EAGLE 2006 – Multi-purpose, multi-angle and multi-sensor in-situ and airborne campaigns over grassland and forest

EAGLE2006 - an intensive field campaign - was carried out in the Netherlands from the 8th until the 18th of June 2006. Several airborne sensors - an optical imaging sensor, an imaging microwave radiometer, and a flux airplane – were used and extensive ground measurements were conducted over one grassland (Cabauw) site and two forest sites (Loobos & Speulderbos) in the central part of the Netherlands, in addition to the acquisition of multi-angle and multi-sensor satellite data. The data set is both unique and urgently needed for the development and validation of models and inversion algorithms for quantitative surface parameter estimation and process studies. EAGLE2006 was led by the Department of Water Resources of the International Institute for Geo-Information Science and Earth Observation and originated from the combination of a number of initiatives coming under different funding. The objectives of the EAGLE2006 campaign were closely related to the objectives of other ESA Campaigns (SPARC2004, Sen2Flex2005 and especially AGRISAR2006). However, one important objective of the campaign is to build up a data base for the investigation and validation of the retrieval of bio-geophysical parameters, obtained at different radar frequencies (X-, C- and L-Band) and at hyperspectral optical and thermal bands acquired over vegetated fields (forest and grassland). As such, all activities were related to algorithm development for future satellite missions such as Sentinels and for satellite validations for MERIS, MODIS as well as AATSR and ASTER thermal data validation, with activities also related to the ASAR sensor on board ESA’s Envisat platform and those on EPS/MetOp and SMOS. Most of the activities in the campaign are highly relevant for the EU GEMS EAGLE project, but also issues related to retrieval of biophysical parameters from MERIS and MODIS as well as AATSR and ASTER data were of particular relevance to the NWO-SRON EcoRTM project, while scaling issues and complementary between these (covering only local sites) and global sensors such as MERIS/SEVIRI, EPS/MetOP and SMOS were also key elements for the SMOS cal/val project and the ESA-MOST DRAGON programme. This contribution describes the mission objectives and provides an overview of the airborne and field campaigns

AHS2005: The 2005 airborne imaging spectroscopy campaign in the Millingerwaard, the Netherlands

The Millingerwaard was one of the first nature rehabilitation projects for river floodplains in the Netherlands. It therefore serves as an example project for other floodplain rehabilitation projects. As a consequence a lot of effort has been put in monitoring the vegetation succession in the floodplain. To stimulate the development of a heterogeneous landscape, a low grazing density of 1 animal (e.g., Galloway, Koniks) per 2-4 ha has been chosen. This density allows grazing whole year round and also development of forest is possible. The surface area of water changes over the year. During high floods, the whole floodplain except for the higher parts of the river dunes is flooded. This report describes the field and airborne data acquired during the AHS2005 imaging spectroscopy campaign in the Millingerwaard floodplain during the summer of 2005. The campaign is part of a research line that explores the use of hyperspectral sensors to retrieve biochemical and biophysical variables as input for ecological models using an integrated approac

Physical interpretation of the correlation between multi-angle spectral data and canopy height

Recent empirical studies have shown that multi-angle spectral data can be useful for predicting canopy height, but the physical reason for this correlation was not understood. We follow the concept of canopy spectral invariants, specifically escape probability, to gain insight into the observed correlation. Airborne Multi-Angle Imaging Spectrometer (AirMISR) and airborne Laser Vegetation Imaging Sensor (LVIS) data acquired during a NASA Terrestrial Ecology Program aircraft campaign underlie our analysis. Two multivariate linear regression models were developed to estimate LVIS height measures from 28 AirMISR multi-angle spectral reflectances and from the spectrally invariant escape probability at 7 AirMISR view angles. Both models achieved nearly the same accuracy, suggesting that canopy spectral invariant theory can explain the observed correlation. We hypothesize that the escape probability is sensitive to the aspect ratio (crown diameter to crown height). The multi-angle spectral data alone therefore may not provide enough information to retrieve canopy height globally

Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping

The continuously increasing demand of accurate quantitative high quality information on land surface properties will be faced by a new generation of environmental Earth observation (EO) missions. One current example, associated with a high potential to contribute to those demands, is the multi-spectral ESA Sentinel-2 (S2) system. The present study focuses on the evaluation of spectral information content needed for crop leaf area index (LAI) mapping in view of the future sensors. Data from a field campaign were used to determine the optimal spectral sampling from available S2 bands applying inversion of a radiative transfer model (PROSAIL) with look-up table (LUT) and artificial neural network (ANN) approaches. Overall LAI estimation performance of the proposed LUT approach (LUTN₅₀) was comparable in terms of retrieval performances with a tested and approved ANN method. Employing seven- and eight-band combinations, the LUTN₅₀ approach obtained LAI RMSE of 0.53 and normalized LAI RMSE of 0.12, which was comparable to the results of the ANN. However, the LUTN50 method showed a higher robustness and insensitivity to different band settings. Most frequently selected wavebands were located in near infrared and red edge spectral regions. In conclusion, our results emphasize the potential benefits of the Sentinel-2 mission for agricultural applications

Retrieval of canopy component temperatures through Bayesian inversion of directional thermal measurements

Evapotranspiration is usually estimated in remote sensing from single temperature value representing both soil and vegetation. This surface temperature is an aggregate over multiple canopy components. The temperature of the individual components can differ significantly, introducing errors in the evapotranspiration estimations. The temperature aggregate has a high level of directionality. An inversion method is presented in this paper to retrieve four canopy component temperatures from directional brightness temperatures. The Bayesian method uses both a priori information and sensor characteristics to solve the ill-posed inversion problem. The method is tested using two case studies: 1) a sensitivity analysis, using a large forward simulated dataset, and 2) in a reality study, using two datasets of two field campaigns. The results of the sensitivity analysis show that the Bayesian approach is able to retrieve the four component temperatures from directional brightness temperatures with good success rates using multi-directional sensors (Srspectra˜0.3, Srgonio˜0.3, and SrAATSR˜0.5), and no improvement using mono-angular sensors (Sr˜1). The results of the experimental study show that the approach gives good results for high LAI values (RMSEgrass=0.50 K, RMSEwheat=0.29 K, RMSEsugar beet=0.75 K, RMSEbarley=0.67 K); but for low LAI values the results were unsatisfactory (RMSEyoung maize=2.85 K). This discrepancy was found to originate from the presence of the metallic construction of the setup. As these disturbances, were only present for two crops and were not present in the sensitivity analysis, which had a low LAI, it is concluded that using masked thermal images will eliminate this discrepanc

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LIST OF FIGURES -- LIST OF TABLES -- PREFACE -- CHAPTER 1— BACKGROUND & OVERVIEW: Alaska’s Native Lands: Alaska Native Claims Settlement Act Lands: Regional Corporations, Village Corporations, Additional ANCSA Land Entitlements, Former Native Reserve Lands; Other Native Lands: Native Allotments, Annette Island Reservation; Native Land Status; Alaskan Forests; What is a Forest Inventory?; Forest Inventories in Alaska; Forest Inventories on Native Land -- CHAPTER 2 — DETERMINING THE NEED FOR AN INVENTORY: Existing Forest Inventory Information; Agency Inventories: Forest Service Inventories, Bureau of Indian Affairs Inventories, Tanana Chiefs Conference Inventories; Level of Inventory -- CHAPTER 3 — INVENTORY PLANNING: Gathering Information; Planning Considerations: Why is This Inventory Needed?, Where will the Inventory Take Place?, What needs to be Inventoried and What Information is to be Collected?, Who is Going to do the Inventory?, When will the Inventory Take Place?, How is the Inventory going to be Done and How will the Data be Processed?, How Much is the Inventory going to Cost?, Unique Alaskan Constraints: Transportation Logistics, Adverse Weather, Musket, Dangerous Wildlife, Vegetation Barriers, Availability of Supplies and Fuel; Advantages of Planning -- CHAPTER 4 — HOW FOREST INVENTORIES ARE CONDUCTED: Maps and Aerial Photographs: Using Aerial Photographs in Forest Inventories, Using Aerial Photographs for Timber Typing; Statistical Considerations of a Forest Inventory: Variability of the Sample, Number of Samples, Sampling Design; Field Measurements: Tree Height, Tree Diameter and Taper, Tree Defects, Tree Age and Growth, Site Conditions, Forestry Equipment -- CHAPTER 5 — AFTER THE FIELD WORK IS DONE: Compilation of Data; When the Inventory is Complete; Looking Toward the Future -- BIBLIOGRAPHY -- APPENDIX I - ALASKA’S PRINCIPAL TREE SPECIES -- APPENDIX II — USES OF ALASKA'S PRINCIPAL TREE SPECIES -- APPENDIX III — FORESTY CONSULTANTS IN ALASKA -- APPENDIX IV — TECHNICAL ASSISTANCE DIRECTORY -- APPENDIX V — SAMPLE OUTLINE FOR DEVELOPING A FOREST INVENTORY PLAN -- APPENDIX VI — USGS OFFICES IN ALASKA -- APPENDIX VII — NATURAL RESOURCES SCHOOLS IN ALASK

Merging the Minnaert-k parameter with spectral unmixing to map forest heterogeneity with CHRIS/PROBA data

The Compact High Resolution Imaging Spectrometer (CHRIS) mounted onboard the Project for Onboard Autonomy (PROBA) spacecraft is capable of sampling reflected radiation at five viewing angles over the visible and near-infrared regions of the solar spectrum with high spatial resolution. We combined the spectral domain with the angular domain of CHRIS data in order to map the surface heterogeneity of an Alpine coniferous forest during winter. In the spectral domain, linear spectral unmixing of the nadir image resulted in a canopy cover map. In the angular domain, pixelwise inversion of the Rahman-Pinty-Verstraete (RPV) model at a single wavelength at the red edge (722 nm) yielded a map of the Minnaert-k parameter that provided information on surface heterogeneity at a subpixel scale. However, the interpretation of the Minnaert-k parameter is not always straightforward because fully vegetated targets typically produce the same type of reflectance anisotropy as non-vegetated targets. Merging both maps resulted in a forest cover heterogeneity map, which contains more detailed information on canopy heterogeneity at the CHRIS subpixel scale than is possible to realize from a single-source optical data set

Spatio-temporal influence of tundra snow properties on Ku-band (17.2 GHz) backscatter

During the 2010/11 boreal winter, a distributed set of backscatter measurements was collected using a ground-based Ku-band (17.2 GHz) scatterometer system at 26 open tundra sites. A standard snow-sampling procedure was completed after each scan to evaluate local variability in snow layering, depth, density and water equivalent (SWE) within the scatterometer field of view. The shallow depths and large basal depth hoar encountered presented an opportunity to evaluate backscatter under a set of previously untested conditions. Strong Ku-band response was found with increasing snow depth and snow water equivalent (SWE). In particular, co-polarized vertical backscatter increased by 0.82 dB for every 1 cm increase in SWE (R2 = 0.62). While the result indicated strong potential for Ku-band retrieval of shallow snow properties, it did not characterize the influence of sub-scan variability. An enhanced snow-sampling procedure was introduced to generate detailed characterizations of stratigraphy within the scatterometer field of view using near-infrared photography along the length of a 5m trench. Changes in snow properties along the trench were used to discuss variations in the collocated backscatter response. A pair of contrasting observation sites was used to highlight uncertainties in backscatter response related to short length scale spatial variability in the observed tundra environment