This paper gives an overview of how certain meteorological data used in studies of the population dynamics of arthropod vectors of disease may be predicted using remotely sensed, satellite data. Details are given of the stages of processing necessary to convert digital data arising from satellite sensors into ecologically meaningful information. Potential sources of error in these processing steps are also highlighted. Relationships between ground-measured meteorological variables (saturation deficit, ground temperature and rainfall) and data from both the National Oceanic and Atmospheric Administration's, polar-orbiting, meteorological satellites and the geostationary, Meteosat satellite are defined and examples detailed for Africa. Finally, the current status of existing satellite platforms and future satellite missions are reviewed and potential data availability discussed. How such satellite-based predictions have proved valuable in understanding the distribution of tsetse fly species in Cote d'Ivoire and Burkina Faso will be the subject of a future review.
Techniques developed for the prediction of winter wheat yields from remotely sensed data indicating crop status over the growing season are tested for their applicability to corn. Ground-based spectral measurements in the Landsat Thematic Mapper bands 3 (0.62-0.69 microns), 4 (0.76-0.90 microns) and 5 (1.55-1.75 microns) were performed at one-week intervals throughout the growing season for 24 plots of corn, and analyzed to derive spectral ratios and normalized spectral differences of the IR and shortwave IR bands with the red. The ratios of the near IR and shortwave IR bands are found to provide the highest and most consistent correlations with corn yield and dry matter accumulation, however the value of band 5 could not be tested due to the absence of water stress conditions. Integration of spectral ratios over several dates improved the correlations over those of any single date by achieving a seasonal, rather than instantaneous, estimate of crop status. Results point to the desirability of further tests under other growth conditions to determine whether satellite-derived data will be useful in providing corn yield information.
Four different approaches to the classification of land cover for whole continents using multitemporal images of the normalized difference vegetation index derived from the Advanced Very High Resolution Radiometer of the NOAA series of satellites are discussed. The first approach uses only two dates from different seasons and classification dependent upon subdivision of the resultant two-dimensional feature space by an analyst using a track ball. The second approach involves a similar method of partitioning the feature space, but with the two dimensions being the first and second principal components derived from 13 four-week composite images. The third approach uses the maximum likelihood rule to derive the classified map. In the fourth approach, the amount of deviation from characteristic curves is used as a basis for classification.
The invertibility of an accurate discrete ordinates canopy reflectance model is investigated through a series of experiments. Effects of different canopy types, noise levels, spectral ranges, and sampling geometries [including the NOAA Advanced Very High Resolution Radiometer (AVHRR) and the proposed Multi-angle Imaging SpectroRadiometer (MISR) satellite sampling schemes] are considered. Both error-free synthetic bidirectional reflectance data and empirical field reflectance data are utilized. Results suggest that the model can retrieve soil and canopy parameters with reasonable accuracy in most cases, and surface state parameters (absorbed radiation, spectral albedo and photosynthetic efficiency) with high accuracy in all cases. The efficiency of several commonly used optimization algorithms is also assessed, and a model sensitivity study is conducted.
No abstract is provided for this article.
No abstract is provided for this article.
Techniques developed for the prediction of winter wheat yields from remotely sensed data indicating crop status over the growing season are tested for their applicability to corn. Ground-based spectral measurements in the Landsat Thematic Mapper bands 3 (0.62-0.69 microns), 4 (0.76-0.90 microns) and 5 (1.55-1.75 microns) were performed at one-week intervals throughout the growing season for 24 plots of corn, and analyzed to derive spectral ratios and normalized spectral differences of the IR and shortwave IR bands with the red. The ratios of the near IR and shortwave IR bands are found to provide the highest and most consistent correlations with corn yield and dry matter accumulation, however the value of band 5 could not be tested due to the absence of water stress conditions. Integration of spectral ratios over several dates improved the correlations over those of any single date by achieving a seasonal, rather than instantaneous, estimate of crop status. Results point to the desirability of further tests under other growth conditions to determine whether satellite-derived data will be useful in providing corn yield information.
A self-contained, portable, hand-radiometer designed for field usage was constructed and tested. The device, consisting of a hand-held probe containing three sensors and a strap supported electronic module, weighs 4 1/2 kilograms. It is powered by flashlight and transistor radio batteries, utilizes two silicon and one lead sulfide detectors, has three liquid crystal displays, sample and hold radiometric sampling, and its spectral configuration corresponds to LANDSAT-D's thematic mapper bands. The device was designed to support thematic mapper ground-truth data collection efforts and to facilitate 'in situ' ground-based remote sensing studies of natural materials. Prototype instruments were extensively tested under laboratory and field conditions with excellent results.
Multitemporal satellite data have application in the detection and quantification of drought through the ability of these data to estimate the photosynthetic capacity of the terrestrial surface and record microwave surface brightness at the 37 GHz frequency. With proper calibration and registration, comparisons can be made between and among years for specific months using the photosynthetic capacity and the 37 GHz microwave surface brightness for selected time periods or growing seasons. This technology has application in identifying and quantifying areas experiencing drought.
No abstract is provided for this article.
Spatially explicit and temporally continuous estimates of photosynthesis will be of great importance for increasing our understanding of and ultimately closing the terrestrial carbon cycle. Current capabilities to model photosynthesis, however, are limited by accurate enough representations of the complexity of the underlying biochemical processes and the numerous environmental constraints imposed upon plant primary production. A potentially powerful alternative to model photosynthesis through these indirect observations is the use of multi-angular satellite data to infer light-use efficiency (ε) directly from spectral reflectance properties in connection with canopy shadow fractions. Hall et al. (this issue) introduced a new approach for predicting gross ecosystem production that would allow the use of such observations in a data assimilation mode to obtain spatially explicit variations in ε from infrequent polar-orbiting satellite observations, while meteorological data are used to account for the more dynamic responses of ε to variations in environmental conditions caused by changes in weather and illumination. In this second part of the study we implement and validate the approach of Hall et al. (this issue) across an ecologically diverse array of eight flux-tower sites in North America using data acquired from the Compact High Resolution Imaging Spectroradiometer (CHRIS) and eddy-flux observations. Our results show significantly enhanced estimates of ε and therefore cumulative gross ecosystem production (GEP) over the course of one year at all examined sites. We also demonstrate that ε is greatly heterogeneous even across small study areas. Data assimilation and direct inference of GEP from space using a new, proposed sensor could therefore be a significant step towards closing the terrestrial carbon cycle.
