A Review of Wetland Remote Sensing <s> Wetland Soil <s> The apparent electrical conductivity (σa) of soil is influenced by a complex combination of soil physical and chemical properties. For this reason, σa is proposed as an indicator of plant stress and potential community structure changes in an alkaline wetland setting. However, assessing soil σa is relatively laborious and difficult to accomplish over large wetland areas. This work examines the feasibility of using the hyperspectral reflectance of the vegetation canopy to characterize the σa of the underlying substrate in a study conducted in a Central California managed wetland. σa determined by electromagnetic (EM) inductance was tested for correlation with in-situ hyperspectral reflectance measurements, focusing on a key waterfowl forage species, swamp timothy (Crypsis schoenoides). Three typical hyperspectral indices, individual narrow-band reflectance, first-derivative reflectance and a narrow-band normalized difference spectral index (NDSI), were developed and related to soil σa using univariate regression models. The coefficient of determination (R2) was used to determine optimal models for predicting σa, with the highest value of R2 at 2206 nm for the individual narrow bands (R2 = 0.56), 462 nm for the first-derivative reflectance (R2 = 0.59), and 1549 and 2205 nm for the narrow-band NDSI (R2 = 0.57). The root mean squared error (RMSE) and relative root mean squared error (RRMSE) were computed using leave-one-out cross-validation (LOOCV) for accuracy assessment. The results demonstrate that the three indices tested are valid for estimating σa, with the first-derivative reflectance performing better (RMSE = 30.3 mS m-1, RRMSE = 16.1%) than the individual narrow-band reflectance (RMSE = 32.3 mS m-1, RRMSE = 17.1%) and the narrow-band NDSI (RMSE = 31.5 mS m-1, RRMSE = 16.7%). The results presented in this paper demonstrate the feasibility of linking plant-soil σa interactions using hyperspectral indices based on in-situ spectral measurements. <s> BIB001 </s> A Review of Wetland Remote Sensing <s> Wetland Soil <s> Abstract Developing spectral models of soil properties is an important frontier in remote sensing and soil science. Several studies have focused on modeling soil properties such as total pools of soil organic matter and carbon in bare soils. We extended this effort to model soil parameters in areas densely covered with coastal vegetation. Moreover, we investigated soil properties indicative of soil functions such as nutrient and organic matter turnover and storage. These properties include the partitioning of mineral and organic soil between particulate (>53 μm) and fine size classes, and the partitioning of soil carbon and nitrogen pools between stable and labile fractions. Soil samples were obtained from Avicennia germinans mangrove forest and Juncus roemerianus salt marsh plots on the west coast of central Florida. Spectra corresponding to field plot locations from Hyperion hyperspectral image were extracted and analyzed. The spectral information was regressed against the soil variables to determine the best single bands and optimal band combinations for the simple ratio (SR) and normalized difference index (NDI) indices. The regression analysis yielded levels of correlation for soil variables with R2 values ranging from 0.21 to 0.47 for best individual bands, 0.28 to 0.81 for two-band indices, and 0.53 to 0.96 for partial least-squares (PLS) regressions for the Hyperion image data. Spectral models using Hyperion data adequately (RPD > 1.4) predicted particulate organic matter (POM), silt + clay, labile carbon (C), and labile nitrogen (N) (where RPD = ratio of standard deviation to root mean square error of cross-validation [RMSECV]). The SR (0.53 μm, 2.11 μm) model of labile N with R2 = 0.81, RMSECV= 0.28, and RPD = 1.94 produced the best results in this study. Our results provide optimism that remote-sensing spectral models can successfully predict soil properties indicative of ecosystem nutrient and organic matter turnover and storage, and do so in areas with dense canopy cover. <s> BIB002
Soil quantity, especially the content of N and organic matter, will directly affect the health of the vegetation, which can be characterized by hyperspectral imagery. To examine the possibility of using hyperspectral reflectance of vegetation canopy to inverse the soil electrical conductivity, Li et al. BIB001 selected Crypsis schoenoides in Central California-managed wetland as study subject and used three indices derived from field spectrometer hyperspectral data, individual narrow-band reflectance, first-derivative reflectance, and narrow-band normalized difference spectral index (NDSI), to determine the soil electrical conductivity using an univariate regression model. These indices provided valid results based on the high correlation coefficient (R 2 ) of 0.56, 0.59, and 0.57 for individual narrow-band reflectance, first-derivative reflectance, and narrow-band NDSI, respectively. Anne et al. BIB002 argued that Hyperion hyperspectral images have the potential to predict the soil properties that indicate the ecosystem nutrients and organic matter storage. They collected soil samples of the mangrove forest and salt marsh on western coast of Central Florida. The single-band and two-band indexes and partial least squares (PLS) statistical models for Hyperion hyperspectral image were tested; the best statistical models were derived from the two-band index and PLS.