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.     

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.     

Integration of radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia

In Queensland, Australia, forest areas are discriminated from non-forest by applying a threshold (∼ 12%) to Landsat-derived Foliage Projected Cover (FPC) layers (equating to ∼ 20% canopy cover), which are produced routinely for the State. However, separation of woody regrowth following agricultural clearing cannot be undertaken with confidence, and is therefore not mapped routinely by State Agencies. Using fully polarimetric C-, L- and P-band NASA AIRSAR and Landsat FPC data for forests and agricultural land near Injune, central Queensland, we corroborate that woody regrowth dominated by Brigalow (Acacia harpophylla) cannot be discriminated using either FPC or indeed C-band data alone, because the rapid attainment of a canopy cover leads to similarities in both reflectance and backscatter with remnant forest. We also show that regrowth cannot be discriminated from non-forest areas using either L-band or P-band data alone. However, mapping can be achieved by thresholding and intersecting these layers, as regrowth is unique in supporting both a high FPC (> ∼ 12%) and C-band SAR backscatter (> ~ − 18 dB at HV polarisation) and low L-band and P-band SAR backscatter (e.g. < =∼ 14 dB at L-band HH polarisation). To provide a theoretical explanation, a wave scattering model based on that of Durden et al. [Durden, S.L., Van Zyl, J.J. & Zebker, H.A. (1989). Modelling and observation of radar polarization signature of forested areas. IEEE Trans. Geoscience and Remote Sensing, 27, 290-301.] was used to demonstrate that volume scattering from leaves and small branches in the upper canopy leads to increases in C-band backscattering (particularly HV polarisations) from regrowth, which increases proportionally with FPC. By contrast, low L-band and P-band backscatter occurs because of the lack of double bounce interactions at co-polarisations (particularly HH) and volume scattering at HV polarisation from the stems and branches, respectively, when their dimensions are smaller than the wavelength. Regrowth maps generated by applying simple thresholds to both FPC and AIRSAR L-band data showed a very close correspondence with those mapped using same-date 2.5 m Hymap data and an average 73.7% overlap with those mapped through time-series comparison of Landsat-derived land cover classifications. Regrowth mapped using Landsat-derived FPC from 1995 and JER-1 SAR data from 1994-1995 also corresponded with areas identified within the time-series classification and true colour stereo photographs for the same period. The integration of Landsat FPC and L-band SAR data is therefore expected to facilitate regrowth mapping across Queensland and other regions of Australia, particularly as Japan's Advanced Land Observing System (ALOS) Phase Arrayed L-band SAR (PALSAR), to be launched in 2006, will observe at both L-band HH and HV polarisations.     

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.     

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     

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.     

Effects of soil moisture and water depth on ERS SAR backscatter measurements from an Alaskan wetland complex

We conducted a preliminary investigation of the response of ERS C-band SAR backscatter to variations in soil moisture and surface inundation in wetlands of interior Alaska. Data were collected from 5 wetlands over a three-week period in 2007. Results showed a positive correlation between backscatter and soil moisture in sites dominated by herbaceous vegetation cover (r = 0.74, p < 0.04). ERS SAR backscatter was negatively correlated to water depth in all open (non-forested) wetlands when water table levels were more than 6 cm above the wetland surface (r = − 0.82, p < 0.001). There was no relationship between backscatter and soil moisture in the forested (black spruce-dominated) wetland site. Our preliminary results show that ERS SAR data can be used to monitor variations in hydrologic conditions in high northern latitude wetlands (including peatlands), particularly sites with sparse tree cover.     

An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data

Accurate high-resolution soil moisture data are needed for a range of agricultural and hydrologic activities. To improve the spatial resolution of ∼ 40 km resolution passive microwave-derived soil moisture, a methodology based on 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) red, near-infrared and thermal-infrared data has been implemented at 4 km resolution. The three components of that method are (i) fractional vegetation cover, (ii) soil evaporative efficiency (defined as the ratio of actual to potential evaporation) and (iii) a downscaling relationship. In this paper, 36 different disaggregation algorithms are built from 3 fractional vegetation cover formulations, 3 soil evaporative efficiency models, and 4 downscaling relationships. All algorithms differ with regard to the representation of the nonlinear relationship between microwave-derived soil moisture and optical-derived soil evaporative efficiency. Airborne L-band data collected over an Australian agricultural area are used to both generate ∼ 40 km resolution microwave pixels and verify disaggregation results at 4 km resolution. Among the 36 disaggregation algorithms, one is identified as being more robust (insensitive to soil, vegetation and atmospheric variables) than the others with a mean slope between MODIS-disaggregated and L-band derived soil moisture of 0.94. The robustness of that algorithm is notably assessed by comparing the disaggregation results obtained using composited (averaged) Terra and Aqua MODIS data, and using data from Terra and Aqua separately. The error on disaggregated soil moisture is systematically reduced by compositing daily Terra and Aqua data with an error of 0.012 vol./vol.     

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.     

Assessment of polarimetric SAR data for discrimination between wet versus dry soil moisture conditions

Radarsat-2 imagery from extreme dry versus wet conditions are compared in an effort to determine the value of using polarimetric synthetic aperture radar (SAR) data for improving estimation of fuel moisture in a chronosequence of Alaskan boreal black spruce ecosystems (recent burns, regenerating forests dominated by shrubs, open canopied forests, moderately dense forest cover). Results show strong distinction between wet and dry conditions for C-HH and C-LR polarized backscatter, and Freeman–Durden and van Zyl surface bounce decomposition parameters (35–65% change for all but the dense spruce site). These four SAR variables have high potential for evaluation of within site surface soil moisture, as well as for relative distinction between wet and dry conditions across sites for lower biomass and sparse canopy forested sites. However, for any given test site except the shrubby regrowth site, van Zyl volume, surface, and double bounce scattering all result in similar percentage increases from dry to wet soil condition. This indicates that for most of these test sites/cases moisture enhances the magnitude of the return for all scattering mechanisms evaluated. Thus, differences in scattering from the interaction of biomass, surface roughness, and moisture condition across sites remains an issue and backscatter due to surface roughness or biomass cannot be uniquely estimated. In contrast, the Cloude–Pottier C-band decomposition variables appear invariant to soil moisture, but may instead account for variations in ecosystem structure and biomass. Further investigation is needed, as results warrant future research focused on evaluation of multiple polarimetric parameters in algorithm development.     

A first assessment of the SMOS data in southwestern France using in situ and airborne soil moisture estimates: The CAROLS airborne campaign

The Soil Moisture and Ocean Salinity (SMOS) satellite mission, based on an aperture synthesis L-band radiometer was successfully launched in November 2009. In the context of a validation campaign for the SMOS mission, intensive airborne and in situ observations were performed in southwestern France for the SMOS CAL/VAL, from April to May 2009 and from April to July 2010. The CAROLS (Cooperative Airborne Radiometer for Ocean and Land Studies) bi-angular (34°-0°) and dual-polarized (V and H) L-band radiometer was designed, built and installed on board the French ATR-42 research aircraft. During springs of 2009 and 2010, soil moisture observations from the SMOSMANIA (Soil Moisture Observing System-Meteorological Automatic Network Integrated Application) network of Météo-France were complemented by airborne observations of the CAROLS L-band radiometer, following an Atlantic-Mediterranean transect in southwestern France. Additionally to the 12 stations of the SMOSMANIA soil moisture network, in situ measurements were collected in three specific sites within an area representative of a SMOS pixel. Microwave radiometer observations, acquired over southwestern France by the CAROLS instrument were analyzed in order to assess their sensitivity to surface soil moisture (w_g). A combination of microwave brightness temperature (T_b) at either two polarizations or two contrasting incidence angles was used to retrieve w_g through regressed empirical logarithmic equations with good results, depending on the chosen configuration. The regressions derived from the CAROLS measurements were applied to the SMOS T_b and their retrieval performance was evaluated. The retrievals of w_g showed significant correlation (p-value < 0.05) with surface measurements for most of the SMOSMANIA stations (8 of 12 stations) and with additional field measurements at two specific sites, also. Root mean square errors varied from 0.03 to 0.09 m³ m^− 3 (0.06 m³ m^− 3 on average).     

The SIR-C/X-SAR experiment on Montespertoli: Sensitivity to hydrological parameters

Multi-frequency and multi-temporal polarimetric SAR measurements, carried out during SIR-C/X-SAR missions over the Montespertoli area have been analysed and compared with data collected at the same frequency and polarization, but at different dates, with the NASA/JPL AIRSAR. This paper presents an analysis of the achieved results aiming at evaluating the contribution of SAR data for estimating some geophysical parameters which play a significant role in hydrological processes and in particular soil moisture and roughness. The study has pointed out that in the scale of surface roughness typical of agricultural areas, a co-polar L-bandsensor gives the highest information content for estimating soil moisture and surface roughness. The sensitivity to soil moisture and surface roughness for individual fields is rather low since both parameters affect the radar signal. However, considering data collected at different dates and averaged over a relatively wide area that includes several fields, the correlation to soil moisture is significant, since the effects of spatial roughness variations are smoothed. On the other hand the sensitivity to surface roughness is better manifested at a spatial scale, integrating on time to reduce the effects of moisture variation. The retrieval of both soil moisture and surface roughness has been performed with good results by means of a semi-empirical model.     

Airborne P-band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest

The objective of this study is to evaluate the relationship between the response (σ°) of airborne P-band Synthetic Aperture Radar (SAR) polarimetric data versus biomass values of primary forest and secondary succession. To ensure that different landscapes of “Terra firme” tropical forest of the Brazilian Amazon were represented, a test-site was selected in the lower Tapajós river region (Pará State). The microwave signals from the P-band polarimetric images were related to the aboveground biomass data by statistical regression models (logarithmic and polynomial functions). In the field survey, physiognomic and structural aspects of primary forest and regrowth were collected and afterwards the biomass was estimated using allometric equations based on dendrometric parameters. As an example of the potential use of P-band polarimetric images, they were classified by a contextual classifier (ICM), whose thematic stratification of land use/land cover was associated with biomass class intervals for mapping purposes. The main objective of this P-band experiment is to improve this tool for regional mapping of Amazon landscape changes, due to the growing rate of land use occupation.     

Assessing the influence of vegetation cover on soil-moisture signatures in fire-disturbed boreal forests in interior Alaska: Modelled results

A microwave backscattering model for shrub clumps was presented. The modelling approach was to treat the clumps as scatterers and attenuators. Three major model components were defined: surface backscattering, clump volume scattering, and multiple path interactions between clumps and ground. Total backscatter was computed by incoherent summation of the components. We then used the model to study the effects of variations in surface and willow properties (soil moisture content, and surface roughness rms height and correlation length, and willow ground coverage, clump height, and stem density) on backscatter from willows in Alaskan boreal forest region. We examined the sensitivity to variations of the six parameters combined and to variation of each parameter alone from willows of three clump sizes representing different stages of vegetation regrowth after fire. Modelled C-band backscatter was more sensitive to the variations of the surface and willow parameters than L-band backscatter at incidence angles between 20° and 60°. At incidence angles of 20-60°, C-HH and C-VV backscatter was sensitive to the variations of the three surface parameters. L-HV and L-VV backscatter were only sensitive to the moisture variation. Among the three willow parameters, change of willow ground coverage produced more sensitive cases than variations of clump height and stem density combined at C- and L-band.     

The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle

In response to the urgent need for improved mapping of global biomass and the lack of any current space systems capable of addressing this need, the BIOMASS mission was proposed to the European Space Agency for the third cycle of Earth Explorer Core missions and was selected for Feasibility Study (Phase A) in March 2009. The objectives of the mission are 1) to quantify the magnitude and distribution of forest biomass globally to improve resource assessment, carbon accounting and carbon models, and 2) to monitor and quantify changes in terrestrial forest biomass globally, on an annual basis or better, leading to improved estimates of terrestrial carbon sources (primarily from deforestation); and terrestrial carbon sinks due to forest regrowth and afforestation. These science objectives require the mission to measure above-ground forest biomass from 70° N to 56° S at spatial scale of 100-200 m, with error not exceeding ± 20% or ± 10 t ha^− 1 and forest height with error of ± 4 m. To meet the measurement requirements, the mission will carry a P-Band polarimetric SAR (centre frequency 435 MHz with 6 MHz bandwidth) with interferometric capability, operating in a dawn-dusk orbit with a constant incidence angle (in the range of 25°-35°) and a 25-45 day repeat cycle. During its 5-year lifetime, the mission will be capable of providing both direct measurements of biomass derived from intensity data and measurements of forest height derived from polarimetric interferometry. The design of the BIOMASS mission spins together two main observational strands: (1) the long heritage of airborne observations in tropical, temperate and boreal forest that have demonstrated the capabilities of P-band SAR for measuring forest biomass; (2) new developments in recovery of forest structure including forest height from Pol-InSAR, and, crucially, the resistance of P-band to temporal decorrelation, which makes this frequency uniquely suitable for biomass measurements with a single repeat-pass satellite. These two complementary measurement approaches are combined in the single BIOMASS sensor, and have the satisfying property that increasing biomass reduces the sensitivity of the former approach while increasing the sensitivity of the latter. This paper surveys the body of evidence built up over the last decade, from a wide range of airborne experiments, which illustrates the ability of such a sensor to provide the required measurements.At present, the BIOMASS P-band radar appears to be the only sensor capable of providing the necessary global knowledge about the world's forest biomass and its changes. In addition, this first chance to explore the Earth's environment with a long wavelength satellite SAR is expected to make yield new information in a range of geoscience areas, including subsurface structure in arid lands and polar ice, and forest inundation dynamics.     

A downscaling method for distributing surface soil moisture within a microwave pixel: Application to the Monsoon '90 data

A downscaling method for the near-surface soil moisture retrieved from passive microwave sensors is applied to the PBMR data collected during the Monsoon '90 experiment. The downscaling method requires (1) the coarse resolution microwave observations, (2) the fine-scale distribution of soil temperature and (3) the fine-scale distribution of surface conditions composed of atmospheric forcing and the parameters involved in the modeling of land surface-atmosphere interactions. During the Monsoon '90 experiment, eight ground-based meteorological and flux stations were operating over the 150 km² study area simultaneously with the acquisition of the aircraft-based L-band PBMR data. The heterogeneous scene is hence composed of eight subpixels and the microwave pixel is generated by aggregating the microwave emission of all sites. The results indicate a good agreement between the downscaled and ground-based soil moisture as long as the intensity of solar radiation is sufficiently high to use the soil temperature as a tracer of the spatial variability of near-surface soil moisture.     

Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia

To evaluate the use of multi-frequency, polarimetric Synthetic Aperture Radar (SAR) data for quantifying the above ground biomass (AGB) of open forests and woodlands, NASA JPL AIRSAR (POLSAR) data were acquired over a 37 × 60 km area west of Injune, central Queensland, Australia. From field measurements recorded within 32 50 × 50 m plots, AGB was estimated by applying species-specific allometric equations to stand measurements. AGB was then scaled-up to the larger area using relationships established with Light Detection and Ranging (LiDAR) data acquired over 150 (10 columns, 15 rows) 500 × 150 m cells (or Primary Sampling Units, PSUs) spaced 4 × 4 km apart in the north- and east-west directions. Large-scale (1 : 4000) stereo aerial photographs were also acquired for each PSU to assess species composition. Based on the LiDAR extrapolations, the median AGB for the PSU grid was 82 Mg ha^− 1 (maximum 164 Mg ha^− 1), with the higher levels associated with forests containing a high proportion of Angophora and Callitris species. Empirical relationships between AGB and SAR backscatter confirmed that C-, L- and P-band saturated at different levels and revealed a greater strength in the relationship at higher incidence angles and a larger dynamic range and consistency of relationships at HV polarizations. A higher level of saturation (above ∼50 Mg ha^− 1) was observed at C-band HV compared to that reported for closed forests which was attributable to a link between foliage projected cover (FPC) and AGB. The study concludes that L-band HV backscatter data acquired at incidence angles approaching or exceeding 45° are best suited for estimating the AGB up to the saturation level of ∼80-85 Mg ha^− 1. For regional mapping of biomass below the level of saturation, the use of the Japanese Space Exploration Agency (JAXA) Advanced Land Observing Satellite (ALOS) Phase Arrayed L-band SAR (PALSAR) is advocated.     

Effects of corn on C- and L-band radar backscatter: A correction method for soil moisture retrieval

This paper discusses the effects of vegetation on C- (4.75 GHz) and L- (1.6 GHz) band backscattering (σ^o) measured throughout a growth cycle at incidence angles of 15, 35 and 55°. The utilized σ^o data set was collected by a truck mounted scatterometer over a corn field and is supported by a comprehensive set of ground measurements, including soil moisture and vegetation biomass. Comparison of σ^o measurement against simulations by the Integral Equation Method (IEM) surface scattering model (Fung et al., 1992) shows that the σ^o measurements are dominated either by an attenuated soil return or by scattering from vegetation depending on the antenna configuration and growth stage. Further, the measured σ^o is found to be sensitive to soil moisture even at peak biomass and large incidence angles, which is attributed to scattering along the soil-vegetation pathway.For the simulation of C-band σ^o and the retrieval of soil moisture two methods have been applied, which are the semi-empirical water cloud model (Attema & Ulaby, 1978) and a novel method. This alternative method uses the empirical relationships between the vegetation water content (W) and the ratio of the bare soil and the measured σ^o to correct for vegetation. It is found that this alternative method is superior in reproducing the measured σ^o as well as retrieving soil moisture. The highest retrieval accuracies are obtained at a 35° incidence angle leading to RMSD's of 0.044 and 0.037 m³ m^− 3 for the HH and VV-polarization, respectively. In addition, the sensitivity of these soil moisture retrievals to W and surface roughness parameter uncertainties is investigated.     

Validation of AMSR-E soil moisture using L-band airborne radiometer data from National Airborne Field Experiment 2006

AMSR-E has been extensively evaluated under a wide range of ground and climate conditions using in situ and aircraft data, where the latter were primarily used for assessing the T_B calibration accuracy. However, none of the previous work evaluates AMSR-E performance under the conditions of flood irrigation or other forms of standing water. Also, it should be mentioned that global soil moisture retrievals from AMSR-E typically utilize X-band data. Here, C-band based AMSR-E soil moisture estimates are evaluated using 1 km resolution retrievals derived from L-band aircraft data collected during the National Airborne Field Experiment (NAFE'06) field campaign in November 2006. NAFE'06 was conducted in the Murrumbidgee catchment area in southeastern Australia, which offers diverse ground conditions, including extensive areas with dryland, irrigation, and rice fields. The data allowed us to examine the impact of irrigation and standing water on the accuracy of satellite-derived soil moisture estimates from AMSR-E using passive microwave remote sensing. It was expected that in fields with standing water, the satellite estimates would have a lower accuracy as compared to soil moisture values over the rest of the domain. Results showed sensitivity of the AMSR-E to changes in soil moisture caused by both precipitation and irrigation, as well as good spatial (average R = 0.92 and RMSD = 0.049 m³/m³) and temporal (R = 0.94 and RMSD = 0.04 m³/m³) agreement between the satellite and aircraft soil moisture retrievals; however, under the NAFE'06 ground conditions, the satellite retrievals consistently overestimated the soil moisture conditions compared to the aircraft.     

Measuring biomass changes due to woody encroachment and deforestation/degradation in a forest-savanna boundary region of central Africa using multi-temporal L-band radar backscatter

Satellite L-band synthetic aperture radar backscatter data from 1996 and 2007 (from JERS-1 and ALOS PALSAR respectively), were used with field data collected in 2007 and a back-calibration method to produce biomass maps of a 15 000 km² forest-savanna ecotone region of central Cameroon. The relationship between the radar backscatter and aboveground biomass (AGB) was strong (r² = 0.86 for ALOS HV to biomass plots, r² = 0.95 relating ALOS-derived biomass for 40 suspected unchanged regions to JERS-1 HH). The root mean square error (RMSE) associated with AGB estimation varied from ~ 25% for AGB < 100 Mg ha^− 1 to ~ 40% for AGB > 100 Mg ha^− 1 for the ALOS HV data. Change detection showed a significant loss of AGB over high biomass forests, due to suspected deforestation and degradation, and significant biomass gains along the forest-savanna boundary, particularly in areas of low population density. Analysis of the errors involved showed that radar data can detect changes in broad AGB class in forest-savanna transition areas with an accuracy > 95%. However, quantitative assessment of changes in AGB in Mg ha^− 1 at a pixel level will require radar images from sensors with similar characteristics collecting data from the same season over multiple years.