Jaime Nickeson

Radiometric rectification: Toward a common radiometric response among multidate, multisensor images☆

Abstract A common radiometric response is required for quantitative analysis of multiple satellite images of a scene acquired on different dates with different sensors. We describe a technique to “radiometrically rectify” multiple Landsat images of a scene to a reference image, and evaluate it using a pair of Landsat 5 images acquired 2 years apart. All rectified images should appear as if they were acquired with the same sensor, while observing through the atmospheric and illumination conditions of the reference image. If atmospheric optical depth and sensor calibration date are available for the reference image, then an atmospheric correction algorithm may be used to correct all the rectified images to absolute surface reflectance. The “radiometric rectification” algorithm identifies “radiometric control sets,” i.e., sets of scene landscape elements with a mean reflectance which is expected to change little with time. The average digital count values of these radiometric control sets are used to calculate linear transforms relating digital count values between images. We evaluate the technique empirically with a pair of Landsat 5 TM images of a scene for which surface reflectance and atmospheric optical depth data are available. We also examine its performance under a wide range of atmospheric conditions using simulations based on atmospheric models. We find that the radiometric rectification algorithm performed well for the visible and near infrared bands, adjusting surface reflectance for the effects of relative atmospheric differences to within 1%. The performance is not as good for the midinfrared bands on TM. There are several possible causes for this; we could not determine which was the most important. We conclude from these studies that for scenes containing reflectance stable elements, radiometric rectification should be a useful alternative to atmospheric radiative transfer codes and sensor calibration approaches when reliable atmospheric optical depth data or calibration coefficients are not available. When atmospheric optical data and sensor calibration information are available for one of a sequence of radiometrically rectified images, an atmospheric radiative transfer code may be used to correct each image in the sequence to absolute surface reflectance.

Satellite remote sensing of surface energy balance: Success, failures, and unresolved issues in FIFE

The FIFE staff science group, consisting of the authors, developed and evaluated process models relating surface energy and mass flux, that is, surface rates, to boundary layer and surface biophysical characteristics, that is, surface states. In addition, we developed and evaluated remote sensing algorithms for inferring surface state characteristics. In this paper we report the results of our efforts. We also look in detail at the sensor and satellite platform requirements (spatial resolution and orbital requirements) as driven by surface energy balance dynamics and spatial variability. We examine also the scale invariance of the process models and remote sensing algorithms, that is, to what degree do the remotely sensed parameters and energy balance relations translate from the patch level where they were developed to the mesoscale level where they are required? Finally, we examine the atmospheric correction and calibration issues involved in extending the remotely sensed observations within a season and between years. From these investigations we conclude that (1) existing formulations for the radiation balance and latent heat components of the surface energy balance equation are valid at the patch level. (2) Many of the surface physiological characteristics that parameterize these formulations can be estimated using satellite remote sensing at both local and regional scales; a few important ones cannot. (3) The mathematical structures relating radiation and surface energy flux to remote sensing parameters are, for the most part, scale invariant over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) study area. The conditions for scale invariance are derived. (4) The precision of satellite remote sensing estimates of surface reflectance, calibrated and corrected for atmospheric effects, is no worse than about 1% absolute. The errors may actually be smaller, but an upper bound of 1% results from sampling variance caused by differences among the satellite and ground sensors in spatial resolution, atmospheric effects, and calibration. (5) Afternoon cumulus in the study area required both the Landsat and the SPOT satellites for monitoring of the vegetation dynamics. This result implies the need for multiple polar orbiters, or geosynchronous satellites in an operational implementation. We found that canopy Fpar, the fraction of incident photosynthetically active radiation absorbed by a canopy, can be estimated with an error of about 10% using remote sensing, provided that regional variability in the reflectance of the canopy substrate is dealt with properly. We also found that spectral vegetation indices (VIs) respond primarily to the photosynthetically active radiation absorbed by the live or green component of the canopy as opposed to its necrotic or dead vegetation. This is of critical importance since radiation absorption by the live part of the canopy is the rate-limiting process for photosynthesis and other key process rates such as evaporation. We found for the FIFE study area the surface moisture content at O to 10 cm to be another key rate-limiting variable in photosynthesis and evaporation. At gravimetric soil moisture levels below 20%, photosynthesis and evaporation were strongly attenuated. Only microwave sensors have shown potential for satellite remote sensing of soil moisture and only in the top few centimeters. Hydrological models may also play a critical role in monitoring root zone soil moisture levels, but additional research is needed. From our review of the research of others in FIFE we conclude that downwelling shortwave radiation and surface albedo are also amenable to remote sensing. Unfortunately, from our research we also found that the remote estimation of surface temperature to useful accuracies is problematical; consequently, the use of thermal infrared measurements to infer sensible heat flux is probably not feasible to acceptable accuracies.

Daily MODIS 500 m reflectance anisotropy direct broadcast DB products for monitoring vegetation phenology dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest.

Assessing Honey Bee Equilibrium Range and Forage Supply using Satelite-Derived Phenology

Two important and highly publicized issues regarding honey bees are impacting agricultural pollination and honey production in the United States. These are: (1) the increasing presence of the invasive Africanized Honey Bee (AHB); and (2) the spread of pests and diseases within managed European Honey Bee (EHB) populations that cause major loss of colonies (of which Colony Collapse Disorder (CCD) is the most recent). The primary objective of this research is to improve prediction of the equilibrium range of both the AHB and EHB within the U.S, and to determine the impact of both urbanization and climate change on this equilibrium range. This will be achieved through integration of: climate data; scale hive defined nectar flow measurements; nectar and pollen source distributions; as well as satellite-derived vegetation phenology.

Managing and supporting large integrated and interdisciplinary field studies: The BOREAS example

Large integrated and interdisciplinary field studies, such as the Boreal Ecosystem-Atmosphere Study (BOREAS), are conducted to refine our understanding of the interactions between the land surface and the atmosphere. Viewed as a case study, the BOREAS research objectives and final data set exemplify the complex nature and requirements of earth systems science research. The management and data system activities required to execute the study also echo this complexity. Rather than several research teams providing the needed management and data support, BOREAS management used a dedicated project staff to handle these functions. As the study progressed, the project staff transitioned from support of logistics and study management to information system operation and data publication, drawing upon the background knowledge gained from the earlier stages of the project. Data publication involves the creation and distribution of quality-checked and documented data with all ancillary information required to make it useful to someone unfamiliar with the study. We assert that the success of large integrated and interdisciplinary field studies depends upon having a dedicated staff. This staff focuses on the overall goals of the study throughout all phases of the effort: contributing to project planning, logistics, management, and data collection efforts; distributing, quality checking, and integrating the diverse data sets; working with the science teams to develop standardized data set documentation; integrating the diverse data and documentation for archiving; and publishing the data for long-term use by the larger scientific community. In this paper, the different phases of BOREAS are discussed, and the contributions that the dedicated staff made are examined. The value of spending resources on a centralized staff for project support and data publication activities is also examined.

Assessing the Accuracy of Landscape‐Scale Phenology Products: An International Workshop on the Validation of Satellite‐Based Phenology Products; Dublin, Ireland, 18 June 2010

A 1-day international workshop on the accuracy assessment of phenology products derived from satellite observations of the land surface was held at Trinity College Dublin. This was in conjunction with the larger 4-day Phenology 2010 conference. Phenology is the study of recurring plant and animal life cycle stages (such as leafing and flowering, maturation of agricultural plants, emergence of insects, and migration of birds). The workshop brought together producers of continental- to global-scale phenology products based on satellite data, as well as providers of field observations and tower-mounted near-surface imaging sensors whose data are useful for evaluating the satellite products. The meeting was held under the auspices of the Committee on Earth Observing Satellites (CEOS) Land Product Validation (LPV) subgroup. The mission of LPV is to foster quantitative validation of high-level global land products derived from remotely sensed data and relay results that are relevant to users.

Coordinating Earth Observing System land validation

NASAs Moderate Resolution Imaging Spectroradiometer (MODIS) land product validation project, initiated prior to the launch of the NASA Earth Observing System (EOS) Terra platform in late 1999, provides data, instrument, and information resources for the validation of products that quantify land surface characteristics from MODIS and other satellite sensors. Land products derived from MODIS and other moderate-resolution sensors include, among others, land cover, snow cover extent, surface temperature, leaf area index, fire occurrence, and vegetation productivity. The land validation project infrastructure, developed at NASAs Goddard Space Flight Center (GSFC), involves an integration of NASA-funded researchers, international collaborators, and science data networks. These resources facilitate determination of product uncertainty, which is the definition of validation, through best practice methods. Key to the project is the set of EOS Land Validation Core Sites, a global network of 33 sites (including six added in 2006), which serve as a focus for validation activities. The initial infrastructure and activities were described in Morisette et al. [1999], Justice et al. [2000], and Morisette et al. [2002].

BOREAS RSS-04 1994 Jack Pine Leaf Biochemistry and Modeled Spectra in the SSA

The BOREAS RSS-4 team focused its efforts on deriving estimates of LAI and leaf chlorophyll and nitrogen concentrations from remotely sensed data for input into the Forest BGC model. This data set contains measurements of jack pine (Pinus banksiana) needle biochemistry from the BOREAS SSA in July and August 1994. The data contain measurements of current and year-1 needle chlorophyll, nitrogen, lignin, cellulose, and water content for the OJP flux tower and nearby auxiliary sites. The data have been used to test a needle reflectance and transmittance model, LIBERTY (Dawson et al., in press). The source code for the model and modeled needle spectra for each of the sampled tower and auxiliary sites are provided as part of this data set. The LIBERTY model was developed and the predicted spectral data generated to parameterize a canopy reflectance model (North, 1996) for comparison with AVIRIS, POLDER, and PARABOLA data. The data and model source code are stored in ASCII files.

Remote sensing of PAR interception and net primary production in trembling aspen and black spruce stands

A series of LANDSAT spectral vegetation index images, calibrated to a common reference image, were used to model the phenological dynamics of 30 quaking aspen (Populus tremuloides) and 29 black spruce (Picea mariana) stands in the boreal forest of northeast Minnesota. Phenology models of the stands were used in a simplified species-specific canopy modeling framework to estimate the amount of photosynthetically active radiation (PAR) intercepted by the canopy annually. The relationship between annual PAR interception (/spl Sigma/IPAR) and measured annual above-ground net primary production (AANPP), that is, the dry matter yield of /spl Sigma/IPAR, was examined. Age was a primary determinant of the relationship between /spl Sigma/IPAR and AANPP in aspen, with /spl Sigma/IPAR in young stands exhibiting a strong predictive capability relative to mature stands. This observation is consistent with increased maintenance respiration demands as the ratio of total to foliar biomass increases with stand age. /spl Sigma/IPAR was a good predictor of AANPP in spruce stands. Calculated values of the dry matter yield of /spl Sigma/IPAR were within the range of literature reports for other forest ecosystems, but further research to understand the sources of between-stand variations is required, and on-going.<<ETX>>

BOREAS Elevation Contours over the NSA and SSA in ARC/INFO Generate Format

This data set was prepared by BORIS Staff by reformatting the original data into the ARC/INFO Generate format. The original data were received in SIF at a scale of 1:50,000. BORIS staff could not find a format document or commercial software for reading SIF; the BOREAS HYD-08 team pro-vided some C source code that could read some of the SIF files. The data cover the BOREAS NSA and SSA. The original data were compiled from information available in the 1970s and 1980s. The data are available in ARC/INFO Generate format files.