Mapping land cover types in the Amazon Basin using 1 km JERS-1 mosaic

During the Global Rain Forest Mapping (GRFM) project, the JERS-1 SAR (Synthetic Aperture Radar) satellite acquired wall-to-wall image coverage of the humid tropical forests of the world. The rationale for the project was to demonstrate the application of spaceborne L-band radar in tropical forest studies. In particular, the use of orbital radar data for mapping land cover types, estimating the area of floodplains, and monitoring deforestation and forest regeneration were of primary importance. In this paper we examine the information content of the JERS-1 SAR data for mapping land cover types in the Amazon basin. More than 1500 high-resolution (12.5 m pixel spacing) images acquired during the low flood period of the Amazon river were resampled to 100 m resolution and mosaicked into a seamless image of about 8 million km², including the entire Amazon basin. This image was used in a classifier to generate a 1 km resolution land cover map. The inputs to the classifier were 1 km resolution mean backscatter and seven first-order texture measures derived from the 100 m data by using a 10 x 10 independent sampling window. The classification approach included two interdependent stages. First, a supervised maximum a posteriori Baysian approach classified the mean backscatter image into five general cover categories: terra firme forest (including secondary forest), savanna, inundated vegetation, open deforested areas and open water. A hierarchical decision rule based on texture measures was then applied to attempt further discrimination of known subcategories of vegetation types based on taxonomic information and woody biomass levels. True distributions of the general categories were identified from the RADAMBRASIL project vegetation maps and several field studies. Training and validation test sites were chosen from the JERS-1 image by consulting the RADAM vegetation maps. After several iterations and combining land cover types, 14 vegetation classes were successfully separated at the 1 km scale. The accuracy of the classification methodology was estimated to be 78% when using the validation sites. The results were also verified by comparison with the RADAM- and AVHRR-based 1 km resolution land cover maps.     

JERS-1/SAR backscatter and its relationship with biomass of regenerating forests

It has been hypothesized that regenerating tropical forests are large atmospheric carbon sinks. Accurate estimates of the location, extent and biomass of regenerating tropical forests are needed in order to quantify their contribution to global carbon budgets. Synthetic Aperture Radar (SAR) data are independent of near-constant tropical cloud cover and have proved useful for locating and mapping the extent of regenerating tropical forests. To estimate the biomass of regenerating tropical forests we need to determine the nature and strength of the relationship between radar backscatter and biomass for different types of regenerating forest. To further investigate this, two extreme forms of regenerating forest were considered; they were block-logged (clear-cut) forest in the Tapajós area of Pará State, Brazil and selectively-logged forest in Southern Cameroon. Biomass was estimated alometrically for 15 plots in Tapajós and 34 plots in Cameroon and was related to L-band backscatter derived from the JERS-1 SAR. The relationship between backscatter and biomass was strong for the Tapajós study area and weak for the Cameroonian study area. It was concluded that there is potential for the use of JERS-1/SAR to locate, map and estimate biomass for young regenerating forests following block-logging rather than selective-logging.     

Relationships between polarimetric SAR backscatter and forest canopy and sub-canopy biophysical properties

This paper highlights the potential of multiwaveband polarimetric SAR data for the estimation of both canopy (percentage canopy closure) and sub-canopy (stem biomass) biophysical variables of a Sitka spruce forest in upland Wales. Stand stem biomass was estimated using forest survey data on diameter at breast height (DBH) and tree height from 0.01 ha plots. Photographs of the forest canopy were taken using a camera fitted with a wide-angle fisheye lens from a number of locations within a stand. The photographs were later digitized and estimates of stand percentage canopy closure were derived using image processing software. It was found that C-band HV and VV, and L-band HV and VV polarization backscatter were significantly related to stem biomass. There was no sensitivity to percentage canopy closure using single polarization backscatter but highly significant relationships were obtained using ratios of single polarization backscatter and variables derived from the polarization signatures. The strong correlations between C-band backscatter and stem biomass indicated a relationship between the structure of the top crown layer and sub-canopy biomass.     

A method to estimate forest biomass and its application to monitor Mongolian Taiga using JERS-1 SAR data

This study presents a technique and potential utilization of JERS-1 Synthetic Aperture Radar (SAR) data for the estimation of Taiga species biomass in the Huvsgul Lake basin, Mongolia. In order to develop algorithms for estimating total stand biomass, shapes of the tree trunks were considered. A least-squares method was used to define tree trunk shape coefficients, which were then used to estimate total stand biomass using ground data. L-band data confirmed the backscattering coefficient to be dependent upon not only the quantity of biomass, but also tree parameters. The relationship between backscattering coefficient and forest stand biomass in slope areas of the study area was obtained.     

Geo-referencing of continental-scale JERS-1 SAR mosaics based on matching homologous features with a digital elevation model: theory and practice

An effective method for a posteriori ortho-rectification of continental-scale synthetic aperture radar (SAR) mosaics using a digital elevation model (DEM) has been developed. The method is based on homologous feature matching between the DEM and a simulated SAR image. The simulated image is derived from the radar-viewing geometry, topographic information and contextual information provided by the Shuttle Radar Topography Mission (SRTM), shorelines and water bodies database (SWBD) and GeoCover Landsat mosaics. Two large L-band SAR mosaics (the global boreal forest mapping (GBFM) Siberia mosaic and the global rain forest mapping (GRFM) Africa mosaic), assembled from the Japanese Earth Resources Satellite-1 (JERS-1) data, were accurately geo-referenced and ortho-rectified. The GRFM Africa mosaic was also radiometrically corrected for topographic effects. The accurate co-registration with the DEM allows for improved classification methods based on the combination of SAR backscatter with terrain features. Comparison of the revised GBFM and GRFM mosaics with a forthcoming set of continental-scale mosaics assembled from the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data will offer a unique possibility for change detection studies over the Tropical and Boreal forest zones with a temporal spacing of some 10 years.     

基于光学与SAR因子的森林生物量多元回归估算

基于福建省Landsat-8 OLI影像，利用混合像元分解模型从实测样地数据中筛选出“纯净”的植被像元，并将筛选出的样地分为针叶林、阔叶林和混交林3种植被类型，依次提取3种不同植被类型“纯净”植被像元的树高、林龄、坡度属性信息以及对应的光学NDVI、RVI植被因子和合成孔径雷达（SAR）HH、HV极化后向散射因子，分别构成不同植被类型的“含光学特征多元因子”（NDVI、RVI、树高、林龄、坡度）和“含SAR特征多元因子”（HH、HV、树高、林龄、坡度），开展对比研究。采用含光学特征的多元因子回归模型先估测不同植被类型的森林叶生物量，然后根据叶生物量与地上生物量的关系间接估测森林地上生物量。同时，采用含SAR特征的多元因子回归模型直接估测森林的地上生物量。最后，对比分析这两组多元回归模型的估测精度。结果表明：不同植被类型的含光学特征多元回归模型的验证精度（针叶林：R²为0.483，RMSE为29.522 t/hm²；阔叶林：R²为0.470，RMSE为21.632 t/hm²；混交林：R²为0.351，RSME为25.253 t/hm²）比含SAR特征多元回归模型的验证精度（针叶林：R²为0.319，RMSE为28.352 t/hm²；阔叶林：R²为0.353，RMSE为18.991t/hm²；混交林：R²为0.281，RMSE为26.637 t/hm²）略高，说明在福建省森林生物量估算中采用含光学特征的多元回归模型（先估测叶生物量进而间接估测地上生物量）比利用含SAR特征的多元回归模型（直接估测地上生物量）更具优势。     

基于光学与SAR因子的森林生物量多元回归估算

基于福建省Landsat-8 OLI影像,利用混合像元分解模型从实测样地数据中筛选出“纯净”的植被像元,并将筛选出的样地分为针叶林、阔叶林和混交林3种植被类型,依次提取3种不同植被类型“纯净”植被像元的树高、林龄、坡度属性信息以及对应的光学NDVI、RVI植被因子和合成孔径雷达（SAR）HH、HV极化后向散射因子,分别构成不同植被类型的“含光学特征多元因子”（NDVI、RVI、树高、林龄、坡度）和“含SAR特征多元因子”（HH、HV、树高、林龄、坡度）,开展对比研究。采用含光学特征的多元因子回归模型先估测不同植被类型的森林叶生物量,然后根据叶生物量与地上生物量的关系间接估测森林地上生物量。同时,采用含SAR特征的多元因子回归模型直接估测森林的地上生物量。最后,对比分析这两组多元回归模型的估测精度。结果表明：不同植被类型的含光学特征多元回归模型的验证精度（针叶林：R²为0.483,RMSE为29.522 t/hm²;阔叶林：R²为0.470,RMSE为21.632 t/hm²;混交林：R²为0.351,RSME为25.253 t/hm²）比含SAR特征多元回归模型的验证精度（针叶林：R²为0.319,RMSE为28.352 t/hm²;阔叶林：R²为0.353,RMSE为18.991t/hm²;混交林：R²为0.281,RMSE为26.637 t/hm²）略高,说明在福建省森林生物量估算中采用含光学特征的多元回归模型（先估测叶生物量进而间接估测地上生物量）比利用含SAR特征的多元回归模型（直接估测地上生物量）更具优势。     

Sensitivity of space-borne SAR data to forest parameters over sloping terrain. Theory and experiment

Recently, SAR data proved to be useful for the retrieval of forest biomass. However, the effects of terrain slope must be addressed towards the generalization of biomass retrieval for varied forest and environmental conditions. To this aim, we developed experimental and theoretical approaches allowing the study of multi-frequency/multi-polarization forest backscatter of a given forest type, as a function of forest parameters and SAR local incidence angle over the relief. The experimental results showed that the sensitivity of SAR data to biomass was similar to that obtained over a flat terrain, only if the backscatter data were calibrated for slope effects. Moreover, the backscatter must also be corrected for its angular decrease, which can be removed using a simple angular model developed under assumptions of theoretical equations. The highest correlation of corrected backscatter with forest parameters related to aboveground biomass (such as stand age and bole volume) was achieved at L-HV 55° (R ²     

L- and P-band backscatter intensity for biomass retrieval in hemiboreal forest

At present, the greatest source of uncertainty in the global carbon cycle is in the terrestrial ecosystems. In order to reduce these uncertainties it is necessary to provide consistent and accurate global estimates of the world forest biomass. One of the most promising methods for obtaining such estimates is through polarimetric SAR backscatter measurements at low frequencies. In this paper, the relation between polarimetric SAR backscatter at L- and P-bands and forest biomass is investigated using data acquired within the BioSAR-I campaign in southern Sweden during 2007. Methods for estimating biomass on stand level using these data are developed and evaluated, and the results for the two frequency bands are compared. For L-band data, the best results were obtained using HV-polarized backscatter only, giving estimation errors in terms of root mean square errors (RMSE) between 31% and 46% of the mean biomass for stands with biomass ranging from 10 to 290 t/ha, and an (adjusted) coefficient of determination (R²) between 0.4 and 0.6. For P-band data, the results are better than for L-band. Models using HV- or HH-polarized P-band backscatter give similar results, as does a model including both HV and HH. The RMSEs were between 18 and 27%, and the R² values were between 0.7 and 0.8.     

Radar imaging of Kelvin arms of ship wakes

The wave pattern generated by a moving ship is formed by two dominant features: the turbulent wake and a 'V'-shaped pattern trailing the ship, consisting of the two Kelvin arms. In this paper we investigate the radar imaging mechanism of Kelvin arms, which are formed by the cusp waves. A composite surface model for the radar backscattering at the ocean surface is used. The radar signatures of Kelvin arms can be attributed to tilt and hydrodynamic modulation of Bragg waves by the cusp waves. The proposed model allows the computation of the normalized radar backscattering cross-section (NRCS) as a function of radar frequency, polarization, incidence angle, wind speed and direction, and wavelength, direction, and slope of the cusp waves. By using this imaging model, radar signatures of cusp waves are calculated for several spaceborne Synthetic Aperture Radars (SARs): (1) the SEASAT L-band HH-polarized SAR, (2) the ERS-1/-2 VV-polarized SAR, (3) the RADARSAT C-band HH-polarized SAR, and (4) the X-, C- and L-band multipolarization SARs of the Space Radar Laboratory flown on the space shuttle during the SIRC/X-SAR mission in 1994. The results of the simulations are compared with SEASAT and SIR-C/X-SAR imagery of ship wake patterns. It is shown that the dependence of the observed radar signatures of Kelvin arms on radar look direction is consistent with the proposed imaging theory and that the measured relative mean NRCS values induced by Kelvin arms can be fairly well reproduced by the proposed model. The simulations indicate that ship wake signatures should be more clearly visible on SEASAT L-band SAR than on ERS-1/-2 or RADARSAT C-band SAR images. The radar signatures of Kelvin arms are strongest at low wind speeds and are not very sensitive to wind direction.     

Sensitivity of SAR data to post-fire forest regrowth in Mediterranean and boreal forests

Disturbed forests may need decades to reach a mature stage and optically-based vegetation indices are usually poorly suited for monitoring purposes due to the rapid saturation of the signal with increasing canopy cover. Spaceborne synthetic aperture radar (SAR) data provide an alternate monitoring approach since the backscattered microwave energy is sensitive to the vegetation structure. Images from two regions in Spain and Alaska were used to analyze SAR metrics (cross-polarized backscatter and co-polarized interferometric coherence) from regrowing forests previously affected by fire. TerraSAR-X X-band backscatter showed the lowest sensitivity to forest regrowth, with the average backscatter increasing by 1-2 dB between the most recent fire scar and the unburned forest. Increased sensitivity (around 3-4 dB) was observed for C-band Envisat Advanced Synthetic Aperture (ASAR) backscatter. The Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) L-band backscatter presented the highest dynamic range from unburned to recently burned forests (approximately 8 dB). The interferometric coherence showed low sensitivity to forest regrowth at all SAR frequencies. For Mediterranean forests, five phases of forest regrowth were discerned whereas for boreal forest, up to four different regrowth phases could be discerned with L-band SAR data. In comparison, the Normalized Difference Vegetation Index (NDVI) provided reliable differentiation only for the most recent development stages. The results obtained were consistent in both environments.     

Soil moisture limitations on monitoring boreal forest regrowth using spaceborne L-band SAR data

A study was carried out to investigate the utility of L-band SAR data for estimating aboveground biomass in sites with low levels of vegetation regrowth. Data to estimate biomass were collected from 59 sites located in fire-disturbed black spruce forests in interior Alaska. PALSAR L-band data (HH and HV polarizations) collected on two dates in the summer/fall of 2007 and one date in the summer of 2009 were used. Significant linear correlations were found between the log of aboveground biomass (range of 0.02 to 22.2 t ha^-1) and σ° (L-HH) and σ° (L-HV) for the data collected on each of the three dates, with the highest correlation found using the L-HV data collected when soil moisture was highest. Soil moisture, however, did change the correlations between L-band σ° and aboveground biomass, and the analyses suggest that the influence of soil moisture is biomass dependent. The results indicate that to use L-band SAR data for mapping aboveground biomass and monitoring forest regrowth will require development of approaches to account for the influence that variations in soil moisture have on L-band microwave backscatter, which can be particularly strong when low levels of aboveground biomass occur.     

Influence of buried topography on surface drainage pattern

Abstract

An automated system has been developed for mosaicking spaceborne synthetic aperture radar (SAR) imagery. The system is capable of producing multiframe mosaics for large-scale mapping by combining images in both the along-track direction and adjacent cross-track swaths from ascending and descending passes. The system requires no operator interaction and is capable of achieving high registration accuracy. The output product is a geocoded mosaic on a standard map grid such as UTM or polar stereographic. The procedure described in detail in this paper consists essentially of remapping the individual image frames into these standard grids, frame-to-frame image registration and radiometric smoothing of the seams. These procedures are directly applicable to both the Magellan Venus Mapper and a scanning SAR design such as Radarsat, Eos SAR in addition to merging image frames from traditional SAR systems such as SEASAT and SIR-B. With minor modifications, it may also be applied to spaceborne optical sensor data to generate large-scale mosaics efficiently and with a high degree of accuracy. The system has been tested with SEASAT, SIR-B and Landsat TM data. Examples presented in this paper include a 38-frame mosaic of the Yukon River basin in central Alaska, a 33-frame mosaic of southern California and a three-frame terrain-corrected geocoded mosaic of the Wind River basin in Wyoming.     

The effects of changes in forest biomass on radar backscatter from tree canopies

We validated a canopy backscatter model for loblolly pine forest stands at the Duke Forest, North Carolina, by comparing the observed and modelled SAR backscatter from the stands. Given the SAR backscatter data calibration uncertainty, the model made good predictions of C-HH, C-HV, L-HH, L-HV, L-VV, P-HH, and P-HV backscatter for most of 25 stands studied. The model overestimated C-VV backscatter for several stands, and largely overestimated P-VV backscatter for most of the stands. Using the collected SAR backscatter and ground data, and the backscatter model, we studied the influences of changes in biomass on SAR backscatter as a function of radar frequency and polarization, and evaluated the feasibility of deriving the biomass from the backscatter. This study showed that C-HH, C-HV, C-VV, L-VV, and P-VV SAR backscatter may be insensitive to the biomass change. L-HH, L-HV, P-HH, and P-HV SAR backscatter changed more than 5dB as the biomass varied. This study also showed that the L-HH and P-HH backscatter or L-HV and P-H V backscatter may be used to develop algorithms to retrieve trunk biomass or canopy biomass of the loblolly pine forests.     

Radiometric slope correction for forest biomass estimation from SAR data in the Western Sayani Mountains,Siberia

We investigated the possibility of using multiple polarization (SIR-C) L-band data to map forest biomass in a mountainous area in Siberia. The use of a digital elevation model (DEM) and a model-based method for reducing terrain effects was evaluated. We found that the available DEM data were not suitable to correct the topographic effects on the SIR-C radar images. A model-based slope correction was applied to an L-band cross-polarized (hv) backscattering image and found to reduce the topographic effect. A map of aboveground biomass was produced from the corrected image. The results indicated that multipolarization L-band synthetic aperture radar (SAR) data can be useful for estimation of total aboveground biomass of forest stands in mountainous areas.     

Estimating forest structure in wetlands using multitemporal SAR

Data from 202 forest plots on the Roanoke River floodplain, North Carolina were used to assess the capabilities of multitemporal radar imagery for estimating biophysical characteristics of forested wetlands. The research was designed to determine the potential for using widely available data from the current set of satellite-borne synthetic aperture radar (SAR) sensors to study forests over broad geographic areas and complex environmental gradients. The SAR data set included 11 Radarsat scenes, 2 ERS-1 images, and 1 JERS-1 scene. Empirical analyses were stratified by flood status such that sites were compared only if they exhibited common flooding characteristics. In general, the results indicate that forest properties are more accurately estimated using data from flooded areas, probably because variations in surface conditions are minimized where there is a continuous surface of standing water. Estimations yielded root mean square errors (RMSEs) for validation data around 10 m²/ha for basal area (BA), and less than 3 m for canopy height. The r² values generally exceeded .65 for BA, with the best predictions coming from sample sites for which both nonflooded and flooded SAR scenes were available. The addition of early spring normalized difference vegetation index (NDVI) values from Landsat Thematic Mapper (Landsat TM) improved model predictions for BA in forests where BA levels were <55 m²/ha. Further analyses indicated a very limited sensitivity of the individual SAR scenes to differences in forest composition, although soil properties in nonflooded areas exerted a weak but nevertheless important influence on backscatter.     

Forest canopy height and carbon estimation at Monks Wood National Nature Reserve, UK, using dual-wavelength SAR interferometry

Forest canopy height is a critical parameter in better quantifying the terrestrial carbon cycle. It can be used to estimate aboveground biomass and carbon pools stored in the vegetation, and predict timber yield for forest management. Polarimetric SAR interferometry (PolInSAR) uses polarimetric separation of scattering phase centers derived from interferometry to estimate canopy height. A limitation of PolInSAR is that it relies on sufficient scattering phase center separation at each pixel to be able to derive accurate forest canopy height estimates. The effect of wavelength-dependent penetration depth into the canopy is known to be strong, and could potentially lead to a better height separation than relying on polarization combinations at one wavelength alone. Here we present a new method for canopy height mapping using dual-wavelength SAR interferometry (InSAR) at X- and L-band. The method is based on the scattering phase center separation at different wavelengths. It involves the generation of a smoothed interpolated terrain elevation model underneath the forest canopy from repeat-pass L-band InSAR data. The terrain model is then used to remove the terrain component from the single-pass X-band interferometric surface height to estimate forest canopy height. The ability of L-band to map terrain height under vegetation relies on sufficient spatial heterogeneity of the density of scattering elements that scatter L-band electromagnetic waves within each resolution cell. The method is demonstrated with airborne X-band VV polarized single-pass and L-band HH polarized repeat-pass SAR interferometry using data acquired by the E-SAR sensor over Monks Wood National Nature Reserve, UK. This is one of the first radar studies of a semi-natural deciduous woodland that exhibits considerable spatial heterogeneity of vegetation type and density. The canopy height model is validated using airborne imaging LIDAR data acquired by the Environment Agency. The rmse of the LIDAR canopy height estimates compared to theodolite data is 2.15 m (relative error 17.6%). The rmse of the dual-wavelength InSAR-derived canopy height model compared to LIDAR is 3.49 m (relative error 28.5%). From the canopy height maps carbon pools are estimated using allometric equations. The results are compared to a field survey of carbon pools and rmse values are presented. The dual-wavelength InSAR method could potentially be delivered from a spaceborne constellation similar to the TerraSAR system.     

An explanation of enhanced radar backscattering from flooded forests

Abstract

A simpie structural backscatter model for a forest stand, suitable for use with L-band HH polarized radar imagery, is used to explain the increased level of backscattering observed from flooded forests. Measurements made of relative levels of backscatter from SIR-B image data of a flooded Australian forest are consistent with an interpretation based upon scattering mechanisms involving both the tree components and the understorey or forest floor. The change in Fresnel power reflection coefficient of the ground with flooding is advanced as the cause of the enhancement in backscattered power levels.     

Monitoring snowmelt across the Arctic forest–tundra ecotone using Synthetic Aperture Radar

Detailed snowpack observations, meteorology, topography and landcover classification were integrated with multi‐temporal SAR data to assess its capability for landscape scale snowmelt mapping at the forest–tundra ecotone. At three sites along an approximately 8° latitudinal gradient in the Fennoscandian mountain range, 16 multi‐temporal spaceborne ERS‐2 synthetic aperture radar (SAR) were used for mapping snowmelt.

Comparison of field measurements and backscatter values demonstrates the difficulty of interpreting observed backscatter response because of complex changes in snow properties on diurnal and seasonal temporal scales. Diurnal and seasonal melt–freeze effects in the snowpack, relative to the timing of ERS‐2 SAR image acquisition, effectively reduce the temporal resolution of such data for snow mapping, even at high latitudes.

The integration of diverse data sources did reveal significant associations between vegetation, topography and snowmelt. Several problems with the application of thresholding for the automatic identification of snowmelt were encountered. These largely related to changes in backscattering from vegetation in the late stages of snowmelt. Due to the impact of environmental heterogeneity in vegetation at the forest–tundra ecotone, we suggest that the potential to map snow cover using single polarization C‐band SAR at the forest–tundra ecotone may be limited to tundra areas.     

Effects of seasonal hydrologic patterns in south Florida wetlands on radar backscatter measured from ERS-2 SAR imagery

A multi-year study was carried out to evaluate ERS synthetic aperture radar (SAR) imagery for monitoring surface hydrologic conditions in wetlands of southern Florida. Surface conditions (water level, aboveground biomass, soil moisture) were measured in 13 study sites (representing three major wetland types) over a 25-month period. ERS SAR imagery was collected over these sites on 22 different occasions and correlated with the surface observations. The results show wide variation in ERS backscatter in individual sites when they were flooded and non-flooded. The range (minimum vs. maximum) in SAR backscatter for the sites when they were flooded was between 2.3 and 8.9 dB, and between 5.0 and 9.0 dB when they were not flooded. Variations in backscatter in the non-flooded sites were consistent with theoretical scattering models for the most part. Backscatter was positively correlated to field measurements of soil moisture. The MIchigan MIcrowave Canopy Scattering (MIMICS) model predicts that backscatter should decrease sharply when a site becomes inundated, but the data show that this drop is only 1-2 dB. This decrease was observed in both non-wooded and wooded sites. The drop in backscatter as water depth increases predicted by MIMICS was observed in the non-wooded wetland sites, and a similar decrease was observed in wooded wetlands as well. Finally, the sensitivity of backscatter and attenuation to variations in aboveground biomass predicted by MIMICS was not observed in the data.The results show that the inter- and intra-annual variations in ERS SAR image intensity in the study region are the result of changes in soil moisture and degree of inundation in the sites. The correlation between changes in SAR backscatter and water depth indicates the potential for using spaceborne SAR systems, such as the ERS for monitoring variations in flooding in south Florida wetlands.