Hyperspectral LiDAR (HSL) technology can obtain spectral and ranging information from targets by processing the recorded waveforms and measuring the time of flight (ToF). With the development of the supercontinuum laser (SCL), it is technically easier to develop an active hyperspectral LiDAR system that can simultaneously collect both spatial information and extensive spectral information from targets. Compared with traditional LiDAR technology, which can only obtain range and intensity information at the selected spectral wavelengths, HSL detection technology has demonstrated its potential and adaptability for various quantitative applications from its spectrally resolved waveforms. However, with most previous HSLs, the collected spectral information is discrete, and such information might be insufficient and restrict the further applicability of the HSLs. In this paper, a tunable HSL technology using an acousto-optic tunable filter (AOTF) as a spectroscopic device was proposed, designed, and tested to address this issue. Both the general range precision and the accuracy of the spectral measurement were evaluated. By tuning the spectroscopic device in the time dimension, the proposed AOTF-HSL could achieve backscattered echo with continuous coverage of the full spectrum of 500–1000 nm, which had the unique characteristics of a continuous spectrum in the visible and near infrared (VNIR) regions with 10 nm spectral resolution. Yellow and green leaves from four plants (aloe, dracaena, balata, and radermachera) were measured using the AOTF-HSL to assess its feasibility in agriculture application. The spectral profiles measured by a standard spectrometer (SVC^© HR-1024) were used as a reference for evaluating the measurements of the AOTF-HSL. The difference between the spectral measurements collected from active and passive instruments was minor. The comparison results show that the AOTF-based consecutive and high spectral resolution HSL was effective for this application.

Radiometric cross-calibration of Earth observation sensors is an effective approach to evaluate instrument calibration performance, identify and diagnose calibration anomalies, and quantify the consistency of measurements from different sensors. In this study a novel cross-calibration method is proposed, taking into account the spectral and viewing angle differences adequately; the method is applied to the FY-3C/Visible Infrared Radiometer (VIRR), taking the Suomi National Polar-Orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) as a reference. The results show that the relative difference between the two sets increases from January to May 2014, and becomes lower for the data on 24 July, 11 September, and 16 September, within approximately 10%. This phenomenon is caused by the updating of the calibration coefficients in the VIRR datasets with results from a vicarious method on June 2014. After performing an approximate estimation of the uncertainty, it is demonstrated that this calibration has a total uncertainty of 5.5%–6.0%, which is mainly from the uncertainty of the Bidirectional Reflectance Distribution Function model.

Accurate inversion of land surface temperature (LST) from remote sensing data is an essential and challenging topic for earth observation applications. This paper successfully retrieves the LST from FY-3C/VIRR data with split-window method. With the simulated data, the algorithm coefficients are acquired with root mean square errors lower than 1.0 K for all subranges when view zenith angle (VZA) <; 30° and the water vapor content (WVC) <; 4.25 g/cm ² , as well as those in which the VZA <; 30° and the LST <; 307.5 K. In addition, a detailed sensitivity analysis is carried out. The analysis result indicates that the total LST uncertainty caused by the standard error of the algorithm, the uncertainties of land surface emissivity and WVC, and the instrument noise would be 1.22 K and 0.94 K for dry and wet atmosphere, respectively. Furthermore, LST retrieval method is applied to the visible and infrared radiometer measurements over the study area covering the geographical latitude of 31.671°N to 44.211°N and longitude of 10.739°W to 1.898°E, and the derived LST is cross-validated with Terra/MODIS LST product. The preliminary validation result shows that the split-window method determines the LST within 2.0 K for vegetation and soil areas.

Optical satellite sensor generally suffers from drifts and biases relative to their pre-launch calibration, caused by launch and the space environment. Radiometric cross-calibration is valuable for calibrating sensors without coincident surface measurements through transfer radiometric values between satellite sensor via stable target. Since the data quality of GF-4/VNIR data seriously depends on vicarious calibration, which is performed once every year, cross-calibration is proposed for monitoring its radiometric performance much more frequently. In this study, the GF-4/VNIR is cross-calibrated by Landsat8/OLI, Sentinel-2/MSI, and Terra/MODIS with the aid of the tandem scenes during 2017 and 2018 after correcting the discrepancy on viewing geometries and spectrum. The results show that there is test site dependency and sensor dependency, and cross-calibration coefficients in the four bands roughly has similar degradation trend as a cosine function during 2017 and 2018, varying from 0.0002 to 0.00065. Furthermore, uncertainty analysis of the cross-calibration is carried out to analyze the uncertainty contributions affecting the cross-calibration accuracy. Note that the calibration accuracy of reference sensors, the uncertainty of MODTRAN model, BRDF uncertainty are the three main factors affecting the cross-calibration accuracy, the total uncertainty is approximately 3.9%-6.64%.

This article addressed the simultaneous retrieval of land surface temperature (LST) and emissivity (LST&E) from the time-series thermal infrared data. On the basis of the assumption that the time-series LSTs can be described by a piecewise linear function, a new method has been proposed to simultaneously retrieve LST&E from atmospherically corrected time-series thermal infrared data using LST linear constraint. A detailed analysis has been performed against various errors, including error introduced by the method assumption, instrument noise, initial emissivity, atmospheric downwelling radiance error, etc. The proposed method from the simulated data is more immune to noise than the existing methods. Even with a noise equivalent delta temperature of 0.5 K, the root-mean-square error of LST is observed to be only 0.13 K, and that of the land surface emissivity (LSE) is 1.8E-3. In addition, our proposed method is simple and efficient and does not encounter the problem of singular values unlike the existing methods. To validate the proposed method, a field experiment from June to September 2017 was conducted for sand target in Baotou site, China. The results show that the samples have an accuracy of LST within 0.87 K and that the mean values of LSE are accurate to 0.01.

Field calibration is a feasible way to evaluate space-borne optical sensor observations via natural or artificial sites on Earth’s surface with the aid of synchronous surface and atmospheric characteristic data. Since field calibration is affected by the coupled effects of surface and atmospheric characteristics, the single calibration results acquired under different surface and atmospheric conditions have different biases and different uncertainties, making it difficult to determine the consistency of these multiple calibration results. In view of this, by assuming that the radiometric performance is invariant during field calibration and the calibration samples are independent of each other, the surface–atmosphere invariant Key Comparison Reference Value (KCRV) is essentially derived from various calibration results. As the number of calibration samples increases, the uncertainty in the KCRV should decrease, and the KCRV should approach the “true” value. This paper addresses a novel method for estimating a weighted average value from multiple calibration results that can be used to compare each calibration result, and this value is accepted as the KCRV. Furthermore, this method is preliminarily applied to the field calibration of the Multispectral Instrument (MSI) onboard the Sentinel-2B satellite via the desert target at the Baotou site, China. After employing a chi-squared test to verify that 12 calibration samples are independent from each other, the KCRV of the 12 calibration samples at the Baotou site is derived, which exhibits much lower uncertainty than a single sample. The results show that the KCRVs of the relative differences between the simulated and observed at-sensor reflectance are 3.75%, 5.11%, 6.09%, and 5.03% for the four bands of Sentinel-2B/MSI, respectively, and the corresponding uncertainties are 1.84%, 1.87%, 1.90%, and 1.93%. It is noted that the KCRV uncertainty obtained with only 12 calibration samples is reduced significantly, and in the future, more samples in other instrumented sites will be used to validate this method thoroughly.