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Matched field inversion (MFI) has been studied extensively in shallow water environments to invert for the geoacoustic parameters using a vertical or horizontal linear array (VLA/HLA). Deploying VLA/HLA at sea is not easy, which limits the use of MFI in practice for large-area surveys. In the future, many inexpensive transceivers will likely be deployed in the ocean forming distributed networks. Geoacoustic parameters could potentially be inverted from signals received on the distributed sensors from distributed sources, such as that generated by a towed source or passing by ships, referred to as a multiple-input–multiple-output (MIMO) system. The data from each source can be processed like a virtual HLA; different virtual HLA geometries are generated for different source locations, providing propagation data along different azimuthal angles, referred to as spatial diversity. The signals between the distributed receivers may not be synchronized, hence only the frequency cost function will be used. The inversion performances using the MIMO system are evaluated using simulated data to compare with that obtained using the traditional methods with a VLA/HLA. Practical applications of this method are proposed. Some technical issues are studied using the Shallow Water 2006 data.

Foliar chemical constituents are important indicators for understanding vegetation growing status and ecosystem functionality. Provided the noncontact and nondestructive traits, the hyperspectral analysis is a superior and efficient method for deriving these parameters. In practice, the spectral noise issue significantly impacts the performance of the hyperspectral retrieving system. To systematically investigate this issue, by introducing varying levels of noise to spectral signals, an assessment on noise-resistant capability of spectral features and models for retrieving concentrations of chlorophyll, carotenoids, and leaf water content was conducted. Given the continuous wavelet analysis (CWA) showed superior performance in extracting critical information associating plants biophysical and biochemical status in recent years, both wavelet features (WFs) and some conventional features (CFs) were chosen for the test. Two datasets including a leaf optical properties experiment dataset $(n\,= \,330)$ , and a corn leaf spectral experiment dataset $(n\,= \,213)$ were used for analysis and modeling. The results suggested that the WFs had stronger correlations with all leaf chemical parameters than the CFs. According to an evaluation by decay rate of retrieving error that indicates noise-resistant capability, both WFs and CFs exhibited strong resistance to spectral noise. Particularly for WFs, the noise-resistant capability is relevant to the scale of the features. Based on the identified spectral features, both univariate and multivariate retrieving models were established and achieved satisfactory accuracies. Synthesizing the retrieving accuracy, noise resistivity, and model's complexity, the optimal univariate WF-models were recommended in practice for retrieving leaf chemical parameters.

The radiance recorded at a sensor is not fully a representative of Earth surface section features but is altered by atmosphere. In this study, we evaluated three atmospheric correction (AC) methods (a typical empirical modeling method, a radiative transfer modeling approach, and a combination of the both methods) in identifying urban tree species/groups with high-resolution WorldView-2 (WV2) imagery in the City of Tampa, FL, USA. We tested whether AC methods were necessary in urban tree species discrimination. In situ spectral measurements were taken from tops of tree canopy and tree crowns were delineated from WV2 imagery. Two-sample $\bm{t}$ -tests, repeated measures ANOVA (RANOVA) tests, linear discriminant analysis (LDA), and classification and regression trees (CART) classifiers were used to test the spectral difference between in situ spectra and atmospherically corrected image spectra and to discriminate urban tree species/groups. The experimental results demonstrate that 1) the empirical line-based AC methods were relatively more effective than a radiative transfer-based AC model to atmospherically correct the image data, due to lacking accurate and reliable atmospheric parameters to run the radiative transfer model and 2) the AC processing to WV2 imagery was unnecessary in identifying seven tree species/groups in this particular case, most likely because the WV2 image data used in this analysis were acquired on a single date and covered a relatively small area ( ${303}\;\mathbf{km}^{2}$ ). The study results also indicate that compared with a nonparametric classifier CART, the parametric classifier LDA produced higher overall accuracy (55% vs. 48%) for identifying the seven species/groups.

The prevalence of powdery mildew (PM) in winter wheat field has a severe impact on crop production. An effective and timely forecast of the disease at a regional scale is necessary to control and prevent it. In this study, both meteorological and remotely sensed observations associated with crop characteristics and habitat traits were integrated for modeling the PM occurrence probability. With an effective feature selection procedure, four meteorological factors, including precipitation, temperature, sun radiation, humidity, and two remotely sensed features including reflectance of red band ( ${\rm R}_{\rm R}$ ) demonstrate that the disease risk maps were able to depict the approximately spatial distribution of PM and its temporal dynamic in the study area. Compared with the model constructed with meteorological data only, the integrated model constructed with both remote sensing and meteorological data has produced a higher accuracy (increasing overall accuracy from 69% to 78%) of forecasting the PM occurrence. This suggests that there would be a great potential for predicting the PM occurrence probability by integrating both meteorological and remote sensing data at a regional scale. In the future, multiple forms of information (e.g., Web sensors networks data) are expected to be incorporated in the disease-forecasting model to further improve its performance for forecasting the disease occurrence (e.g., PM) at a regional scale.

Continuous monitoring leaf area index (LAI) of field crops in a growing season has a great challenge. The development of remote sensing technology provides a good tool for timely mapping LAI regionally. In this study, hyperspectral reflectance data (405-835 nm) obtained from an airborne hyperspectral imager (Pushbroom Hyperspectral Imager) were used to model LAI of winter wheat canopy in the 2002 crop growing season. LAI was modeled based on its semi-empirical relationships with six vegetation indices (VIs), including ratio vegetation index (RVI), modified simple ratio index (MSR), normalized difference vegetation index (NDVI), a newly proposed index NDVI-like (which resembles NDVI), modified triangular vegetation index (MTVI2), and modified soil adjusted vegetation index (MSAVI). To assess the performance of these VIs, root mean square errors (RMSEs) and determination coefficient (R-2) between estimated LAI and measured LAI were reported. Our result showed that NDVI-like was the most accurate predictor of LAI. The inclusion of a green band in MTVI2 trended to give a rise to a much quicker saturation with increase of LAI (e. g., over 3.5). MSAVI and MTVI2 showed comparable but lower potential than NDVI-like in estimating LAI. RVI and MSR demonstrated their lowest prediction accuracy, implying that they are more likely to be affected by environmental conditions such as atmosphere and cloud, thus cannot properly reflect the properties of winter wheat canopy. Our results support the use of VIs for a quick assessment of seasonal variations in winter wheat LAI. Among the indices we tested in this study, the newly developed NDVI-like model created the most accurate and reliable results.

The vegetation indices from hyperspectral data have been shown to be effective for indirect monitoring of plant diseases. However, a limitation of these indices is that they cannot distinguish different diseases on crops. We aimed to develop new spectral indices (NSIs) that would be useful for identifying different diseases on crops. Three different pests (powdery mildew, yellow rust, and aphids) in winter wheat were used in this study. The new optimized spectral indices were derived from a weighted combination of a single band and a normalized wavelength difference of two bands. The most and least relevant wavelengths for different diseases were first extracted from leaf spectral data using the RELIEF-F algorithm. Reflectance of a single band extracted from the most relevant wavelengths and the normalized wavelength difference from all possible combinations of the most and least relevant wavelengths were used to form the optimized spectral indices. The classification accuracies of these new indices for healthy leaves and leaves infected with powdery mildew, yellow rust, and aphids were 86.5%, 85.2%, 91.6%, and 93.5%, respectively. We also applied these NSIs for nonimaging canopy data of winter wheat, and the classification results of different diseases were promising. For the leaf scale, the powdery mildew-index (PMI) correlated well with the disease index (DI), supporting the use of the PMI to invert the severity of powdery mildew. For the canopy scale, the detection of the severity of yellow rust using the yellow rust-index (YRI) showed a high coefficient of determination ( \mbiR 2 = 0.86) between the estimated DI and its observations, suggesting that the NSIs may improve disease detection in precision agriculture application.

Wheat aphid, Sitobion avenae F. is one of the most destructive pests that emerge in northwest China almost every year, impacting on the production of winter wheat. Hyperspectral remote sensing has been demonstrated to be superior to a traditional method in detecting diseases and pests. In this study, spectral features (SFs) were examined by four methods to detect aphid density of wheat leaf and model was established to estimate aphid density using partial least square regression (PLSR). A total of 60 wheat leaves with different aphid densities were selected. Aphid density of the leaves was first visually estimated, and then the reflectance of leaves was measured in the spectral range of 350-2500 nm using a spectroradiometer coupling with a leaf clip. A total of 48 spectral features were obtained and examined via correlation analysis, independent t-test by spectral derivative method, continuous removal method, continuous wavelet analysis (CWA) and commonly used vegetation indices for stress detection. Based on variable importance in projection (VIP), five spectral features (VIP ≥ 1) were selected from 17 spectral features due to their strong correlation with aphid density (R2 ≥ 0.5) to establish the model for estimating aphid density by PLSR. The result showed that the model had a great potential in detecting aphid density with a relative root mean square error (RMSE) of 15 and a coefficient of determination (R2) of 0.77.