Different vegetation canopy reflectance models have been reviewed. Several models based on the one-dimensional radiative transfer equation developed by Kubelka and Munk are discussed with respect to their suitability for remote sensing applications. Another class of models based on radiative transfer within a finite number of uniform layers has been treated as well. Out of numerous results obtained from canopy reflectance measurements, examples are given concerning cause-effect relationships, non-Lambertian reflectance, model verification, crop yield estimation and the assessment of the influence of the atmosphere and the observation geometry. Recommendations for future research are discussed, which are dealing with crop growth monitoring, model inversion and application of atmospheric correction models. The effect of the polarization of scattered radiation should be also investigated in relation to the increased interest in data acquisition under varying view angles.

Electro-optical terrain reflectance modeling is one of the components required in our overall capability to simulate remote sensing measurement systems as an aid to the sensor or information processing designer. Given that sensor fields of view may vary from a few centimeters to several meters and that measurement devices may be placed at varying heights above the terrain surface, modeling of complex combinations of terrain classes or media with respect to both vertical and horizontal scales may be required. This paper addresses the issue of combining modeling approaches for different classes of materials in the optical regime and recommends a more formal approach to the radiative characterization of media properties as well as the calculation of the bidirectional reflectance distribution functions.

This paper reviews the atmospheric effects on remote sensing of surface reflectance. The scattering and absorption of sunlight by atmospheric molecules and aerosols affects the quality of images of the surface remotely sensed from satellites and aircrafts. The concentration and characteristics of the atmospheric aerosols vary from place to place and vary with time. The effect of atmospheric aerosols on the upward radiance depends on their optical thickness, scattering phase function and absorption. These parameters result from the aerosol concentration, composition, and the relative humidity. For high resolution images the aerosol scale height is also of importance. The radiative transfer theory that predicts the atmospheric radiances for a given surface and atmosphere is a well established theory for the case of uniform surfaces (or low resolution data). Some radiative transfer models exist for nonuniform surfaces and others are being developed. Recent field experiment and laboratory simulation data confirm the need for these models and can be used for their testing. It is shown that the atmospheric effect reduces the apparent resolution of satellite imagery and causes errors in the classification of surface fields. Suggestions for correction procedures are given. Such corrections can be based on ground observations, on satellite radiances above dark areas, or on climatologic information, depending on the accuracy of the corrections needed. The chosen correction algorithm depends also on the image resolution and the specific remote sensing application.

The importance of accurate absolute radiometric calibration is discussed by reference to the needs of those wishing to validate or use models describing the interaction of electromagnetic radiation with the atmosphere and earth surface features. The in-flight calibration methods used for the Landsat Thematic Mapper (TM) and the Systeme Probatoire d'Observation de la Terre, Haute Resolution Visible (SPOT/HRV) systems are described and their limitations discussed. The questionable stability of in-flight absolute calibration methods suggests the use of a radiative transfer program to predict the apparent radiance, at the entrance pupil of the sensor, of a ground site of measured reflectance imaged through a well characterized atmosphere. The uncertainties of such a method are discussed.

Oceanographers and coastal researchers have long acknowledged the need for making synoptic observations. However, the conventional observational techniques, employing slow and costly oceanographic ships, preclude an adequate sampling of many phenomena that vary rapidly in both space and time. This is particularly true of the properties of the ocean surface. Remote sensing from aircraft and satellites, by its very nature, provides the required synoptic coverage. The development of quantitative techniques for the analysis of satellite data is already contributing to the understanding of the spatial distribution and dynamics of coastal and oceanographic parameters. Therefore, remote sensing has become quite important for coastal research and marine resources monitoring. Coastal applications of remote sensing require a wide assortment of sensors ranging from aerial film cameras for coastline erosion mapping; to multispectral scanners for marsh biomass or ocean chlorophyll concentration studies; to thermal infrared scanners for mapping surface temperatures and currents; and microwave devices for salinity or wave measurements. The purpose of this paper is to summarize the state of the art of remote sensing of coastal and ocean properties and to point out new sensing techniques needed for meeting user requirements.

Spectral remote sensing has been practiced on a large scale since the launch of Landsat 1 in 1972. The limited information contained in this spectrally undersampled data set has led to the development of sophisticated statistical-inferential methods for data analysis. The results are usually limited by the availability of ground truth information. Recent technological developments have made it feasible to create narrow-band, contiguous, spectral image data sets that make possible the identification of surface cover materials based on the complete reflectance spectrum for each picture element. This capability will revolutionize the use of remote sensing data and require new deterministic image processing techniques to extract the full information content from the data. Sensors, based on the concept of imaging spectrometry and the new technology of area array infrared detectors, have been constructed and are candidates for shuttle and space platform flights.

The concept of inferring surface cover type and condition from measurements of reflected or emitted radiation has its roots in spectroscopy. In that discipline physicists and physical chemists enjoyed great success in identifying chemical compounds from observing their "spectral signatures." Much of the early research in remote sensing followed this paradigm. Reflectance spectra were obtained for various natural materials: leaf reflectances and transmittances (refs. 1,2,3), and soils reflectance (refs. 4,5) to name a few. Spectral signature catalogs were developed (ref. 6). While these and smaller studies were quite useful and provided the rationale for the band selection of modern aircraft and spacecraft multispectral sensors, it became evident that the paradigm which had provided the basis for the spectroscopic identification of materials, was incomplete when applied to the inference of type and condition of materials in a natural environment. Briefly stated, it was found that one could not collect a remote sensing signature from an unknown ground cover class at a particular time and place and match that signature with an a priori catalog value to infer the properties of the unknown cover class. Thus, for remote sensing, the traditional definition of "spectral signature" found limited utility.

The potential for remotely sensed data being used by farmers in making day-to-day management decisions has not been fully realized because the data have generally not been available in real-time. Furthermore, the relationships between spectral data and crop and soil properties have yet to be incorporated into the expert systems necessary for rapid data analysis and interpretation. This review details some major requirements for a farm oriented remote sensing system, and evaluates the state-of-the-art concerning relationships between spectral data and crop and soil properties. Current and proposed remote sensing systems geared toward farm management are discussed.

Oblique illumination of irregular topography generates a pattern of highlighting and shadow as slopes facing the solar beam receive direct illumination, and as those on opposite sides of ridges are shadowed. On remotely sensed images these patterns appear as alternating dark and bright regions that reveal approximate positions of ridges and valleys. Knowledge of scene-specific variables (such as sun angle and elevation), general knowledge of geomorphology, atmospheric scattering, and spectral characteristics of landscapes permits reconstruction of the topography from its manifestation on the image. Raw image data record combined effects of topography, atmosphere, and varied spectral reflections of surface materials as a single image. Our interpretation procedure isolates brightnesses caused by direct and indirect illumination, varied material reflectances, and topographic modulation. From varied brightnesses caused by direct and indirect illumination, positions of ridges and valleys can be approximated. From variations in material reflectance, large rivers (channels with large areas of open water) can be detected. Finally, relative elevations can be estimated from analysis of drainage and ridge patterns using a strategy of "elevation growing" that assigns increasing elevation values to pixels as they are positioned at greater distances from rivers or other valley pixels already assigned elevations. Elevations along directions perpendicular to valleys increase as they climb towards the ridges interpreted from the shadow pattern. The result is an image that approximates the patterns of relief of the original topography.

With the availability of data from Landsat and other remote sensing satellites, the potential scope of the problems attacked becomes global. This in turn encourages and allows the use of widely disparate data types. However, this use has been hampered by the diversity of data formats, archival conditions and procedures, and the need for data registration. Ability to locate, obtain, and use the data has not been generally expeditious. Systems are being planned to test the ability to alleviate some of these problems.

The remote sensing community has a major opportunity at the present time which it must seize and use effectively. Activity in the remote sensing research community is at a very low ebb at this time. With a few exceptions such as some experimentation with the use of radar for remote sensing being conducted from the Space Shuttle missions, very little research work is now in progress in the U.S. Indeed over the past 10 years, the effort to create new capability has been steadily phased down in favor of efforts to apply the known technology.

Satellite programs of the 1980's such as Landsat-5, Shuttle/Spacelab and SPOT will produce a variety of image data recorded by film cameras, electro-optical sensors and synthetic aperture radars which can be used for mapping tasks. Efforts will focus on producing topographic, thematic and image maps at scales of 1:25,000 to 1:100,000. Studies to date indicate that spatial resolution and geometric fidelity are the most important factors controlling the completeness of detail and accuracy to which terrain coordinates can be derived. As most satellite systems do not currently provide both spacecraft position and attitude data to sufficient accuracies for cartographic purposes, terrain coordinates must be derived from image measurements referenced to ground control. Of the satellite systems currently planned for operation during the next two years, the Large Format Camera and Metric Camera employed on board the Shuttle will provide photographs with resolution and geometric characteristics compatible with map products at 1:50,000 scale. SPOT, which will produce both 10m panchromatic and 20m multispectral data in stereo formats, will provide digital data of the Earth suitable for cartographic applications.

This paper was written and is being presented at a critical time in the life of land remote sensing. It is a technology that is just over twenty years old, if one starts counting from the development in 1961 of the now famous multispectral scanner at the Willow Run Laboratories of the University of Michigan; now the Environmental Research Institute of Michigan. During this brief period the technology has undergone a remarkable growth, achieved a number of the goals and objectives of the research team, and provided ample evidence of the wisdom shown by the government sponsors in making the required federal investment. That this is a critical time is demonstrated by the fact that an intensive effort is under way to create a Commercial Land Remote Sensing (CRLS) system including both space and ground segments. If one takes the optimistic view then the term critical simply means that this is a major milestone in the program. There is however a large segment of the user community that believes that the program is at a make or break point in its history.