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The newest soil moisture-dedicated satellite, the Soil Moisture Active Passive (SMAP) mission, provides global maps of soil moisture using concurrent L-band radar and radiometer acquisitions. To support the ongoing validation activities of SMAP soil moisture products, in this paper, we examined the retrieval accuracy of four SMAP soil moisture products by using well-calibrated and dense in situ measurements from the Little Washita Watershed network, one of the SMAP core validation sites with intensive ground sampling. The four SMAP products include the active (3 km), passive (36 km), active-passive (9 km), and the enhanced passive product which is a newly released soil moisture data set with a grid resolution of 9 km. Efforts on identifying the possible error sources of these products were also made for the purpose of improving the SMAP soil moisture algorithms. The results show that the passive and active-passive products can well capture the temporal dynamic of ground soil moisture with overall unbiased root-mean-square error (ubRMSE) values of 0.032 and $0.041~\text {m}^{3}\cdot ~\text {m}^{-3}$ , respectively, which generally meet their mission requirement of $0.04~\text {m}^{3}\cdot ~\text {m}^{-3}$ . In contrast, some irregular fluctuations exist in the active product, leading to an overall wet bias, which makes its accuracy a little poorer than its expected retrieval accuracy of $0.06~\text {m}^{3}\cdot ~\text {m}^{-3}$ . The new enhanced passive product shows the lowest ubRMSE value of $0.026 ~\text {m}^{3}\cdot ~\text {m}^{-3}$ though it underestimates in situ measurements with a bias of $0.059 ~\text {m}^{3}\cdot ~\text {m}^{-3}$ , revealing its great potential to substitute the active-passive product to provide global soil moisture measurements at a medium resolution of 9 km. The underestimation of SMAP surface temperature data may be one of the reasons that contribute to the dry bias of SMAP passive, active-passive, and enhanced passive products. The microwave polarization difference index and HV-polarized backscatter show good response to in situ soil moisture and may be considered in SMAP algorithms to further improve the accuracy of soil moisture retrievals. We expect that our findings can be fed back to improve the SMAP soil moisture algorithms and thus promote the application of SMAP soil moisture products in terrestrial water, energy, and carbon cycles.

Existing remote-sensing algorithms to estimate the phycocyanin (PC) concentration in turbid inland waters have high associated uncertainties, especially at low PC concentrations in diverse phytoplankton communities. This paper provides the theoretical framework for a four-band semi-analytical algorithm (FBA_PC) which isolates PC absorption from second-order variability caused by yellow matter and other phytoplankton pigment absorption. The algorithm suits the band configuration of both the Medium Resolution Imaging Spectrometer (MERIS) and Sentinel-3 Ocean and Land Color Instrument (OLCI). Calibration of the algorithm was based on absorption data from 12 inland water bodies in the USA, The Netherlands, and China, combined with measurements from laboratory-grown cultures, which demonstrated that the assumptions underlying FBA-PC are an improvement over existing three-band approaches. Validation of FBA_PC in seven inland water bodies in the USA, The Netherlands, and China showed good agreement of FBA_PC adjusted to the MERIS/OLCI band configuration with measured PC, with root-mean-square error =27.691 $\text {mg} \cdot \text {m}^{-3}$ , mean absolute percentage error = 172.863%, and coefficient of determination ( $R^{2}) = 0.730$ . FBA_PC outperformed previously proposed PC algorithms that can be applied to MERIS or OLCI data, and is expected to be more robust when applied to a wider range of water bodies.

In the above paper [1] , the name of the institution of the authors Dandan Zhi, Tanqi Yu, and Yan Pan were incorrect, they are with the Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Key Laboratory of Optical Calibration and Characterization, Hefei 230031, China, and also with the University of Science and Technology of China, Hefei 230026, China (e-mail: 1416652331@qq.com).

The MEdium Resolution Spectral Imager (MERSI) is the keystone instrument onboard FengYun-3 (FY-3). After FY-3A, FY-3B MERSI is the second launched in November 2010. Nineteen of the 20 MERSI spectral bands are the reflective solar bands, which cannot be absolutely calibrated onboard. The annual vicarious calibration (VC) based on synchronous in situ measurements at the Dunhuang site is the baseline calibration method for MERSI. To assure frequent and stable calibration updates, a multisite calibration tracking method is developed. This paper presents the FY-3B MERSI postlaunch daily calibration updating method based on multisite calibration tracking with the Dunhuang VC correction, the long-term sensor response on-orbit change, and the calibration performance evaluation. A reflectance-based method is used for the Dunhuang VC, and the reflectance calibration uncertainties are within 3% for most MERSI bands. The multisite calibration tracking method relies on simulated radiation over several stable sites without synchronous in situ measurements. A postlaunch daily calibration updating model is established using a linear function of days since launch to describe the long-term trend. The calibration updating model is validated by the Dunhuang VC, showing the relative bias within 3.5% for most bands. It is found that the shortwave channels of MERSI experience large degradation, particularly the 412-nm band with an annual degradation rate of approximately 18%, whereas most red and near-infrared bands are relatively stable. Using the calibration updating model with the Dunhuang VC correction, the recalibrated MERSI data are validated against Moderate Resolution Imaging Spectroradiometer by near synchronous-nadir-observation analysis, and good agreement is achieved.

The processing steps and error corrections needed to retrieve estimates of sea surface range Doppler velocities from Envisat Advanced Synthetic Aperture Radar Wide Swath Medium resolution image products are presented. Retrieval accuracies based on examination of the corrected Doppler shift measurements are assessed. The root-mean-square errors of the Doppler shift after bias corrections are found to be 4.7 and 3.9 Hz in VV and HH polarizations, respectively. At 35° incidence angle, this corresponds to horizontal Doppler velocities of 23 and 19 cm/s.

The matrix inversion method (MIM) is an effective algorithm for estimating water constituent concentrations in case II waters. To apply this method, appropriate and accurate specific inherent optical properties (SIOPs) for each constituent in water are essential. However, many routine observations of lake water quality do not in fact provide SIOPs, thus limiting the application of the MIM. In this paper, an alternative MIM method based on linear matrix inversion theory was proposed to relax the requirement of SIOPs measurement. For this, so-called ESIOPs (Estimated SIOPs) were first derived by an unusual application of MIM based on adequate calibration samples; then the water constituent concentrations for the whole study area were retrieved by the standard application of MIM based on the derived ESIOPs. For each calibration sample, measurement of the reflectance spectrum and corresponding water constituent concentrations, which can be obtained from periodical satellite data and routine field surveys, is required. The performance of the proposed method was evaluated using the simulation data from Hydrolight and three MEdium Resolution Imaging Spectrometer Instrument (MERIS) images. The results showed that this method yielded satisfactory estimations of the water constituent concentrations for the noise-contaminated simulation data sets. For MERIS data in our study area (Lake Kasumigaura, Japan), the average bias (mean normalized bias or MNB) and relative random uncertainty (normalized root mean square error, or NRMS) were in the range of -11.2% to 3.4% and 4.8% to 29.7% for each water constituent concentration. These findings imply that the algorithm proposed in this study is theoretically reasonable and practically applicable.