coverage and Leaf Area Index (LAI). SAR airborne data consisted of multi-frequency and multi-polarized SAR images (X, C and L frequencies and HH, HV, VH and VV polarizations). By exploiting this very wide dataset, this paper, explores the capabilities of SAR in describing four of the main soil-vegetation variables (SVV). As a first attempt, backscattering and SVV temporal behaviors are compared (dynamic analysis) and single-channel regressions between backscattering and SVV are analyzed. Remarkably, no significant correlations were found between backscattering and soil roughness (over both bare and vegetated plots), whereas it has been noticed that the contributions of water content of soil underlying the vegetation often did not influence the backscattering (depending on canopy structure and SAR configuration). Most significant regressions were found between backscattering and SVV characterizing the vegetation biomass (fractional cover and LAI). Secondly, the effect of SVV changes on the spatial correlation among SAR channels (accounting for different polarization and/or frequencies) was explored. An inter-channel spatial/temporal correlation analysis is proposed by temporally correlating two-channel spatial correlation and SVV. This novel approach allowed a widening in the number of significant correlations and their strengths by also encompassing the use of SAR data acquired at two different frequencies.

Use of SAR Data for Detecting Floodwater in Urban and Agricultural Areas: The Role of the Interferometric Coherence

The use of synthetic aperture radar (SAR) data is presently well established in operational services for flood management. Nevertheless, detecting inundated vegetation and urban areas still represents a critical issue, because the radar signatures of these targets are often ambiguous. This paper analyzes the role of the interferometric coherence in complementing intensity SAR data for mapping floods in agricultural and urban environments. The advantages of the joint use of intensity and coherence are first discussed in a theoretical way and then verified on a case study, namely, the flood that hit the Emilia-Romagna region (Northern Italy) in January 2014. The short revisit time of the COSMO-SkyMed images, as well as a dedicated acquisition plan tailored to the requirements of the Italian Civil Protection Department, has allowed us to build a data set of radar interferometric observations of the event. Results show that the analysis of the multitemporal trend of the coherence is useful for the interpretation of SAR data since it enables a considerable reduction of classification errors that could be committed considering intensity data only. Interferometric data have permitted us to distinguish zones where water receded from areas where it persisted for a longer time and, in one case, to measure changes of water level.

An efficient application of fusion approach for hot spot detection with MODIS and PALSAR-1 data

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.

Prospection and Monitoring of the Archaeological Heritage of Nasca, Peru, with ENVISAT ASAR

ABSTRACTThe processing method based on synthetic aperture radar (SAR) amplitude information presented by Cigna et al. (2013, this issue) was used to extract the backscattering coefficient (σ0) from ENVISAT advanced SAR (ASAR) scenes to investigate the archaeological heritage of the Nasca region, southern Peru. Average backscattering and σ0 time series in 2003–2007 were obtained for some of the most famous groups of the Nazca Lines, as well as for the adobe structures of the Ceremonial Centre of Cahuachi, and allowed the recognition of anthropogenic features on arid and bare soil. Despite the expected constraints due to the medium spatial resolution of the ASAR scenes (~30 m), some features related to the Nasca ancient aqueduct systems (puquios) were detected, and water level changes were inferred from amplitude change detection maps and σ0 time series. The SAR‐based prospection results were also compared with a vegetation index derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for 2003, 2004 and 2007. The changes observed over Cahuachi and the neighbouring archaeological mounds are then discussed in light of the recent conservation history of the site and the contemporary archaeological excavations. The research opens interesting perspectives for routine use of SAR data for purposes of archaeological prospection and condition monitoring in (semi‐)arid and desert regions. Copyright © 2013 John Wiley & Sons, Ltd.

SYNTHETIC APERTURE RADAR (SAR) AND OPTICAL IMAGERY DATA FUSION: CROP YIELD ANALYSIS IN SOUTHEAST ASIA

Abstract. With the expanding energy crisis and rising food prices, crop yield analysis in Southeast Asia is an increasingly important topic in this region. Rice is the most important food crop in Southeast Asia and the ability to accurately predict crop yields during a growing season is useful for decision-makers, aid providers, and commercial trade organizations. The use of optical satellite image data by itself is difficult due to the almost constant cloud in many parts of Southeast Asia. However, Synthetic Aperture Radar (SAR), or SAR data, which can image the Earth's surface through cloud cover, is suitable for many agricultural purposes, such as the detection of rice fields, and the identification of different crop species. Crop yield analysis is difficult in this region due to many factors. Rice cropping systems are often characterized by the type of rice planted, the size of rice field, the sowing dates for different fields, different types of rice cropping systems from one area to another, as well as cultural practices such as sowing and transplanting. This paper will discuss the use of SAR data fused with optical imagery to improve the ability to perform crop yield analysis on rice crops in Southeast Asia.

Optimization of Soil Hydraulic Model Parameters Using Synthetic Aperture Radar Data: An Integrated Multidisciplinary Approach

It is widely recognized that synthetic aperture radar (SAR) data are a very valuable source of information for the modeling of the interactions between the land surface and the atmosphere. During the last couple of decades, most of the research on the use of SAR data in hydrologic applications has been focused on the retrieval of land and biogeophysical parameters (e.g., soil moisture contents). One relatively unexplored issue consists of the optimization of soil hydraulic model parameters, such as, for example, hydraulic conductivity values, through remote sensing. This is due to the fact that no direct relationships between the remote-sensing observations, more specifically radar backscatter values, and the parameter values can be derived. However, land surface models can provide these relationships. The objective of this paper is to retrieve a number of soil physical model parameters through a combination of remote sensing and land surface modeling. Spatially distributed and multitemporal SAR-based soil moisture maps are the basis of the study. The surface soil moisture values are used in a parameter estimation procedure based on the extended Kalman filter equations. In fact, the land surface model is, thus, used to determine the relationship between the soil physical parameters and the remote-sensing data. An analysis is then performed, relating the retrieved soil parameters to the soil texture data available over the study area. The results of the study show that there is a potential to retrieve soil physical model parameters through a combination of land surface modeling and remote sensing.

OCEAN FEATURE DETECTION USING MICROWAVE BACKSCATTER AND SUN GLINT OBSERVATIONS

Synthetic Aperture Radar (SAR) satellite sensors have demonstrated their ability to observe ocean features related to dynamical processes including internal waves, currents, eddies, fronts, and the presence of bathymetric features. Because of the high resolution of available SAR sensors, circulation details and small-scale processes can be detected that are not observable by other sensors more frequently used for ocean research such as the NOAA AVHRR and the ORBVIEW2 SeaWiFS. In contrast to these, LANDSAT-TM thermal and optical channels can be used to observe sea surface temperatures, surface layer ocean color (upwelled radiance) as well as sun glint (reflected radiance) patterns of surface roughness at a spatial resolution comparable to that of SAR. Several examples of TM images obtained in 1997-2002 over the Argentine coastal ocean region where selected from an extensive data set. These images were analyzed and compared with a series of SAR images acquired over the same region by the ERS satellites and in some cases near coincident with the TM data. A close inspection of these data demonstrates that over a sun glint region, a high-resolution optical sensor can provide observations of sea surface patterns related to ocean dynamic processes very similar to those captured by SAR. The ability of TM to detect such ocean features can extend and complement the use of SAR data for ocean research

Remotely sensed indicators of habitat heterogeneity: Use of synthetic aperture radar in mapping vegetation structure and bird habitat

Remotely sensed indicators of habitat heterogeneity: Use of synthetic aperture radar in mapping vegetation structure and bird habitat

Forest mapping from multi‐source satellite data using neural network classifiers—an experiment in Portugal

Abstract The monitoring of forest ecosystems and their biodiversity is a major challenge for satellite remote sensing. The opportunity now exists for operationally using multi‐sensor high resolution satellite data from optical, infrared and microwave parts of the electromagnetic spectrum. An experiment is reported on the integrated use of Landsat Thematic Mapper (TM) imagery combined with ERS‐1 Synthetic Aperture Radar (SAR) imagery for mapping eight identifiable types of forest ecosystem in a test area of approximately 100 x 100 km in western central Portugal. The TM and SAR images were geometrically co‐registered and resampled to a spatial resolution of 25 m. The SAR data were despeckled using a multiple regression method based on the TM scene. The classification of the integrated multi‐sensor dataset was carried out using multi‐layer perceptron neural networks which were trained using ground truth samples taken from test sites within the experimental area. The neural network was used in two separate mapping stages: the first to map nine basic classes of land cover including one of forest, and the second to map eight different types of forest within the generic forest land cover area. The total classification accuracy achieved for the eight forest classes was 77.7% from the integrated TM and SAR dataset, which was 1.7% higher than the accuracy achieved using the TM data alone. Whilst the overall classification accuracy did not improve significantly through the use of SAR data in addition to TM, there were some significant gains for certain individual classes. In particular, the addition of the SAR channel gave a 13.1% improvement in the discrimination of a mixed conifer/broad‐leaf class largely by giving better separation from a pure conifer class.