Yu-Ze Zhang

Absorption and scattering properties of water body in Taihu Lake, China : absorption

In order to acquire inherent optical properties to serve the lake water colour/quality remote sensing in Taihu Lake 67 samples were distributed almost all over the lake. Surface water samples were collected and returned to the laboratory for the subsequent processing and analysis. In the laboratory, the absorptions due to the total particulate matter, non‐algal particulate matter, phytoplankton pigment, and CDOM, together with their concentrations were measured and/or calculated, respectively. Then their absorption properties were analysed and compared with those of other lake waters and/or coastal/open waters. Some different and similar characteristics were uncovered. On the one hand, it provides not only a solid basement for the Taihu Lake water colour/quality remote sensing with semi‐analytical/analytical approach but also a typical case for inherent optical properties of case two water especially for inland freshwater lakes. On the other, it is very helpful to improve the practical and intensive application and development of remote sensing in monitoring lake water quality.

Relating photosynthesis of biological soil crusts with reflectance : preliminary assessment based on a hydration experiment

This paper examines the relationship between spectral indices (the normalized difference vegetation index [NDVI] and photochemical reflectance index [PRI]) and the photosynthetic capacities based on chlorophyll fluorescence (Fv/Fm ) of moss‐, lichen‐ and cyanobacteria‐dominated biological soil crusts collected from the Gurbantonggut Desert, Xinjiang, China, based on a liquid–water hydration experiment. While no photosynthetic activity was detected for dry crusts, hydrated crusts showed significantly higher Fv/Fm values than dry crusts. A significant correlation between the PRI and Fv/Fm was found in moss‐ and lichen‐dominated crusts, and the determination coefficients were higher than those between the NDVI and Fv/Fm .

Land Surface Temperature and Emissivity Retrieval from Field-Measured Hyperspectral Thermal Infrared Data Using Wavelet Transform

Currently, the main difficulty in separating the land surface temperature (LST) and land surface emissivity (LSE) from field-measured hyperspectral Thermal Infrared (TIR) data lies in solving the radiative transfer equation (RTE). Based on the theory of wavelet transform (WT), this paper proposes a method for accurately and effectively separating LSTs and LSEs from field-measured hyperspectral TIR data. We show that the number of unknowns in the RTE can be reduced by decomposing and reconstructing the LSE spectrum, thus making the RTE solvable. The final results show that the errors introduced by WT are negligible. In addition, the proposed method usually achieves a greater accuracy in a wet-warm atmosphere than that in a dry-cold atmosphere. For the results under instrument noise conditions (NE∆T = 0.2 K), the overall accuracy of the LST is approximately 0.1–0.3 K, while the Root Mean Square Error (RMSE) of the LSEs is less than 0.01. In contrast to the effects of instrument noise, our method is quite insensitive to noises from atmospheric downwelling radiance, and all the RMSEs of our method are approximately zero for both the LSTs and the LSEs. When we used field-measured data to better evaluate our method’s performance, the results showed that the RMSEs of the LSTs and LSEs were approximately 1.1 K and 0.01, respectively. The results from both simulated data and field-measured data demonstrate that our method is promising for decreasing the number of unknowns in the RTE. Furthermore, the proposed method overcomes some known limitations of current algorithms, such as singular values and the loss of continuity in the spectrum of the retrieved LSEs.

Fast and accurate measurement of spectral emissivity with a portable field infrared spectrometer: ancillary equipment and methods

ABSTRACT Validation of land surface emissivity (LSE) is very important for assessing the accuracy of remote sensing products and understanding the potential and limitations of retrieval methods. Currently, a commonly used method for obtaining in situ spectral emissivity at ground level is to use a portable Fourier Transform Infrared (FTIR) spectrometer, such as the Model 102F Portable Field Spectrometer. However, since complicated procedures are necessary to ensure the accuracy of measured spectral emissivity, the traditional measuring methods for the 102F FTIR, are usually time-consuming. Additionally, a time-consuming process also means the thermal environment can change, which can further decrease the accuracy of the measurement. A prior knowledge of the sample emissivity in certain wavelength intervals is also necessary for the calculations in the current software package for the 102F FTIR, which makes the performance of the estimated LSEs highly dependent on the accuracy of this prior knowledge. To overcome the limitations in measuring LSE with the 102F FTIR, this paper presents a new solution using well-designed ancillary equipment and an optimized temperature and emissivity separation algorithm. The ancillary equipment consists of 4 major parts and is mainly designed for rapidly switching the sample and the diffuse gold-plate using a remote control. As no extra manual adjustments are necessary in our method, it will obviously decrease the risk of thermal infrared environmental change during measurements. In addition, by using our ancillary equipment, the reflected environmental radiance over the height surface is now measurable instead of using the approximations at the ground. By using a Wavelet-Transformed Temperature and Emissivity Separation (WTTES) algorithm, the surface temperature and the spectral emissivity are now simultaneously obtained without any prior knowledge, thus making the retrievals more reasonable. According to the experiments, our method is more efficient and accurate for measuring the spectral emissivity through the 102F FTIR spectrometer.

Complement analysis for the wavelet transform method for separating temperature and emissivity

This paper presents a complement analysis for the wavelet transform method for separating temperature and emissivity (WTTES) with different wavelets, wavelet levels and biased atmospheric downwelling radiance. According to the results, the WTTES algorithm is quite insensitive to the choice of the wavelet. By comparing the retrievals with different wavelet levels, a wavelet level of n=3 or n=4 is more recommended in most cases. In addition, compared with the white noise, the WTTES algorithm is more sensitive to the atmospheric downwelling radiance with bias errors. For the profile with a bias error of 10%, the RMSE of the emissivity retrievals can be increased approximately 0.17%-2.33%, which depends on the specified water vapor content of the profile. However, different from the obvious errors on emissivity, the overall accuracies of the temperature retrievals under different atmospheric profiles are all less than 0.7K, which means the WTTES algorithm is still feasible to retrieve the temperature under the condition of biased moisture profiles.

Land Surface Temperature and Emissivity Separation from Cross-Track Infrared Sounder Data with Atmospheric Reanalysis Data and ISSTES Algorithm

The Cross-track Infrared Sounder (CrIS) is one of the most advanced hyperspectral instruments and has been used for various atmospheric applications such as atmospheric retrievals and weather forecast modeling. However, because of the specific design purpose of CrIS, little attention has been paid to retrieving land surface parameters from CrIS data. To take full advantage of the rich spectral information in CrIS data to improve the land surface retrievals, particularly the acquisition of a continuous Land Surface Emissivity (LSE) spectrum, this paper attempts to simultaneously retrieve a continuous LSE spectrum and the Land Surface Temperature (LST) from CrIS data with the atmospheric reanalysis data and the Iterative Spectrally Smooth Temperature and Emissivity Separation (ISSTES) algorithm. The results show that the accuracy of the retrieved LSEs and LST is comparable with the current land products. The overall differences of the LST and LSE retrievals are approximately 1.3 K and 1.48%, respectively. However, the LSEs in our study can be provided as a continuum spectrum instead of the single-channel values in traditional products. The retrieved LST and LSEs now can be better used to further analyze the surface properties or improve the retrieval of atmospheric parameters.