Laboratory measurements of the spectral reflectance of leaves from a variety of species were made over the 400-2500 nm range using a spectroradiomctcr. The relation between spectral reflectance and leaf water content was investigated and the effect of differences in leaf structure were found to be important. The first derivative of the reflectance spectrum, at wavelengths corresponding to the slopes on the edges of the water absorption bands, was found to be highly correlated with leaf water content and insensitive to differences in leaf structure.

The empirical line method is an atmospheric correction technique that provides an alternative to radiative transfer modelling approaches. It offers a relatively simple means of surface reflectance calibration, providing that a series of invariant-in-time calibration target measurements are available. This technique has been applied with variable success to both airborne data and coarser spatial resolution satellite sensor data. However, with the advent of higher spatial resolution space-borne sensors there is a need to re-evaluate its potential. The empirical line method was tested for correcting multispectral IKONOS imagery acquired over the tropical island of San Andres, Colombia. The high spatial resolution (4 m) of the data made it possible to identify a number of homogeneous targets with a range of reflectances that were used for the calibration. Coefficients of determination of the prediction equations observed were large, ranging from 0.96-0.99 for each of the four wavebands. An accuracy assessment was performed using a set of independent targets. It demonstrated that the empirical line method can be applied to correct such imagery with accurate results.

This paper uses the Special Issue of the International Journal of Remote Sensing on Remote Sensing of the Coastal Marine Environment to highlight recent advances in knowledge of remote sensing of the coastal zone and to define a series of priorities where future research into the application should be addressed. Advances were identified in the benefit of high spatial and spectral resolution data and complementary remote sensing techniques (e.g. optical and acoustic, optical and Synthetic Aperture Radar (SAR)). Further benefits are identified in rapid and more frequent data acquisition, faster and more automated processing and a greater sampling intensity over conventional field-based techniques. Issues associated with adoption of remotely sensed data for management are discussed. Research priorities include the need for improved understanding and description of biotope classes and the functional interpretation of biotope maps and continued developments in understanding the radiative transfer properties of coastal environments. New knowledge is required on spatial and temporal variations of water column optical properties and its constituents. Methods for the best approaches to processing hyperspectral data require further investigation, as does the need for further testing of hyperspectral sensors for bottom type discrimination using data obtained at space-borne altitudes. Areas of value which continue to remain poorly investigated include the improvements to be gained from synergistic use of multi-wavelength remote sensing approaches, change detection techniques and multi-temporal comparisons and knowledge-based approaches to improve classification. The importance of specifically dedicated coastal zone sensors is discussed, as is alternative means of deployment (e.g. International Space Station (ISS) and Un-inhabited Aerial Vehicles (UAVs)). The potential role of airborne digital photography for marine mapping is highlighted. The lack of accurate near-shore bathymetric data is identified as a key limitation in the application of geospatial data to coastal environments.

Underwater image restoration is of significant importance in unveiling the underwater world. Numerous techniques and algorithms have been developed in recent decades. However, due to fundamental difficulties associated with imaging/sensing, lighting, and refractive geometric distortions in capturing clear underwater images, no comprehensive evaluations have been conducted with regard to underwater image restoration. To address this gap, we constructed a large-scale real underwater image dataset, dubbed Heron Island Coral Reef Dataset (‘HICRD’), for the purpose of benchmarking existing methods and supporting the development of new deep-learning based methods. We employed an accurate water parameter (diffuse attenuation coefficient) to generate the reference images. There are 2000 reference restored images and 6003 original underwater images in the unpaired training set. Furthermore, we present a novel method for underwater image restoration based on an unsupervised image-to-image translation framework. Our proposed method leveraged contrastive learning and generative adversarial networks to maximize the mutual information between raw and restored images. Extensive experiments with comparisons to recent approaches further demonstrate the superiority of our proposed method. Our code and dataset are both publicly available.