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With the launch of Landsat 9 in September 2021, an optimal opportunity for in-flight cross-calibration occurred when Landsat 9 flew underneath Landsat 8 while being moved into its final orbit. Since the two instruments host nearly identical imaging systems, the underfly event offered ideal cross-calibration conditions. The purpose of this work was to use the underfly imagery collected by the instruments to estimate cross-calibration parameters for Landsat 9 for a calibration update scheduled at the end of the on-orbit initial verification (OIV) period. Three types of uncertainty were considered: geometric, spectral, and angular (bidirectional reflectance distribution function—BRDF). Differences caused by geometric uncertainty were found to be negligible for this application. Spectral uncertainty was found to be minimal except for the green band when viewing vegetative targets. BRDF models derived from the MODIS BRDF product indicated substantial error could occur and required development of a mitigating methodology. With these three contributions of uncertainty properly addressed, it was estimated that the total cross-calibration uncertainty for underfly data could be kept under 1%. The data collected during the underfly were filtered to remove outliers based on uncertainty analysis. These data were used to calculate the TOA reflectance and radiance cross-calibration values for each spectral band by taking the ratio of Landsat 8 average pixel values to Landsat 9. Initial results of this approach indicated the cross-calibration may be as accurate as 0.5% in reflectance space and 1.0% in radiance space. The initial results developed in this study were used to refine the cross-calibration of Landsat 9 to Landsat 8 at the end of the OIV period.

Multi-spectral imagery captured from unmanned aerial systems (UAS) is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS-based data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate bidirectional reflectance distribution function (BRDF) correction. Future UAS-based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.

Context In arid and semi-arid regions, the expansion of the shrub-encroached grassland (SEG) has a profound impact on ecosystem dynamics and desertification processes. However, there is a serious lack of research on the interaction between plant community structure and soil using model systems at the scene scale. Objectives A repeatable and scalable soil–canopy 3D model framework for arid areas was established, and provided theoretical support and methodological reference for ecological monitoring and desertification control. Methods This study integrated the three-dimensional radiation transfer model (LESS) with the hyperspectral soil reflectance model (GSV) to build a fully parameterized 3D plant–soil fusion simulation framework. Using this framework, we systematically quantified how community structural characteristics and soil background synergistically regulate vegetation community bidirectional reflectance factor (BRF) and normalized difference vegetation index (NDVI). Finally, we validated the simulation results of the fusion model using Sentinel-2 data to evaluate its applicability and accuracy. Results The simulation results show that the fusion model can accurately reproduce the joint effect of soil reflectance and community structure characteristics on spectral response, especially in the near-infrared (NIR) band with significant differences. The study of community distribution patterns found that the Gap model has the strongest stability and environmental adaptability, while the Spot model can effectively buffer the interference of adverse soil conditions. In addition, the ecological explanatory power and application feasibility of the fusion model were further verified by combining Sentinel-2 NDVI time series data. Conclusions This study expanded the application boundaries of LESS and GSV models at the community scale. This comprehensive simulation method provides an operational solution for the construction of plant community scenarios in arid areas and related research.

GeoNEX is a collaborative project led by scientists from NASA, NOAA, and many other institutes around the world to generate Earth monitoring products using data streams from the latest Geostationary (GEO) sensors including the GOES-16/17 Advanced Baseline Imager (ABI), the Himawari-8/9 Advanced Himawari Imager (AHI), and more. An accurate and consistent product of the Top-Of-Atmosphere (TOA) reflectance and brightness temperature is the starting point in the scientific processing pipeline and has significant influences on the downstream products. This paper describes the main steps and the algorithms in generating the GeoNEX TOA products, starting from the conversion of digital numbers to physical quantities with the latest radiometric calibration information. We implement algorithms to detect and remove residual georegistration uncertainties automatically in both GOES and Himawari L1bdata, adjust the data for topographic relief, estimate the pixelwise data-acquisition time, and accurately calculate the solar illumination angles for each pixel in the domain at every time step. Finally, we reproject the TOA products to a globally tiled common grid in geographic coordinates in order to facilitate intercomparisons and/or synergies between the GeoNEX products and existing Earth observation datasets from polar-orbiting satellites.

Light reflected from satellite surfaces is polarized light, which plays a crucial role in space target identification and remote sensing. To deepen our understanding of the polarized reflectance property for satellite material surface, we present the experiments of polarimetric laboratory measurements from two typical satellite materials in the wavelength range of 400–1000 nm by using a goniometer instrument. The bidirectional polarized reflectance factor (BPRF) is used to describe the polarization characteristics of our samples. The polarized spectral reflectance and distribution of BPRF for our datasets are analyzed. Furthermore, five semi-empirical polarized bidirectional reflectance distribution functions (pBRDFs) models for polarized reflectance of typical satellite material surfaces (Preist–Germer model, Maxwell–Beard model, three-component model, Cook–Torrance model, and Kubelka–Munk model) are quantitatively intercompared using the measured BPRFs. The results suggest that the measured BPRFs of our samples are spectrally irrelevant, and the hemispherical distribution of BPRFs is obviously anisotropic. Except for the Preist–Germer model, the other semi-empirical models are in good agreement with the measured BPRF at the selected wavelengths, indicating that we can accurately simulate the polarized reflectance property of the satellite surface by using the existing polarimetric models. The Kubelka–Munk pBRDF model best fits the silver polyimide film and white coating surfaces with RMSE equal to 3.25% and 2.03%, and the correlation coefficient is 0.994 and 0.984, respectively. This study can be applied to provide an accurate pBRDF model for space object scene simulation and has great potential for polarization remote sensing.

Optical wavelength satellite data have directional reflectance effects over non-Lambertian surfaces, described by the bidirectional reflectance distribution function (BRDF). The Sentinel-2 multi-spectral instrument (MSI) acquires data over a 20.6° field of view that have been shown to have non-negligible BRDF effects in the visible, near-infrared, and short wave infrared bands. MSI red-edge BRDF effects have not been investigated. In this study, they are quantified by an examination of 6.6 million (January 2016) and 10.7 million (April 2016) pairs of forward and back scatter reflectance observations extracted over approximately 20° × 10° of southern Africa. Non-negligible MSI red-edge BRDF effects up to 0.08 (reflectance units) across the 290 km wide MSI swath are documented. A recently published MODIS BRDF parameter c-factor approach to adjust MSI visible, near-infrared, and short wave infrared reflectance to nadir BRDF-adjusted reflectance (NBAR) is adapted for application to the MSI red-edge bands. The red-edge band BRDF parameters needed to implement the algorithm are provided. The parameters are derived by a linear wavelength interpolation of fixed global MODIS red and NIR BRDF model parameters. The efficacy of the interpolation is investigated using POLDER red, red-edge, and NIR BRDF model parameters, and is shown to be appropriate for the c-factor NBAR generation approach. After adjustment to NBAR, red-edge MSI BRDF effects were reduced for the January data (acquired close to the solar principal where BRDF effects are maximal) and the April data (acquired close to the orthogonal plane) for all the MSI red-edge bands.

Recent studies have demonstrated the potential of using bidirectional reflectance distribution function (BRDF) signatures captured by multi-angle observation data to enhance land cover classification and retrieve vegetation architectures. Considering the diversity of crop architectures, we proposed that crop mapping precision may be enhanced by using BRDF signatures. We compared the accuracy of four supervised machine learning classifiers provided by the Google Earth Engine (GEE), namely random forest (RF), classification and regression trees (CART), support vector machine (SVM), and Naïve Bayes (NB), using the moderate resolution imaging spectroradiometer (MODIS) nadir BRDF-adjusted reflectance data (MCD43A4 V6) and BRDF and albedo model parameter data (MCD43A1 V6) as input. Our results indicated that using BRDF signatures leads to a moderate improvement in classification results in most cases, compared to using reflectance data from a single nadir observation direction. Specifically, the overall validation accuracy increased by up to 4.9%, and the validation kappa coefficients increased by up to 0.092. Furthermore, the classifiers were ranked in order of accuracy, from highest to lowest: RF, CART, SVM, and NB. Our study contributes to the development of crop mapping and the application of multi-angle observation satellites.

In the process of radiometric calibration, the corrections for bidirectional reflectance distribution functions (BRDFs) and spectral band adjustment factors (SBAFs) are crucial. Time-series MODIS images are commonly used to construct BRDFs by using the Ross–Li model in current research. However, the Ross–Li BRDF model is based on the linear relationship between the kernel models and is unable to take into account the nonlinear relationship between them. Furthermore, when using SBAF to account for spectral difference, a radiative transfer model is often used, but it requires many parameters to be set, which may introduce more errors and reduce the calibration accuracy. To address these issues, the random forest algorithm and a spectral interpolation convolution method using the Sentinel-2/multispectral instrument (MSI) are proposed in this study, in which the HuanJing-2A (HJ-2A)/charge-coupled device (CCD3) sensor is taken as an example, and the Dunhuang radiometric calibration site (DRCS) is used as a radiometric delivery platform. Firstly, a BRDF model by using the random forest algorithm of the DRCS is constructed using time-series MODIS images, which corrects the viewing geometry difference. Secondly, the BRDF correction coefficients, MSI reflectance, and relative spectral responses (RSRs) of CCD3 are used to correct the spectral differences. Finally, with the validation results, the maximum relative error between the calibration results of the proposed method and the official calibration coefficients (OCCs) published by the China Centre for Resources Satellite Data and Application (CRESDA) is 3.38%. When tested using the Baotou sandy site, the proposed method is better than the OCCs of the average relative errors calculated for all the bands except for the near-infrared (NIR) band, which has a larger error. Additionally, the effects of the light-matching method and the radiative transfer method, different approaches to constructing the BRDF model, using SBAF to account for spectral differences, different BRDF sources, as well as the imprecise viewing geometrical parameters, spectral interpolation method, and geometric positioning error, on the calibration results are analyzed. Results indicate that the cross-calibration coefficients obtained using the random forest algorithm and the proposed spectral interpolation method are more applicable to the CCD3; thus, they also account for the nonlinear relationships between the kernel models and reduce the error due to the radiative transfer model. The total uncertainty of the proposed method in all bands is less than 5.16%.

Unmanned aerial vehicle (UAV) imaging provides the ability to obtain high-resolution images at a lower cost than satellite imagery and aerial photography. However, multiple UAV images need to be mosaicked to obtain images of large areas, and the resulting UAV multispectral image mosaics typically contain seam lines. To address this problem, we applied irradiance, vignette, and bidirectional reflectance distribution function (BRDF) filters and performed field work using a DJI Mavic 3 Multispectral (M3M) camera to collect data. We installed a calibrated reference tarp (CRT) in the center of the collection area and conducted three types of flights (BRDF, vignette, and validation) to measure the irradiance, radiance, and reflectance—which are essential for irradiance correction—using a custom reflectance box (ROX). A vignette filter was generated from the vignette parameter, and the anisotropy factor (ANIF) was calculated by measuring the radiance at the nadir, following which the BRDF model parameters were calculated. The calibration approaches were divided into the following categories: a vignette-only process, which solely applied vignette and irradiance corrections, and the full process, which included irradiance, vignette, and BRDF. The accuracy was verified through a validation flight. The radiance uncertainty at the seam line ranged from 3.00 to 5.26% in the 80% lap mode when using nine images around the CRT, and from 4.06 to 6.93% in the 50% lap mode when using all images with the CRT. The term ‘lap’ in ‘lap mode’ refers to both overlap and sidelap. The images that were subjected to the vignette-only process had a radiance difference of 4.48–6.98%, while that of the full process images was 1.44–2.40%, indicating that the seam lines were difficult to find with the naked eye and that the process was successful.

Accurate solar background noise modeling in island-reef LiDAR surveys is hindered by anisotropic coastal reflectivity and dynamic light paths, which isotropic models fail to address. We propose BNR-B, a bidirectional reflectance distribution function (BRDF)-based noise model that integrates solar-receiver geometry with micro-facet scattering dynamics. Validated via single-photon LiDAR field tests on diverse coastal terrains at Jiajing Island, China, BNR-B reveals the following: (1) Solar zenith/azimuth angles non-uniformly modulate noise fields—higher solar zenith angles reduce noise intensity and homogenize spatial distribution; (2) surface reflectivity linearly correlates with noise rate (R2 > 0.99), while roughness governs scattering directionality through micro-facet redistribution. BNR-B achieves 28.6% higher noise calculation accuracy than Lambertian models, with a relative phase error < 2% against empirical data. As the first BRDF-derived solar noise correction framework for coastal LiDAR, it addresses critical limitations of isotropic assumptions by resolving directional noise modulation. The model’s adaptability to marine–terrestrial interfaces enhances precision in coastal monitoring and submarine mapping, offering transformative potential for geospatial applications requiring photon-counting LiDAR in complex environments. Key innovations include dynamic coupling of geometric optics and surface scattering physics, enabling robust spatiotemporal noise quantification, critical for high-resolution terrain reconstruction.