This letter describes a laboratory experiment where reflectance signatures of three plant species are measured at a leaf level in the 3-15-µm spectral domain. The leaf samples are progressively dried in order to analyze the behavior of their spectral signature according to the variations in their water content. Our first objective aims to underline leaf water content (LWC) impact on the spectral signatures. This work is a necessary step toward further studies dealing with interpretation of multispectral remote sensing data or estimation of water stress and energy budget. The drying process and measurement method are detailed. This letter deals with dry and fresh leaves (as found in literature) and considers intermediate water content levels as well. For intermediate LWC levels, our analysis outlines some important results: The spectral domain may be divided into two parts, namely, 3-5.5 and 5.5-15 µm, each corresponding to different impacts of LWC variation; both sides of a cherry tree leaf have not the same behavior according to the water content amount; and, in the 8-15-µm, the drying process impacts when the LWC becomes lower than a threshold value (around 30%).

A new semi-empirical soil model simulating the spectral signatures of bare soils in the optical domain 0.4-14 µm according to surface moisture content variation is presented and applied to several databases. The model specification is based on laboratory spectral reflectance measurements of many bare soils at different moisture contents. The measurement analysis leads to the definition of groups of bare soil samples according to their spectral behaviours. These laboratory measurements are made also to characterize the impact of soil moisture on spectral signatures (reflectance levels increasing with moisture content) and to give information on absorption peaks related to soil mineral components (hydroxyl, carbonate, and quartz). The procedure of modelling the spectral signatures of bare soil groups according to moisture content is discussed. The model is applied to a laboratory reflectance database and to the data available in the literature. The spectral reflectances, estimated with a semi-empirical model, compare favourably with reflectance observations.

This work aims to compare the performance of new methods to estimate the Soil Moisture Content (SMC) of bare soils from their spectral signatures in the reflective domain (0.4–2.5 μm) in comparison with widely used spectral indices like Normalized Soil Moisture Index (NSMI) and Water Index SOIL (WISOIL). Indeed, these reference spectral indices use wavelengths located in the water vapour absorption bands and their performance are thus very sensitive to the quality of the atmospheric compensation. To reduce these limitations, two new spectral indices are proposed which wavelengths are defined using the determination matrix tool by taking into account the atmospheric transmission: Normalized Index of Nswir domain for Smc estimatiOn from Linear correlation (NINSOL) and Normalized Index of Nswir domain for Smc estimatiOn from Non linear correlation (NINSON). These spectral indices are completed by two new methods based on the global shape of the soil spectral signatures. These methods are the Inverse Soil semi-Empirical Reflectance model (ISER), using the inversion of an existing empirical soil model simulating the soil spectral reflectance according to soil moisture content for a given soil class, and the convex envelope model, linking the area between the envelope and the spectral signature to the SMC. All these methods are compared using a reference database built with 32 soil samples and composed of 190 spectral signatures with five or six soil moisture contents. Half of the database is used for the calibration stage and the remaining to evaluate the performance of the SMC estimation methods. The results show that the four new methods lead to similar or better performance than the one obtained by the reference indices. The RMSE is ranging from 3.8% to 6.2% and the coefficient of determination R2 varies between 0.74 and 0.91 with the best performance obtained with the ISER model. In a second step, simulated spectral radiances at the sensor level are used to analyse the sensitivity of these methods to the sensor spectral resolution and the water vapour content knowledge. The spectral signatures of the database are then used to simulate the signal at the top of atmosphere with a radiative transfer model and to compute the integrated incident signal representing the spectral radiance measurements of the HYMAP airborne hyperspectral instrument. The sensor radiances are then corrected from the atmosphere by an atmospheric compensation tool to retrieve the surface reflectances. The SMC estimation methods are then applied on the retrieve spectral reflectances. The adaptation of the spectral index wavelengths to the HyMap sensor spectral bands and the application of the convex envelope and ISER models to boarder spectral bands lead to an error on the SMC estimation. The best performance is then obtained with the ISER model (RMSE of 2.9% and R2 of 0.96) while the four other methods lead to quite similar RMSE (from 6.4% to 7.8%) and R2 (between 0.79 and 0.83) values. In the atmosphere compensation processing, an error on th...