Search

The European Space Agency (ESA) developed the Sentinel-2 Multispectral Imager (MSI), which offers a higher spatial resolution and shorter repeat coverage, making it an important source for the remote-sensing monitoring of water bodies. Atmospheric correction is crucial for the monitoring of water quality. To compare the applicability of seven publicly available atmospheric correction processors (ACOLITE, C2RCC, C2XC, iCOR, POLYMER, SeaDAS, and Sen2Cor), we chose complex and diverse lakes in Qinghai Province, China, as the research area. The lakes were divided into three types based on the waveform characteristics of Rrs: turbid water bodies (class I lakes) represented by the Dabusun Lake (DBX), clean water bodies (class II lakes) represented by the Qinghai Lake (QHH), and relatively clean water bodies (class III lakes) represented by the Longyangxia Reservoir (LYX). Compared with the in situ Rrs, it was found that for the DBX, the Sen2Cor processor performed best. The POLYMER processor exhibited a good performance in the QHH. The C2XC processor performed well with the LYX. Using the Sen2Cor, POLYMER, and C2XC processors for classes I, II, and III, respectively, compared with the Sentinel-3 OLCI Level-2 Water Full Resolution (L2-WFR) products, it was found that the estimated Rrs from the POLYMER had the highest consistency. Slight deviations were observed in the estimation results for both the Sen2Cor and C2XC. [ABSTRACT FROM AUTHOR]

Band selection is an important preprocessing technique for hyperspectral imagery, through which a subset of critical and representative spectral bands can be selected from a raw image cube for compact yet effect representation. Among the valid selection strategies, performing band selection in an unsupervised manner is usually considered more general due to its application-independent characteristic. This letter proposed a novel unsupervised hyperspectral band selector that can capture the interband redundancy nature of hyperspectral images through low-rank modeling. Experiments on three real-world hyperspectral data sets demonstrated that the proposed band selector can generate band subsets suitable in the context of hyperspectral pixel classification. [ABSTRACT FROM PUBLISHER]

Information on vegetation water content (VWC) is important in retrieving soil moisture using microwave remote sensing. It can be also used for other applications, including drought detection, bushfire prediction, and agricultural productivity assessment. Through the Soil Moisture Active Passive (SMAP) mission of the National Aeronautics and Space Administration, radar data may potentially provide the VWC information needed for soil moisture retrieval from the radiometer data acquired by the same satellite. In this paper, VWC estimation is tested using radar vegetation index (RVI) data from the third SMAP airborne Experiment. Comparing with coincident ground measurements, prediction equations for wheat and pasture were developed. While a good relationship was found for wheat, with r=0.49, 0.62,\ \mboxand\ 0.65 and root-mean-square error (\mboxRMSE)=0.42, 0.37,\ \mboxand\ 0.36 kg/m2, the relationship for pasture was poor, with r=-0.06,-0.14,\ \mboxand\ -0.002 and \mboxRMSE=0.15,0.15,\ \mboxand\ 0.15, kg/m2, for 10-, 30-, and 90-m resolutions, respectively. These results suggested that RVI is better correlated with VWC for vegetation types having a greater dynamic range. However, the results were not as good as those from a previous tower-based study ( $r=0.98$ and \mbox{RMSE}=0.12\ \mbox{kg/m}^2) over wheat. This is possibly due to spatial variation in vegetation structure and surface roughness not present in tower studies. Consequently, results from this study are expected to more closely represent those from satellite observations such as SMAP, where large variation in vegetation and environment conditions will be experienced. [ABSTRACT FROM PUBLISHER]