The European Space Agency's (ESA) BIOMASS mission is a pioneering Earth observation satellite mission launched on April 29, 2025. Utilizing a P-band synthetic aperture radar (SAR), the objective of BIOMASS is to deliver estimates of above-ground forest biomass, forest height (FH), and forest disturbance (FD), with unprecedented accuracy. The mission's primary scientific goal is to quantify the distribution and changes in forest biomass, thereby reducing uncertainties in carbon flux estimates and informing climate models. The satellite's advanced instrumentation and innovative approach allow it to penetrate dense forest canopies, capturing data even in challenging environments. The mission will operate in two distinct phases: the tomographic phase and the interferometric phase, which will support polarimetric interferometric SAR (Pol-InSAR) and tomographic SAR (TomoSAR) processing. Additionally, BIOMASS will provide valuable observational data for ice sheets, deserts, the ionosphere, below canopy topography, and other domains.

With the rise in global greenhouse gas emissions, accurate large-scale tree canopy height maps are essential for understanding forest structure, estimating above-ground biomass, and monitoring ecological disruptions. To this end, we present a novel approach to generate large-scale, high-resolution canopy height maps over time. Our model accurately predicts canopy height over multiple years given Sentinel 2 time series satellite data. Using GEDI LiDAR data as the ground truth for training the model, we present the first 10 m resolution temporal canopy height map of the European continent for the period 2019–2022. As part of this product, we also offer a detailed canopy height map for 2020, providing more precise estimates than previous studies. Our pipeline and the resulting temporal height map are publicly available, enabling comprehensive large-scale monitoring of forests and, hence, facilitating future research and ecological analyses. For an interactive viewer, see https://europetreemap.projects.earthengine.app/view/europeheight.

We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in the ground-truth height measurements, and employs data from the Shuttle Radar Topography Mission to effectively filter out erroneous labels in mountainous regions, enhancing the reliability of our predictions in those areas. A comparison between predictions and ground-truth labels yields an MAE/RMSE of 2.43 / 4.73 (meters) overall and 4.45 / 6.72 (meters) for trees taller than five meters, which depicts a substantial improvement compared to existing global-scale products. The resulting height map as well as the underlying framework will facilitate and enhance ecological analyses at a global scale, including, but not limited to, large-scale forest and biomass monitoring.

The NASA ISRO Synthetic Aperture Radar (NISAR) is scheduled for launch early in 2024 from the Satish Dhawan Space Centre (SDSC), at Sriharikota, near Chennai, India. This mission is the result of a collaboration between NASA and Indian Space Research Organization (ISRO), where NASA has contributed elements of the mission such as an L-band SAR, and ISRO has contributed other elements, such as an S-band SAR. After successful launch, the NISAR mission will collect left-looking L-band SAR data over most of the Earth’s land areas twice during every 12-day exact repeat orbit. (once while in an ascending orbit direction and once while in a descending orbit direction). NASA and ISRO have individual and joint requirements on the mission that include the performance of the imaging radars onboard the spacecraft. For example, NASA must demonstrate that this L-band SAR will achieve a set of identified science measurement accuracy requirements that span Ecosystem science, Solid Earth science, and Cryosphere science disciplines. Likewise, ISRO has several applications objectives on both the L-band and S-band data from NISAR that the ISRO science team and project will be developing and testing. Pre-launch and post-launch activities have been planned to validate that these requirements are met. Here, we will discuss how the NASA plans are being executed and will present any initial results at the conference.

In preparation for the NISAR launch and data collection in 2024, the NISAR Project Science Team is building workflows for each Science Team discipline (Ecosystems, Cryosphere, and Solid Earth). This abstract focuses on the Ecosystem disciplines and the development of on-demand cloud-processing workflows for wetlands inundation, forest biomass, agricultural active crop area, and forest disturbance. The workflow simulates NISAR data using UAVSAR or ALOS-2 Single Look Complex data, which are processed to Level 2 geocoded polarimetric covariance matrix products using InSAR Scientific Computing Environment 3.0 software and to Level 3 science products using the Algorithm Theoretical Basis Documents. In this presentation, we describe these workflows and efforts to improve efficiency and data accessibility by using a cloud processing system. We present preliminary sample products from each Ecosystem discipline: inundation, forest biomass, crop area, and forest disturbance..

The NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument has performed tomographic SAR experiments over a number of study areas, including Rabi Forest in Gabon in 2016 and Sierra National Forest in California, USA in 2021. Tomographic SAR, or TomoSAR, is a technique enabling 3-D radar imaging with diverse applications including mapping of vegetation structure. Convolutional neural networks (CNNs) have shown widespread potential for many image processing and computer vision tasks such as image segmentation, classification, and object recognition. By using 3-D CNNs rather than 2-D CNNs, the filters can be applied to all three dimensions of a forest volume imaged by TomoSAR. We have trained 3-D CNN-based deep learning models to estimate canopy height and canopy cover from fully polarimetric UAVSAR TomoSAR images using lidar data as training and validation. When applied to canopy height estimation in the Rabi Forest study area, a trained network had root mean square error (RMSE) of 3.6 m (11%) compared to the validation dataset. For canopy cover estimation in the Sierra National Forest study area, the RMSE was 12%. Further work can be done to optimize the network architecture, improve the output spatial resolution, and to check if these methods can be applied to other study areas or to other vegetation structure parameters such as above-ground biomass. The results show the strong potential of 3-D CNNs for mapping wall-to-wall vegetation structure from tomographic SAR imagery using lidar training data.

Tropical forests play an important role in regulating the global carbon cycle and are crucial for maintaining the tropical forest biodiversity. Therefore, there is an urgent need to map the extent of tropical forest ecosystems. Recently, deep learning has come out as a powerful tool to map these ecosystems with the caveat of curating high quality reference datasets. Since, manually annotating high quality reference datasets is time consuming and expensive, weakly supervised learning techniques offer the potential to train high quality models without the need for manually annotating large quantities of reference datasets. In this manuscript, we propose two weakly supervised approaches that are based on Sentinel-1 SAR images, sparsely distributed pixel-wise high quality reference labels and densely distributed noisy reference labels. The proposed approaches were tested in a tropical setting in the Brazilian amazon. The results demonstrate that high quality tropical forest maps can be derived from weakly supervised learning without the need for manually annotated labels.

The NISAR mission which in its most recent round of launch preparations was set to launch in the spring of 2024, and now delayed until later in the fall or early spring of 2025, will serve as an unprecedented resource for the Remote Sensing of Ecosystems Science community. The two frequency, L- and S-band will full-polarimetric capability over a 250 km wide swath using the SweepSAR technique [1] will collect reliable set of observations (60 per year; 30 each for ascending and descending passes) on a continuing basis that will allow for the modeling and observation of time-varying processes that are prevalent in the living environment broadly described as Ecosystems. Among the prime science goals of the NISAR Ecosystems disciplines are in the characterization of agriculture, disturbance, biomass, forest structure and water dynamics seen in the world’s rivers, coasts, and permafrost regions. In this paper we provide an overview of the Ecosystem science that will be enabled by the NISAR mission and give a status of the basic algorithms that are being used to provide a basic set of tools to the community to make use of the data that NISAR will provide.

Tropical seasonal forests make up 40% of the globally available forest stock and play an essential role in regulating the variability in the global carbon cycle. Therefore, there is a strong need to persistently monitor seasonal forest changes to understand the forest carbon fluctuation and to better conserve biodiversity and enforce laws. In this regard, the advent of the European Space Agency (ESA) Copernicus program avails a dense time-series of both Synthetic Aperture Radar (SAR) and optical images, globally and free of charge, that enables the exploitation of these images for near real-time forest monitoring. Detecting seasonal forest changes in dense time-series, however, is complicated by fluctuation in the detected signal that is induced by forest phenology change. Therefore, forest disturbance detection methods should account for these seasonal fluctuations to make an accurate inference about forest disturbances. In this regard, deep learning approaches designed for sequential data such as Transformers can be used to implicitly learn the natural forest seasonality pattern in the signal to detect forest disturbances. This abstract demonstrates the efficacy of Transformers to detect seasonal forest disturbance in a seasonal dry-forest region in Bolivia.

Continuous monitoring and quantification of aboveground biomass (AGB) using in situ methodologies are limited by cost and time. UAV-lidar has been used as an efficient tool for estimating AGB, however, up to date, no study has attempted to estimate total AGB (TAGB) change detection using UAV-lidar in tropical savannas. This study aimed to estimate TAGB stock and changes in the Brazilian savanna (Cerrado) using UAV-lidar data and Support Vector Machine (SVM) model. We used four canopy-level derived metrics from the UAV-lidar data (COV, H99TH, HSKE, and HKUR) for modeling TAGB with an R2of 0.62, RMSE of 26.62 Mg/ha (46.5%), and bias -3.85 Mg/ha (6.73%), respectively. Our results showed an increase in the average TAGB of 5.07 mg/ha for the SCNPK site in 2021 when compared to 2019. The Kolmogorov-Smirnov (KS) test confirmed a statistical difference in the distribution of TAGB (p-value =< 0.5).

The NASA ISRO Synthetic Aperture Radar (NISAR) Mission plans to generate >40TB of raw data daily to support open access and operational L-band science. This includes Ecosystems applications for agriculture. To further prepare, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) platform was used to observe cropland sites across the southern United States to support the development of L-band prototype science products. Major crops include corn, cotton, pasture, peanut, rice, and soybean. A suite of cropland classification experiments applied a set of strategic algorithms to synergistically assess performance, scattering mechanisms, and limitations. SAR terms with sensitivity to volume scattering performed well and consistently across mapping experiments achieving accuracy greater than 80% for cropland vs not cropland. Volume scattering and cross-pol terms were most useful across the different ML techniques with overall accuracy and Kappa consistently over 90% and. 85, respectively, for crop type by late growth stages for both L-band observations.

The European Space Agency's 7th Earth Explorer mission, BIOMASS, was proposed in 2005 and since then there have been major changes in the scientific and political conditions within which it was conceived, and also within the technology and methodology both of the mission itself and of the complementary systems that BIOMASS will work with. This paper describes some of the most important recent developments in this overall environment of the mission, and how they affect the likely use of data from BIOMASS mission after its launch in 2023 and over its nominal five-year lifetime.

The NISAR mission, an L-band and S-band 12-day repeat-pass InSAR, currently scheduled to be launched in January 2023, will serve as an unprecedented resource for the Remote Sensing of Ecosystems Science community. The reliable set of 30 ascending and 30 descending 250 km swath of observations on a continuing basis will allow for the modeling and observation of hydrologic processes that serves as a forcing function and mode of energy transport for most living things, as well as changes in the landcover that are associated with agriculture, river and coastal dynamics, and disturbance. In this paper we provide an overview of the Ecosystem science that will be enabled by the NISAR mission and give a status of the basic algorithms that are being used to provide a basic set of tools to the community to make use of the data that NISAR will provide.

The ecosystem science requirements for the NASA ISRO Synthetic Aperture Radar (NISAR) will need to be validated after its launch in 2021 [1]. Out of all disciplines that are encompassed by the NISAR mission, ecosystems are the one in most need of pre-launch proxy data, consisting of repeated L-band observations over an extended period of time. The solid earth, cryosphere, and hazards research communities have been able to use historical and contemporary spaceborne data available from ERS-1/2, Radarsat, TerraSAR, Sentinel-1, and others for developing and evaluating products similar to what NISAR would be able to provide. This has been possible, in part, because of the focus of these disciplines on sparsely vegetated surfaces and the fairly straight-forward correspondence of surface scattering properties at both L- and C-band (wavelength of 24 cm and 5 cm respectively). Time series data from L-band sensors of value for ecosystem science disciplines, in contrast, have been sporadic and irregular. Ecosystems targets are almost always vegetated, with the scattering components and volume scattering nature of the target giving different scattering responses at the different wavelength regimes. For this reason, the use of C-band observations as a proxy for NISAR's L-band, as is often done for other disciplines, is not possible for the development and testing of NISAR algorithms.In 2018, a plan was developed for a field/airborne/spaceborne campaign to acquire data in 2019 for NISAR pre-launch ecosystem algorithm development and for evaluation of NISAR ecosystem Cal/Val protocols. This plan includes the acquisition of not just L-band SAR data from the NASA/JPL UAVSAR airborne SAR, but also the acquisition of spaceborne data, airborne data, and field measurements to fully exercise the NISAR protocols for validation of its ecosystem science measurement requirements. Included in the plan is the processing of the field, airborne, and spaceborne data into validation products and the generation of NISAR-like level 3 science products.

Neural networks were used to estimate the ability of different sets of predictors to capture the variability of above ground biomass (AGB). SMOS brightness temperatures (TBs) for only two incidence angles capture 85% of the AGB variance. Adding soil moisture or L-band vegetation optical depth (L-VOD) increase the ability to capture the AGB variance to 90 % and 92 %, respectively. With respect to using only TBs, L-VOD improves the AGB estimation in regions of low vegetation.

The structure of forests, in terms of mass and the three-dimensional arrangement of individual trees, is a direct indicator of how much carbon is stored in the ecosystem, which in turn, has a profound effect on how the ecosystem functions and cycles carbon, water, and nutrients (Shugart et al., 2010). There is an increased need to understand local to global storage and dynamics of carbon in terrestrial vegetation, as carbon storage is a prerequisite to understanding the coupling of the biosphere to other components of Earth systems including the climate (Luyssaert et al., 2008). By focusing on live carbon pool, the above ground biomass (AGB) can be estimated from algorithmic modeling using remote sensing data. Low-frequency Radar backscatter, among others, has unique advantages due to its sensitivity to forest AGB and potentially at fine spatial and temporal resolutions (Le Toan et al., 2011 ; Mitchard et al., 2009).

The structure of forests, in terms of mass and the three-dimensional arrangement of individual trees, is a direct indicator of how much carbon is stored in the ecosystem, which in turn, has a profound effect on how the ecosystem functions and cycles carbon, water, and nutrients (Shugart et al., 2015). There is an increased need to understand local to global storage and dynamics of carbon in terrestrial vegetation, as carbon storage is a prerequisite to understanding the coupling of the biosphere to other components of Earth systems including the climate. The BIOMASS mission of the European Space Agency (ESA), to be launched in 2021-2022 will provide, for the first time, synthetic aperture radar measurements at P-band frequency (~70 cm wavelength) and tomographical (TomoSAR) imaging capability to quantify forest structure and above ground biomass.

Airborne lidar provides timely updated maps for monitoring forest change at high resolution but it has been little used for that purpose due to the scarcity of long-term time-series over a common area. The NASA Jet Propulsion Laboratory Airborne Snow Observatory (ASO) is a landscape-level monitoring system that provides ongoing multi-year remote sensing measurements over mountainous ecosystems to primary quantify snow volume and dynamics. It collects low-resolution lidar data (~1.5 pt/m2) with a nominal weekly frequency up to 12 times a year with measurements that span 2013-2017 over 12 mountain watersheds across the western US that currently face ecological threads. In this work, we present a method to automatically register ASO weekly low-resolution lidar point clouds in order to calculate spatially consistent datasets (~12 pt/m2) adapted to fine scale forestry studies. We test the method using 12 lidar datasets acquired over the Tuolumne River Basin (Sierra Nevada, California) in the spring and summer of 2014. On average, the ASO lidar system provides accurate measurements in terms of geolocation (0.38m and 0.12m for the horizontal and vertical dimension, respectively) but some datasets are biased up to 1.38m and 0.53m, respectively. Our registration method successfully corrected for systematic bias improving the 3D geometry of forest point clouds.

Quantifying tropical aboveground biomass (agb) is an outstanding challenge that requires knowledge on the 3D structure of forests. Recent studies suggest that the uncertainty in estimating agb of large trees is significantly reduced if tree height and crown size are accounted for in addition to the traditional trunk diameter and wood density. Due to the fact that field inventory techniques are not adapted to characterize the 3D forest structure, crown size metrics (e.g. height and radius) are commonly estimated as a function of trunk diameter using allometric models with limitations in explaining crown variability. Airborne lidar techniques have the potential for characterizing tree height and crown size but are not adapted to estimate trunk diameter, which is a strong predictor of agb. Here, we investigate the synergy of field inventory and airborne lidar techniques to characterize the forest structure by assessing the uncertainty introduced by the field-based allometric models in the estimation of agb at the tree-level. We focus in 1454 large individual trees (trunk diameter > 60 cm) located within the La Selva Biological Station for which we dispose of field observations (trunk diameter and wood density) and lidar derived metrics (tree height and crown radius). We show that the field-based allometric models overestimate tree height and underestimate crown radius. As a result, the allometric approach overestimates the tree-level agb in 0.8 Mg when considering the 1454 individuals and the errors can reach more than 50% of the agb of individual trees. These errors on the large trees agb highly impact on the plot-level results and then propagate to the estimation of carbon stocks at the regional and national-levels.

The Earth Explorer Biomass mission will provide the scientific community with accurate maps of tropical, temperate and boreal forest biomass, including height and disturbance patterns. This information is urgently needed to improve our understanding of the global carbon cycle and to reduce uncertainties in the calculation of carbon stocks and fluxes associated to the terrestrial biosphere. It is also crucial for approaches to managing climate, such as the UNFCCC initiative known as Reducing Emissions through Degradation and Deforestation (REDD+), aimed at climate change mitigation through conservation and better management of tropical forests The required measurements are forest biomass and forest height at resolution of 200 m, and detection of deforestation at 50 m. Global maps of biomass are required with accuracy of 20% (or l0 t ha-1when above-ground biomass are less than 50 t ha-1). To achieve this Biomass will be implemented as a P-band SAR mission. It will exploit the unique sensitivity of P-band SAR together with advanced retrieval methods including polarimetric interferometry (Pol-InSAR) and SAR tomography to measure biomass, height and disturbances across the entire biomass range every 6 months. The mission will also support important secondary objectives, including sub-surface imaging in arid zones, production of a bare-earth DTM and ice applications.

The ESA-sponsored AfriSAR campaign took place in Gabon between 2015 and 2016. It was designed to collect data from tropical forests in order to support the future ESA-BIOMASS mission. This paper addresses the potential of P-band PoIIn-SAR and tomography for retrieving vegetation parameters from the multi-baseline airborne data acquired by ONERA over the forest of Lopé. It is shown that a correction of phase disturbances (phase screens) is necessary. A correction procedure based on recent works from the litterature is applied. The PolInSAR and tomographic results are presented and compared with the available LIDAR data.

Background The AfriSAR campaign was a joint NASA and European Space Agency airborne campaign conducted in Gabon in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The aim of the campaign was to collect ground, airborne SAR and airborne Lidar data for the development and evaluation of forest structure and biomass retrieval algorithms. The campaign consisted of two deployments, the first in 2015 with the ONERA SETHI SAR system and the second in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR and the DLR F-SAR. In addition, field teams from the Gabon ANPN (Agence Nationale des Parcs Nationaux), University College London and NASA were collecting ground data. Here we focus on the 2016 NASA contributions to campaign.

Airborne lidar is the tool best suited to provide timely updated maps for monitoring forest change in both horizontal and vertical dimensions. Still, it has been little used due to the scarcity of long-term time-series of lidar measurements. The NASA Jet Propulsion Laboratory Airborne Snow Observatory (ASO) is a landscape-level monitoring system that provides ongoing remote sensing measurements with high temporal resolution over large mountainous areas to quantify snow volume and dynamics. ASO collects multi-year low-resolution lidar data (~1.5 pt/m2) with a nominal weekly frequency up to 12 times a year. In this work, we present a new method to automatically register ASO weekly low-resolution forest lidar point clouds in order to calculate spatially consistent datasets (~18 pt/m2) adapted to fine scale forestry studies. Our method is tested using 12 lidar datasets acquired over the Tuolumne River Basin (California) in the spring and summer of 2014. On average, the ASO lidar system provides accurate measurements in terms of geolocation (0.38m and 0.12m for the horizontal and vertical dimension, respectively) but some datasets are biased up to 1.38m and 0.53m, respectively. Our registration method successfully corrected for systematic bias improving the 3D geometry of forest point clouds.

Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88% for fragmented forests and 74% for urban tree-cover areas, with FPRs lower than 2% for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps.

Estimation of forest height from combined polarimetric and interferometric synthetic aperture radar (Pol-InSAR) measurements has been the focus of radar remote sensing studies in the past decade. The simplicity of the random-volume-over-ground (RVoG) model makes it one of the most widely used candidates for estimating canopy height. However, the polarization-independent extinction coefficient assumption in the RVoG model fails in some certain types of the canopies, as suggested by the oriented-volume-over-ground (OVoG) model. The sensitivity of coherence magnitude and phase to different parameters of the canopy is expressed in a closed-form formulation in this paper for the first time. In order to simplify our formulation, the forest is represented by a layer of discrete randomly distributed dielectric scatterers over ground, with azimuthal symmetry. The sensitivity analysis of this work quantifies the contribution of differential extinction due to polarization change in interferometric coherence. Therefore, we can quantitatively evaluate whether the RVoG model is accurate enough to be used for a specific kind of canopy or the OVoG model is needed for better estimation. A simple layer of leaves over ground is used to simulate the sensitivity of Pol-InSAR measurements to different parameters.

In this paper, we report on the performance of a semiempirical algorithm for the retrieval of soil moisture (SM) under dense tropical forests using ultrahigh frequency (UHF) polarimetric synthetic aperture radar (SAR) data. The algorithm is a simplification of a 3-D coherent model of forest canopy based on the distorted Born approximation (DBA). The simplified model reduces the number of parameters and preserves the three dominant scattering mechanisms of volume, volume-surface, and surface for three polarized backscattering coefficients, i.e., σHH, σHV, and σVV, at UHF frequencies. The inversion process uses the Levenberg-Marquardt nonlinear least squares method to estimate the three model parameters: vegetation aboveground biomass, integrated SM up to a certain depth, and surface roughness. The performance of the inversion process is examined by first using simulation data where the initial values of the inversion process vary randomly and then using airborne UHF SAR data acquired in Costa Rica over La Selva Biological Station. The results with simulated data show that the inversion process is not significantly sensitive to initial values considering they are in the range of ±50% of the true value. A root-mean-square error (RMSE) of less than 4% can be achieved in retrieving the SM. The use of an alternate inversion approach without initial conditions using a genetic algorithm is less efficient (> 120 times longer time) and produces larger error with simulated data (RMSE = 11%) than the Levenberg-Marquardt estimation method. The inversion model simultaneously produces a biomass and SM distribution at 100-m spatial resolution. The RMSE of biomass estimation is 38 Mg/ha (15% relative error) when compared with 28 field plots. Over the plots where SM ground measurements are available, but not at the exact same day as the radar flight occurred, the total volumetric RMSE is 13.6%. However, only two ground measurements were very close to the flight day (three days apart), and for those, the SM estimate has about 3% absolute volumetric error. At the P-band, the SM sensing depth is inversely correlated with the SM allowing to map the spatial variations of SM close to the average root zone or hydrological active horizon of soils in tropical ecosystems.

In this letter, we introduce a polarization optimization concept to maximize the sensitivity of the synthetic aperture radar (SAR) backscatter measurements to a biophysical parameter. An iterative method based on Lagrangian multipliers is introduced for optimization. Using a priori information, the optimization identifies the polarization most sensitive (or least sensitive) to the quantity of interest, with best predictive characteristics. The methodology is tested for estimating forest aboveground biomass using polarimetric SAR data acquired by DLR's E-SAR airborne sensor at L- and P-band frequencies over a boreal forest test site in Krycklan Catchment, Sweden. The results show an improvement of sensitivity to forest biomass using the optimized polarization compared to canonical polarizations. Via polarization basis transformation, the correlation of biomass to backscatter is shown to improve by up to 0.23 and 0.59 at L- and P-bands, respectively.

Estimating the amount of above ground biomass in forested areas and the measurement of carbon flux through the quantification of disturbance and regrowth are critical to develop a better understanding of ecosystem processes. Well-resolved and globally consistent inventories of forest carbon must rely on remote sensing measurements, particularly from polarimetric radars. While a wide variety of studies conducted over the past three decades have shown how radar polarimetric measurements can be used to estimate above ground carbon for regions with less than 100 Mg of biomass per hectare, there is no established methodology for assessing biomass estimation accuracy based on a priori instrument and mission parameters. In this paper, a framework for assessing biomass estimation accuracy is presented that is a blend of the basic imaging physics and empirically derived parameters that describe various relationships between biomass and radar polarimetric observable quantities. The implications of this error model on the design and performance of a polarimetric radar are explored using instrument, mission, and science parameters from a notional Earth observing mission.

Radar measurements at P-band are sensitive to profile soil moisture. Associated backscatter measurements depend on the distribution and variation of the soil moisture profile. Existing scattering models account for this variation by approximating the soil moisture profile as consisting of a number of homogeneous layers. Since the inversion of the scattering models during the retrieval process can be based on only a few polarimetric backscatter measurements, the number of obtainable independent layers in the profile representation is limited. The purpose of this paper is to gain insights into the effects of the layering representation on the resulting modeled forward scattering. These insights form the rational basis for the design of retrieval algorithms. The effects of reflections between layers and other sources of error on simulated backscattering coefficients are first illustrated using several case studies. To determine the combined effect of different error sources for realistic soil moisture profiles, ten years of conditions at a grassland in California are studied. Depending on the layering strategy and the polarization, the root-mean-square error (RMSE) of backscattering coefficients due to misrepresenting the profile alone can be up to 2 dB, although errors can be up to 10 dB in particular cases. The error generally decreases as additional layers are added. The HH-polarization is more sensitive to the subsurface than the VV-polarization and has greater errors. Using a profile-dependent layer placement strategy decreases the RMSE of the backscatter simulation by less than 1 dB relative to a strategy with fixed layering.

This paper analyzes the spatial and temporal dynamics of forest structure in tropical wet forest at the La Selva Biological Station, Costa Rica. Three small-footprint lidar datasets from 1997, 2006, and 2009 are used over old-growth and secondary forests at 1-meter spatial resolution. The results quantify the spatial variability, the vertical forest structure profiles and the temporal variability, demonstrating the determination of the forest succession stages from remote sensing data.

The BIOMASS mission is designed to provide unique information on the biomass in the world's forests at spatial and temporal resolutions suitable for characterizing their dynamics and their contribution to carbon cycle estimates. To achieve this it combines biomass estimates from direct inversion of polarimetric backscattering coefficients with Pol-InSAR forest height estimates. The mission will also support important secondary objectives, including sub-surface imaging in arid zones, production of a bare-earth DTM and ice applications, and is optimized to be robust against environmental and ionospheric disturbances.

A semi-empirical algorithm for the retrieval of soil moisture, root mean square (RMS) height and biomass from polarimetric SAR data is explained and analyzed in this paper. The algorithm is a simplification of the distorted Born model. It takes into account the physical scattering phenomenon and has three major components: volume, double-bounce and surface. This simplified model uses the three backscattering coefficients (σHH, σHVand σVV) at low-frequency (P-band). The inversion process uses the Levenberg-Marquardt non-linear least-squares method to estimate the structural parameters. The estimation process is entirely explained in this paper, from initialization of the unknowns to retrievals. A sensitivity analysis is also done where the initial values in the inversion process are varying randomly. The results show that the inversion process is not really sensitive to initial values and a major part of the retrievals has a root-mean-square error lower than 5% for soil moisture, 24 Mg/ha for biomass and 0.49 cm for roughness, considering a soil moisture of 40%, roughness equal to 3cm and biomass varying from 0 to 500 Mg/ha with a mean of 161 Mg/ha.

Biomass estimation performance using polarimetric interferometric synthetic aperture radar (PolInSAR) data is evaluated at L- and P-band frequencies over boreal forest. PolInSAR data are decomposed into ground and volume contributions, retrieving vertical forest structure and polarimetric layer characteristics. The sensitivity of biomass to the obtained parameters is analyzed, and a set of these parameters is used for biomass estimation, evaluating one parametric and two non-parametric methodologies: multiple linear regression, support vector machine, and random forest. The methodology is applied to airborne SAR data over the Krycklan Catchment, a boreal forest test site in northern Sweden. The average forest biomass is 94 tons/ha and goes up to 183 tons/ha at forest stand level (317 tons/ha at plot level). The results indicate that the intensity at HH-VV is more sensitive to biomass than any other polarization at L-band. At P-band, polarimetric scattering mechanism type indicators are the most correlated with biomass. The combination of polarimetric indicators and estimated structure information, which consists of forest height and ground-volume ratio, improved the root mean square error (rmse) of biomass estimation by 17%-25% at L-band and 5%-27% at P-band, depending on the used parameter set. Together with additional ground and volume polarimetric characteristics, the rmse was improved up to 27% at L-band and 43% at P-band. The cross-validated biomass rmse was reduced to 20 tons/ha in the best case. Non-parametric estimation methods did not improve the cross-validated rmse of biomass estimation, but could provide a more realistic distribution of biomass values.

Biomass estimation performance from model-based polarimetric SAR interferometry (PolInSAR) using generic parametric and non-parametric regression methods is evaluated at Land P-band frequencies over boreal forest. PolInSAR data is decomposed into ground and volume contributions, estimating vertical forest structure, and using a set of obtained parameters for biomass regression. The considered estimation methods include multiple linear regression, support vector machine and random forest. The biomass estimation performance is evaluated on DLR's airborne SAR data at L- and P-bands over Krycklan Catchment, a boreal forest test site in Northern Sweden. The combination of polarimetric indicators and estimated structure information has improved the root mean square error (RMSE) of biomass estimation up to 28% at L-band and up to 46% at P-band. The cross-validated biomass RMSE was reduced to 20 tons/ha.

The European Space Agency (ESA) released a Call for Proposals for the next Earth Explorer Core Mission in March 2005, with the aim to select the 7thEarth Explorer (EE-7) mission for launch in the next decade. Twenty-four proposals were received and subject to scientific and technical assessment. Six candidate missions were selected and further investigated in the preliminary feasibility studies (Phase 0). One of these missions is BIOMASS, which has recently been selected to proceed to Phase-A. BIOMASS is a response to the urgent need for greatly improved mapping of global biomass and the lack of any current space systems capable of addressing this need.

At present there is no consensus as to which remote sensing technologies are appropriate for tropical forest biomass estimation. Cloud cover in the tropics and biomass saturation suggest that a combination of low-frequency SAR and interferometry (either PolInSAR or dual-band interferometric SAR DBInSAR) could provide a solution. Tropical forest biomass recovery using X-P DBInSAR has been demonstrated from an airborne platform using the X-P DEM height difference. This height is known to be considerably lower than the tree height as a result of penetration of microwaves into the canopy that can be significant even at X-band. We model the penetration using the RVOG model and show that in the strong attenuation approximation the interferometric coherence magnitude can be used to estimate penetration depth. We compare the model with GeoSAR DBInSAR observations of Peruvian forest, and, by comparison with LiDAR data, show that the GeoSAR Xband interferometric height can be corrected towards the upper canopy using knowledge of the coherence magnitude combined with the high-frequency "X-RVOG" model. We employ the corrected height with a biomass inversion equation derived from plot samples covered in the Peru campaign and generate a map of above ground forest biomass.

The woody vegetation of the Kruger National Park varies greatly in species composition, biomass and cover at regional scales. This study focuses on woody (tree and shrub) cover as a defining characteristic of savannas. We combine field measurements, optical and radar remote sensing to map woody cover across the whole of the Kruger Park at medium resolution (90 m). We also explore relationships between the mapped woody cover, climate, soil, topography, fire and herbivory. The spatial and temporal variability of woody cover is significant for Park managers in support of priorities relating to maintenance of structural and biotic heterogeneity. We derive a product that quantifies the spatial heterogeneity in woody cover within 1km cells.

In the last decades, the detection of drought occurrences and assessment of its severity using satellite data are becoming popular in disaster, desertification, crop production, phenology, land cover change and climate change studies. To detect the drought effects on different vegetation types, many methodologies have been developed, mostly relying on the use of vegetation indices. This communication reports the first attempt to assess the capability of MODIS NDVI, Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) from 2000 to 2006 time-series to detect the 2005 drought in Amazonia. To reach this objective, monthly composites of the MOD13A2 product were generated from period. Then, monthly anomalies were calculated, considering anomalous values when lower than −1 standard deviation (sd) or higher than 1 sd. Rainfall data provided by the Tropical Rainfall Measuring Mission (TRMM) was also acquired for the same time-series with the objective of supporting the understanding of vegetation response with the precipitation. Water deficit data calculated based on the TRMM data were also used to guide the sampling scheme. A land cover map for South America updated with natural land cover changes detected by the Near Real Time Deforestation Detection Project (DETER) was used as a mask to avoid false anomalies in the Brazilian Amazon. In general, NDWI and EVI showed to be sensitive and consistent for the temporal series used. NDVI presented a high variability and though a difficult interpretation. Critical months in the NDWI and EVI series coincided with the months with higher water stress calculated based on the TRMM data. EVI also showed to detect changes in the canopy structure. These preliminary results suggest that this is a strong methodology to be used in the spatial analysis of the extent of the drought effects in the vegetation. Literfall data will be incorporate in this research for validation purposes.

The National Research Council's Decadal Survey for Earth Science identified InSAR (Interferometric Synthetic Aperture Radar) observations among the highest priorities for new NASA Earth missions. A system making observations required by the solid Earth, vegetation, and ice/climate science communities is recommended. In response, analyses are underway to evaluate efficient combinations of science objectives and mission/instrument scenarios. The InSAR component can be satisfied by a new radar instrument concept capitalizing on existing technology and hardware, including a large commercial mesh reflector antenna and transmit/receive modules developed for the UAVSAR airborne radar. This InSAR system satisfies key science objectives and addresses several shortcomings of existing InSAR capable satellites. To reduce temporal decorrelation, L-Band (23 cm) wavelength is used. A 300 km wide-swath scanSAR mode with 8 day repeat enhances study of ice dynamics, pre/post earthquake deformation, volcano monitoring, and other dynamic phenomena. With a minor orbit change, global biomass surveys are possible using multipolarization. Key challenges are involve scheduling to optimize conflicting observational requirements of various science communities served.

The Microwave Observatory of Subcanopy and Subsurface (MOSS) is a mission concept for a spaceborne synthetic aperture radar (SAR) system that provides global observations of soil moisture under substantial vegetation cover (exceeding 20 kg/m2) and at useful depths (1-5 m). The concept was developed and a number of new required technologies were demonstrated through a National Aeronautics and Space Administration Earth Science Technology Office Instrument Incubator Program project. This very high frequency (VHF)/ultrahigh frequency (UHF) polarimetric SAR is designed to provide 7-10-day observations of soil moisture at 1-km resolution. The rapid repeat cycle mandates swath widths in the range of 300-400 km, which must be realized by a 30-m-long antenna. Conventional array implementations would result in a mass of more than 4000 kg, whereas with the technology proposed and demonstrated in this project, the total antenna mass is less than 500 kg. The antenna concept is a dual-stacked patch array feed illuminating a 30-m mesh reflector to synthesize the long apertures and achieve the wide swath. The feed system prototype was fabricated and its performance demonstrated. Other major project components were: (1) system-level SAR and mission design; (2) demonstration of science data and products, using a tower-based VHF/UHF radar; (3) spacecraft and mesh reflector antenna mechanical design; (4) developing mitigation strategies for ionospheric effects; and (5) assessing frequency interference effects. Experimental science data were generated from the tower radar for soil moisture profiling in Arizona and for forest penetration in Oregon. The soil moisture products were demonstrated through an integrated inversion-processing algorithm. This paper summarizes the results from the MOSS project and demonstrates the feasibility of the spaceborne mission.

Understanding fire behavior characteristics and planning for fire management require maps showing the distribution of wildfire fuel loads at medium to fine spatial resolution across large landscapes. Radar sensors from airborne or spaceborne platforms have the potential of providing quantitative information about the forest structure and biomass components that can be readily translated to meaningful fuel load estimates for fire management. In this paper, we used multifrequency polarimetric synthetic aperture radar (SAR) imagery acquired over a large area of the Yellowstone National Park by the Airborne SAR sensor to estimate the distribution of forest biomass and canopy fuel loads. Semiempirical algorithms were developed to estimate crown and stem biomass and three major fuel load parameters, namely: 1) canopy fuel weight; 2) canopy bulk density; and 3) foliage moisture content. These estimates, when compared directly to measurements made at plot and stand levels, provided more than 70% accuracy and, when partitioned into fuel load classes, provided more than 85% accuracy. Specifically, the radar-generated fuel parameters were in good agreement with the field-based fuel measurements, resulting in coefficients of determination of R2=85 for the canopy fuel weight, R2=0.84 for canopy bulk density, and R2=0.78 for the foliage biomass

The potentially measurable effects of Faraday rotation on linearly polarized backscatter measurements from space are addressed. Single-polarized, dual-polarized, and quad-polarized backscatter measurements subject to Faraday rotation are first modeled. Then, the impacts are assessed using L-band polarimetric synthetic aperture radar (SAR) data. Due to Faraday rotation, the received signal will include other polarization characteristics of the surface, which may be detectable under certain conditions. Model results are used to suggest data characteristics that will reveal the presence of Faraday rotation in a given single-polarized, dual-polarized, or quad-polarized L-band SAR dataset, provided the user can identify scatterers within the scene whose general behavior is known or can compare the data to another, similar dataset with zero Faraday rotation. The data characteristics found to be most sensitive to a small amount of Faraday rotation (i.e., a one-way rotation <20/spl deg/) are the cross-pol backscatter [/spl sigma//spl deg/(HV)] and the like-to-cross-pol correlation [e.g., /spl rho/(HHHV/sup */)]. For a diverse, but representative, set of natural terrain, the level of distortion across a range of backscatter measures is shown to be acceptable (i.e., minimal) for one-way Faraday rotations of less than 5/spl deg/, and 3/spl deg/ if the radiometric uncertainty in the HV backscatter is specified to be less than 0.5 dB.

Measurements of deep and subcanopy soil moisture are critical in understanding the global water and energy cycle, as well as the interaction of the carbon and water cycles, but are presently not available on a synoptic basis. In this paper, a low-frequency UHF/VHF radar mission concept is presented and technology challenges to implement it are discussed. This mission concept is currently being studies under a NASA/ESTO instrument incubator program (IIP) project. The progresses of several aspects of the project are discussed.

The Global Rain Forest Mapping project (GRFM) is an initiative started by the National Agency for Space Development of Japan (NASDA) in 1996 with the main goal of creating a wall to wall radar map of the tropical belt with homogeneous and consistent characteristics. GRFM Africa-the part of the project related to tropical Africa-has evolved through several years to the stage where significant thematic products have been generated. It is maintained that these products bear relevance to global change studies and to the sustainable management of local resources in the tropics. The objective of this paper is to lend support to this proposition by illustrating through a few examples the results achieved so far. In particular two land cover maps are presented covering respectively the Central Congo basin, and the Gabon country. Validation of these large-scale high-resolution products poses a challenging problem. The method adopted in GRFM Africa is outlined. It is based on comparison with independent thematic information with known error budget, derived from a combination of optical remote sensing observations, national forestry maps and ground surveys.

The Global Rain Forest Mapping Project (GRFM) is an international collaborative effort initiated and managed by the National Space Development Agency of Japan (NASDA). The main goal of the project is to produce a high resolution wall-to-wall map of the entire tropical rain forest domain in four continents using the L-band SAR onboard the JERS-1 spacecraft. The processing phase, which entails the generation of wide area radar mosaics from the raw SAR data, was split according to the geographic area. In this paper, the focus is on the part related to Africa. The GRFM project's goal calls for the coverage of a continental scale area of several million km/sup 2/ using a sensor with the resolution of tens of meters. In the case of the African continent, this entails the assemblage of some 3900 high resolution SAR scenes into a bitemporal mosaic at 100 m pixel spacing and with known geometric accuracy. While this fact opens up an entire new perspective for vegetation mapping in the tropics, it presents a number of technical challenge. The authors report on the solutions adopted in the GRFM Africa mosaic development and discuss some quantitative and qualitative aspects related to the characterization and validation of the GRFM products. In particular, the mosaic geolocation and its validation are discussed in detail. Indeed, the internal geometric consistency (subpixel accuracy in the coregistration of the two dates), and the absolute geolocation (residual mean squared error of 240 m with respect to ground control points) are key features of the GRFM Africa mosaic.

Characterization of boreal forests in ecosystem models requires temporal and spatial distributions of water content and biomass over local and regional scales. The authors report on the use of a semi-empirical algorithm for deriving these parameters from polarimetric synthetic aperture radar (SAR) measurements. The algorithm is based on a two layer radar backscatter model that stratifies the forest canopy into crown and stem layers and separates the structural and biometric attributes of forest stands. The structural parameters are estimated by training the model with SAR image data over dominant coniferous and deciduous stands in the boreal forest such as jack pine, black spruce, and aspen. The algorithm is then applied on AIRSAR images collected during the Boreal Ecosystem Atmospheric Study (BOREAS) over the boreal forest of Canada. The results are verified using biometry measurements during BOREAS-intensive field campaigns. Field data relating the water content of tree components to dry biomass are used to modify the coefficients of the algorithm for crown and stem biomass. The algorithm was then applied over the entire image generating biomass maps. A set of 18 test sites within the imaged area was used to assess the accuracy of the biomass maps. The accuracy of biomass estimation is also investigated by choosing different combinations of polarization and frequency channels of the AIRSAR system. It is shown that polarimetric data from P-band and L-band channels provide similar accuracy for estimating the above-ground biomass for boreal forest types. In general, the use of P-band channels can provide better estimates of stem biomass, while L-band channels can estimate the crown biomass more accurately.

The objective of this paper is to study the use of a decision tree classifier and multiscale texture measures to extract thematic information on the tropical vegetation cover from the Global Rain Forest Mapping (GRFM) JERS-1 SAR mosaics. The authors focus their study on a coastal region of Gabon, which has a variety of land cover types common to most tropical regions. A decision tree classifier does not assume a particular probability density distribution of the input data, and is thus well adapted for SAR image classification. A total of seven features, including wavelet-based multiscale texture measures (at scales of 200, 400, and 800 m) and multiscale multitemporal amplitude data (two dates at scales 100 and 400 m), are used to discriminate the land cover classes of interest. Among these layers, the best features for separating classes are found by constructing exploratory decision trees from various feature combinations. The decision tree structure stability is then investigated by interchanging the role of the training samples for decision tree growth and testing. They show that the construction of exploratory decision trees can improve the classification results. The analysis also proves that the radar backscatter amplitude is important for separating basic land cover categories such as savannas, forests, and flooded vegetation. Texture is found to be useful for refining flooded vegetation classes. Temporal information from SAR images of two different dates is explicitly used in the decision tree structure to identify swamps and temporarily flooded vegetation.

During several field campaigns in spring and summer of 1994, the NASA/JPL airborne synthetic aperture radar (AIRSAR) collected data over the southern and northern study sites of BOREAS. Among the areas over which radar data were collected was the young jack pine (YJP) tower site in the south, which is generally characterized as having short (2-4 m) but closely spaced trees with a dense crown layer. In this work, the AIRSAR data over this YJP stand from six different dates were used, and the dielectric constant and hence the moisture content of its branch layer components were estimated. The approach was to first derive a parametric scattering model from a numerical discrete-component forest model, which is possible if the predominant scattering mechanism can be identified. Here, a classification algorithm was used for this purpose, concentrating on areas where the volume scattering mechanism from the branch layer dominates. The unknown parameters mere taken to be the real and imaginary parts of the dielectric constant, from which the moisture content can be derived. Once the parametric model was derived, a nonlinear estimation algorithm was employed to retrieve the model parameters from SAR data. This algorithm is iterative, and takes the statistical properties of the data and unknown parameters into account. The inversion process was first verified using synthetic data. It was observed that the algorithm is robust with respect to the a priori estimate. The estimation algorithm was then applied to AIRSAR data of BOREAS. The results show how the environmental conditions affected the moisture state of this forest stand over a period of six months. It is observed that canopy moisture increased during the thaw season, was stable starting from the end of the thaw season throughout most of the growing season, after which a period of dry-down was observed at the end of the growing season.

In modeling forest canopies, several scattering mechanisms are taken into account, (1) volume scattering; (2) surface-volume interaction; (3) surface scattering from forest floor. Depending on the structural and dielectric characteristics of forest canopies, the relative contribution of each mechanism in the total backscatter signal of an imaging radar can vary. In this paper, two commonly used first-order discrete scattering models, distorted Born approximation (DBA) and radiative transfer (RT) are used to simulate the backscattered power received by polarimetric radars at P-, L-, and C-bands over coniferous and deciduous forests. The difference between the two models resides on the coherent effect in the surface-volume interaction terms. To demonstrate this point, the models are first compared based on their underlying theoretical assumptions and then according to simulation results over coniferous and deciduous forests. It is shown that by using the same scattering functions for various components of trees (i.e. leaf, branch, stem), the radiative transfer and distorted Born models are equivalent, except in low frequencies, where surface-volume interaction terms may become important, and the coherent contribution may be significant. In this case, the difference between the two models can reach up to 3 dB in both co-polarized and cross-polarized channels, which can influence the performance of retrieval algorithms.

As part of the intensive field campaign (IFC) for the Boreal forest ecosystem-atmosphere research (BOREAS) project in August 1993, the NASA/JPL AIRSAR covered an area of about 100 km/spl times/100 km near the Prince Albert National Park in Saskatchewan, Canada. At the same time, ground-truth measurements were made in several stands which have been selected as the primary study sites. This paper focuses on an area including jack pine stands in the Nipawin area near the park. Upon examining the AIRSAR data from stands of old and young jack pine (OJP and YJP), distinct signatures are observed for each of the forest types at various frequencies and polarizations, in particular, at P-band HH. The authors use a forest scattering model in conjunction with the ground-truth measurements to explain such behavior. The forest model includes the major scattering mechanisms by taking the forest component interactions into account. The contribution from each of the scattering mechanisms to the total backscatter is calculated and their differences for OJP and YJP stands are evaluated. The results are used to discuss the effect of the physical properties of the forest components in each stand on radar backscatter. They are also used to show that it is not only the backscatter level but also the relative contribution from various scattering mechanisms that will help in quantitative interpretation of SAR data. This work is mainly intended as a precursor to the authors ongoing work which uses a mechanism-specific inversion technique to retrieve forest parameters from SAR data for these BOREAS sites.>

Discusses early results obtained from Spaceborne Imaging Radar-C (SIR-C) and X-band synthetic aperture radar (XSAR) data over a boreal forest in Saskatchewan, Canada. Multifrequency and multipolarization image data were made available during the SRL-1 (Apr. 10, 1994) and SRL-2 (Oct. 1, 1994) missions. These image data sets were analyzed and maps of forest cover type and above ground woody dry biomass were generated. A portion of the Southern Study Area of the Boreal Ecosystem-Atmosphere Study (BOREAS) was mapped for forest cover type with classification accuracies on the order of 80%. Maps of estimated biomass were also produced that match observed patterns and preliminary ground data. The upper limit of sensitivity of the radar to boreal forest biomass in the study area was about 20 kg/m/sup 2/ or 200 tons/ha. The highest average observed biomass in the ground measurements was about 25 kg/m/sup 2/. The highest sensitivity of the radar to biomass was attained using April backscatter data and a ratio of L-band HV to C-band HV. Results show that radar estimates of biomass were within /spl plusmn/2 kg/m/sup 2/ at the 95% confidence level. A comparison of the April and October data sets was conducted to understand the effects of seasons on the analysis. It appears that the frozen trees and wetter background contributes to increased backscattering observed in the April data. These early results indicate that multiple polarization and multiple frequency SAR data can be used to monitor and map northern forest biomes.>

Microwave radar and radiometer measurements of grasslands indicate a substantial reduction in sensor sensitivity to soil moisture in the presence of a thatch layer. When this layer is wet it masks changes in the underlying soil, making the canopy appear warm in the case of passive sensors (radiometer) and decreasing backscatter in the active case (scatterometer). A model for a grass canopy with thatch is presented in order to explain this behavior and for comparison with observations. The canopy model consists of three layers: grass, thatch, and the underlying soil. The grass blades are modeled by elongated elliptical discs and the thatch is modeled as a collection of disk shaped water droplets (i.e., the dry matter is neglected). The ground is homogeneous and flat. The distorted Born approximation is used to compute the radar cross section of this three layer canopy and the emissivity is computed from the radar cross section using the Peake formulation for the passive problem. Results are computed at L-band (1.4 GHz) and C-band (4.75 GHz) using canopy parameters (i.e., plant geometry, soil moisture, plant moisture, etc.) representative of Konza Prairie grasslands. The results are compared to C-band scatterometer measurements and L-band radiometer measurements at these grasslands.>

The effects of the rock fraction were investigated through a combination of laboratory dielectric measurements and field observation of emissivity. A series of field measurements were conducted which included soils with (35% by volume) and without rocks. Analysis focused on the use of a 21-cm wavelength, although some field observations at 6 cm were also made. For the rock samples, the average values of the dielectric constant were 4.7 and 0.07 for the real and imaginary parts, respectively. The effects of rock fraction are not significant in estimating the sample soil moisture when 21-cm data are used, for the rock fraction examined. Data collected at 6 cm clearly showed that the presence of rocks will make this and shorter wavelengths useless as soil moisture sensors.>

An experiment that examined the C-band backscattering characteristics of conifer trees was conducted using a truck-mounted scatterometer. Small (1-m tall) balsam fir (Abies balsamea) trees were arranged at various equidistant spacings on a platform to present canopies of varying density to the radar. C-band backscattering measurements of a range of canopy densities were acquired under different polarizations and incidence angles. In addition, physical measurements of the trees were made including leaf area index, biomass, leaf and branch angle distributions, and dielectric constant. A backscatter model was implemented using measured canopy attributes and showed close agreement with scatterometer measurements over the range of canopy densities.>