Changes In Surface Reflectance Research Articles

Assessment of the spectral misalignment effect (SMILE) on EarthCARE's Multi-Spectral Imager aerosol and cloud property retrievals

Abstract. The Multi-Spectral Imager (MSI) on board the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) will provide horizontal information about aerosols and clouds. These measurements are needed to extend vertical cloud and aerosol property information, which is obtained from EarthCARE's active sensors, in order to obtain a full three-dimensional view of cloud and aerosol conditions. Mesoscale weather systems, in particular, will be characterized. The discovery of a non-compliance of the MSI visible–near-infrared–shortwave infrared (VNS) camera’s visible (VIS) and shortwave infrared (SWIR1) channels regarding a spectral central wavelength (CWVL) shift across-track of up to 14 nm (VIS) and 20 nm (SWIR1) led to the need for an analysis regarding its impact on MSI Level-2A aerosol and cloud products. A significant influence of the spectral misalignment effect (SMILE) on MSI retrievals is identified due to the spectral variation in gas absorption, surface reflectance, and aerosol and cloud properties within the spectral ranges of these MSI bands. For example, the VIS channel is positioned in close proximity to the red edge of green vegetation and is impacted by residual absorption of water vapor and ozone. Small central wavelength variations introduce uncertainties due to the rapid change in surface reflectance for conditions with low optical thickness. The present central wavelength shift in the VIS towards shorter wavelengths than at nadir introduces a relative error in transmission of up to 3.3 % due to the increasing influence of water vapor and ozone absorption. We found relative errors in the top-of-atmosphere (TOA) signal due to the SMILE of up to 30 % for low optical thickness over a land surface in that band. Since the magnitude of the impact strongly depends on the underlying surface and atmospheric conditions, we conclude that accounting for the SMILE in Level-2 retrievals or correcting the Level-1 signal will improve MSI aerosol and cloud product quality.

Comparison of surface characteristics of denture base resin materials with two surface treatment protocols and simulated brushing.

To investigate the effects of 4 denture base materials, 2 surface treatment protocols, and simulated brushing (SB) on the surface hardness, surface roughness, surface gloss, and the surface loss of denture base materials. Four denture base resin material groups (compression-molded, injection-molded, 3D-printed, and milled) with two different surface treatment protocols (polished and glazed) were utilized in this study. A total of 80 samples (n = 10) were evaluated for surface hardness (Vickers) before SB. SB was performed for each sample (custom-built V8 cross brushing machine, 50,000 reciprocal strokes). Surface roughness (Ra) was measured before and after SB with a non-contact optical profilometer. Surface gloss was performed using a glossmeter to determine changes in surface reflectivity of the specimens before and after SB. Surface loss (wear resistance) was measured after SB using optical profilometry. The effects of material, surface treatment, and SB on all surface characteristics were examined with two-way and three-way analysis of variance models (ANOVA) (α = 0.05). The polished compression-molded group had significantly higher surface hardness than all other groups. The protective glaze coating significantly increased the surface hardness for all groups (P < 0.001). SB increased the surface roughness of all groups regardless of surface treatments (P < 0.001). The increase in surface roughness after SB was significantly higher with polished surface treatment than with a glazed surface treatment in all groups (P < 0.001). Surface gloss was significantly higher with the glazed surface treatment than with the polished surface treatment for all denture base materials (P < 0.001). After SB, milled denture base material showed the highest, and 3D-printed material showed the second highest surface gloss compared to the other groups (P < 0.001), regardless of surface treatment. In all materials tested, surface glaze significantly decreased surface loss (P < 0.001). With the glaze surface treatment, compression-molded denture base material had significantly less surface loss (more surface gain) than other materials, while with the polished surface treatment, 3D-printed denture base material had the least surface loss when compared with other groups. A single layer of nano-filled, light-polymerizing protective glaze coating has displayed potential for enhancing the longevity of denture base materials, as evidenced by increased hardness and wear resistance. Following simulated brushing, the milled denture material exhibited the highest surface gloss and lowest surface roughness among all groups, regardless of the surface treatment protocol. This indicates that milled denture base material possesses favorable surface properties and may serve as a viable alternative to traditional denture base materials.

Efficiency enhancement of solar cell by using methylammonium lead tribromide/polymethyl methacrylate hybrid film as luminescent down shifting layer

Luminescent down-shifting (LDS) layer was introduced to tackle the issue of poor short-wavelength response of solar cell. Besides LDS effect, coating a LDS layer on the surface of a solar cell leads to a change in surface reflectance. LDS and antireflection (AR) effect are coupled and usually reported as a single effect. For those solar cell with optimized AR, it is difficult to enhance power conversion efficiency (PCE) with the LDS layer due to the variation of reflectance. In this work, we report an obvious PCE enhancement of two mainstream commercial silicon (Si) solar cells with the optimized AR by using methylammonium lead tribromide (MAPbBr3)/polymethyl methacrylate (PMMA) hybrid film as a LDS layer. The used MAPbBr3/PMMA nanoplatelets solutions for LDS layer have an absolute photoluminescent quantum yield of ∼85 % (excited by 350 nm) in air atmosphere for more than 30 days, laying a foundation for the realization of effective LDS effect. Theoretical analysis has separated the AR and LDS effect. It has demonstrated that AR effect has a positive influence on LDS effect, and PMMA plays the key role in the improvement of AR. Experimental studies further elucidated the LDS and AR effect provided by MAPbBr3/PMMA hybrid film. Thanks to this combined effect, the application of MAPbBr3/PMMA hybrid film as a LDS layer on Si heterojunction solar cell yields a PCE gain of 0.3 % absolute and that on passivated emitter and rear cell produces an absolute PCE enhancement of about 0.7 %.

Evaluation of the Relationship Between Snow Cover Index and Land Surface Temperature for Sulaymaniyah City, Iraq

Changes in land surface reflectance measured by remote sensing data can be useful in climate change studies. This study attempts to Evaluation of the relationship between Snow cover index and Land surface temperature for by used Normalized snow index (S3), Land Surface Temperature (LST), in Sulaymaniyah city, northeastern Iraq. Data from Landsat5 TM and Landsat8 OLI images were used to detect the change in snow cover and land surface temperature during the study period years 1987, 1999, 2009, and 2022. ArcGIS 10.8 is used to determine the variations and calculate S3, NDVI, and LST index indices. All S3 maps have shown a decrease trend from 1987 to 2022 which depicted decreases of snow cover area. Also, LST has increased from 13°C in 1987 to 19 °C in 2022. also the results showed S3 and LST correlation has a strong reverse relationship existed in each year. The reverse relationship found between S3 and LST indicates that areas in which increase land surface temperature it leads to inducing much changes in snow cover.

Research on surface shape measurement of highly reflective surfaces based on stripe projection

Metal workpieces with highly reflective surfaces are particularly common in modern industrial manufacturing systems, and their three-dimensional dimensional measurement has great applications and needs in various fields. Among the various three-dimensional dimensional measurement technologies, projection three-dimensional measurement technology is adopted for its non-contact, low-cost, high-precision, and fast data processing characteristics. However, because of the high reflectance of the highly reflective surface, the structured light photos taken by the camera will be overexposed and underexposed, which will lead to the lack of three-dimensional information obtained. Due to the large range of changes in surface reflectance and the limited grayscale range of the camera, the traditional stripe projection method cannot correctly measure the three-dimensional morphology of highly reflective surfaces [1]. This paper introduces an adaptive sinusoidal stripe grating based on the reflectance of the surface of an object, which is used to compensate for the overexposure of the camera when shooting on a highly reflective surface. Finally, the generated adaptive stripes are projected onto the measured object, and the phase solution and three-dimensional surface shape reconstruction are carried out by the heterodyne multi-frequency phase shift method.

Virtual image-based cloud removal for Landsat images

ABSTRACT The inevitable thick cloud contamination in Landsat images has severely limited the usability and applications of these images. Developing cloud removal algorithms has been a hot research topic in recent years. Many previous algorithms used one or multiple cloud-free image(s) in the same area acquired on other date(s) as reference image(s) to reconstruct missing pixel values. However, it remains challenging to determine the optimal reference image(s). In addition, abrupt land cover change can substantially degrade the reconstruction accuracies. To address these issues, we present a new cloud removal algorithm called Virtual Image-based Cloud Removal (VICR). For each cloud region, VICR reconstructs the missing surface reflectance by three steps: virtual image within cloud region construction based on time-series reference images, similar pixel selection using the newly proposed temporally weighted spectral distance (TWSD), and residual image estimation. By establishing two buffer zones around the cloud region, VICR allows automatic selection of the optimal set of time-series reference images. The effectiveness of VICR was validated at four testing sites with different landscapes (i.e. urban, croplands, and wetlands) and land change patterns (i.e. phenological change, abrupt change caused by flooding and tidal inundation), and the performances were compared with mNSPI (modified neighborhood similar pixel interpolator), WLR (weighted linear regression) and ARRC (AutoRegression to Remove Clouds). Experimental results showed that VICR outperformed the other algorithms and achieved higher Correlation Coefficients and lower Root Mean Square Errors in surface reflectance estimation at the four sites. The improvement is particularly noticeable at the sites with abrupt land change. By considering the difference in the contributions from the reference images, TWSD can select more reliable similar pixels to improve the prediction of abrupt change in surface reflectance. Moreover, VICR is more robust to different cloud sizes and to changing reference images. VICR is also computationally much faster than ARRC. The framework for time-series image cloud removal by VICR has great potential to be applied for large datasets processing.

Prediction of Fine Particulate Matter Concentration near the Ground in North China from Multivariable Remote Sensing Data Based on MIV-BP Neural Network

Rapid urbanization and industrialization lead to severe air pollution in China, threatening public health. However, it is challenging to understand the pollutants’ spatial distributions by relying on a network of ground-based monitoring instruments, considering the incomplete dataset. To predict the spatial distribution of fine-mode particulate matter (PM2.5) pollution near the surface, we established models based on the back propagation (BP) neural network for PM2.5 mass concentration in North China using remote sensing products. According to our predictions, PM2.5 mass concentrations are affected by changes in surface reflectance and the dominant particle size for different seasons. The PM2.5 mass concentration predicted by the seasonal model shows a similar spatial pattern (high in the east but low in the west) influenced by the terrain, but shows high value in winter and low in summer. Compared to the ground-based data, our predictions agree with the spatial distribution of PM2.5 mass concentrations, with a mean bias of +17% in the North China Plain in 2017. Furthermore, the correlation coefficients (R) of the four seasons’ instantaneous measurements are always above 0.7, indicating that the seasonal models primarily improve the PM2.5 mass concentration prediction.

A New Spatial–Temporal Depthwise Separable Convolutional Fusion Network for Generating Landsat 8-Day Surface Reflectance Time Series over Forest Regions

Landsat has provided the longest fine resolution data archive of Earth’s environment since 1972; however, one of the challenges in using Landsat data for various applications is its frequent large data gaps and heavy cloud contaminations. One pressing research topic is to generate the regular time series by integrating coarse-resolution satellite data through data fusion techniques. This study presents a novel spatiotemporal fusion (STF) method based on a depthwise separable convolutional neural network (DSC), namely, STFDSC, to generate Landsat-surface reflectance time series at 8-day intervals by fusing Landsat 30 m with high-quality Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m surface reflectance data. The STFDSC method consists of three main stages: feature extraction, feature fusion and prediction. Features were first extracted from Landsat and MODIS surface reflectance changes, and the extracted multilevel features were then stacked and fused. Both low-level and middle-level features that were generally ignored in convolutional neural network (CNN)-based fusion models were included in STFDSC to avoid key information loss and thus ensure high prediction accuracy. The prediction stage generated a Landsat residual image and is combined with original Landsat data to obtain predictions of Landsat imagery at the target date. The performance of STFDSC was evaluated in the Greater Khingan Mountains (GKM) in Northeast China and the Ziwuling (ZWL) forest region in Northwest China. A comparison of STFDSC with four published fusion methods, including two classic fusion methods (FSDAF, ESTARFM) and two machine learning methods (EDCSTFN and STFNET), was also carried out. The results showed that STFDSC made stable and more accurate predictions of Landsat surface reflectance than other methods in both the GKM and ZWL regions. The root-mean-square-errors (RMSEs) of TM bands 2, 3, 4, and 7 were 0.0046, 0.0038, 0.0143, and 0.0055 in GKM, respectively, and 0.0246, 0.0176, 0.0280, and 0.0141 in ZWL, respectively; it can be potentially used for generating the global surface reflectance and other high-level land products.

Evaluation of Satellite-Derived Estimates of Lake Ice Cover Timing on Linnévatnet, Kapp Linné, Svalbard Using In-Situ Data

Arctic lakes are sensitive to climate change, and the timing and duration of ice presence and absence (i.e., ice phenology) on the lake surface can be used as a climate indicator. In this study of Linnévatnet, one of the largest lakes on Svalbard, we compare inferences of lake ice duration from satellite data with continuously monitored lake water temperature and photographs from automatic cameras. Visible surface reflectance data from the moderate-resolution imaging spectroradiometer (MODIS) were used to observe the change in the lake-wide mean surface reflectance of Linnévatnet from 2003–2019, and smoothing splines were applied to the to determine the date of summer ice-off (also called “break-up end”—BUE). Similarly, BUE and fall ice-on (or “freeze-up end”—FUE) were determined from lake-wide mean time series of Sentinel-1 microwave backscatter from 2014–2019. Overall, the ice timing dates identified from the satellite observations agree well with the in-situ observations (RMSE values of approximately 2–7 days for BUE and FUE, depending on the method and in-situ dataset), lending confidence to the accuracy of remote sensing of lake ice phenology in remote Arctic regions. Our observations of Linnévatnet indicate that BUE dates do not have a significant trend, while FUE dates have been occurring approximately 1.5 days later per year during the study period. These results support an overall decrease in annual duration of lake ice cover in this part of Svalbard.

Deep Learning-Based Spatiotemporal Data Fusion Using a Patch-to-Pixel Mapping Strategy and Model Comparisons

Tradeoffs among the spatial, spectral, and temporal resolutions of satellite sensors make it difficult to acquire remote sensing images at both high spatial and high temporal resolutions from an individual sensor. Studies have developed methods to fuse spatiotemporal data from different satellite sensors, and these methods often assume linear changes in surface reflectance across time and adopt empirical rules and handcrafted features. Here, we propose a dense spatiotemporal fusion (DenseSTF) network based on the convolutional neural network (CNN) to deal with these problems. DenseSTF uses a patch-to-pixel modeling strategy that can provide abundant texture details for each pixel in the target fine image to handle heterogeneous landscapes and models both forward and backward temporal dependencies to account for land cover changes. Moreover, DenseSTF adopts a mapping function with few assumptions and empirical rules, which allows for establishing reliable relationships between the coarse and fine images. We tested DenseSTF in three contrast scenes with different degrees of heterogeneity and temporal changes, and made comparisons with three rule-based fusion approaches and three CNNs. Experimental results indicate that DenseSTF can provide accurate fusion results and outperform the other tested methods, especially when the land cover changes abruptly. The structure of the deep learning networks largely impacts the success of data fusion. Our study developed a novel approach based on CNN using a patch-to-pixel mapping strategy and highlighted the effectiveness of the deep learning networks in the spatiotemporal fusion of the remote sensing data.

An Improved Spatiotemporal Fusion Algorithm for Monitoring Daily Snow Cover Changes With High Spatial Resolution

Considering the tradeoff between spatial resolution and temporal resolution, spatiotemporal fusion has become a promising technique to monitor snow cover dynamics with both high spatial and temporal resolutions. The representative spatiotemporal fusion methods, e.g. Spatial Temporal Data Fusion Approach (STDFA), usually exist obvious phenomenon of spectral distortion when the surface reflectance changes nonlinearly, which affects the quality of the spatiotemporal fusion image. To address this issue, an effective STDFA-Matching-Pix2pix-Generative Adversarial Network (SMPG) algorithm combining the unmixing-based method, deep learning method, pre-matching and post-matching module is proposed to reduce the spectral distortion of STDFA fusion image. The high-temporal-low-spatial (HTLS) resolution MOD09GA data and high-spatial-low-temporal resolution (HSLT) Landsat 8 data are selected in this study. SMPG algorithm is firstly employed to obtain daily high-spatial-high-temporal (HSHT) images, and then daily snow cover results with a spatial resolution of 30 m are obtained by calculating the normalized difference snow index (NDSI). SMPG algorithm is further compared with STDFA, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), Swin SpatioTemporal Fusion Model (SwinSTFM), and Generative Adversarial Network-based SpatioTemporal Fusion Model (GAN-STFM). The experimental results indicate that the proposed algorithm yields better overall performance in daily spatiotemporal fusion image and snow cover result with a spatial resolution of 30 m. The mean correlation coefficient (CC) of SMPG can achieve 0.962, which is 0.06-0.36 higher than that of other spatiotemporal fusion methods. The error between the percentage of snow cover area obtained through SMPG and validation data is within 0.84%.

Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China

Flood is a kind of natural disaster that is extremely harmful and occurs frequently. To reduce losses caused by the hazards, it is urgent to monitor the disaster area timely and carry out rescue operations efficiently. However, conventional space observers cannot achieve sufficient spatiotemporal resolution. As spaceborne GNSS-R technique can observe the Earth’s surface with high temporal and spatial resolutions; and it is expected to provide a new solution to the problem of flood hazards. During 19–21 July 2021, Henan province, China, suffered a catastrophic flood and urban waterlogging. In order to test the feasibility of flood disaster monitoring on a daily basis by using GNSS-R observations, the CYGNSS (Cyclone Global Navigation Satellite System) Level 1 Science Data were processed for a few days before and after the flood to obtain surface reflectivity by correcting the analog power. Afterwards, the flood was monitored and mapped daily based on the analysis of changes in surface reflectivity from spaceborne GNSS-R mission. The results were evaluated based on the image from MODIS (Moderate Resolution Imaging Spectroradiometer) data, and compared with the observations of SMAP (Soil Moisture Active Passive) in the same period. The results show that the area with high CYGNSS reflectivity corresponds to the flooded area monitored by MODIS, and it is also in high agreement with SMAP. Moreover, CYGNSS can achieve more detailed mapping and quantification of the inundated area and the duration of the flood, respectively, in line with the specific situation of the flood. Thus, spaceborne GNSS-R technology can be used as a method to monitor floods with high temporal resolution.

The Prospect of the Electroless Atomic Layer Deposition (ALD) for Applications in Semiconductor Technology

The metallic thin films are widely used for in semiconductor technology. Surface morphology, thickness and purity of the film are the key attributes determining its functionality 1 , 2. Recent developments indicate that Cu/liner/TaN structures where liner could be either Co, Ru or Rh and TaN serves as barrier are of particular interest. The electrochemical doping of Zn with liner material to develop barrier-less Cu structures has been demonstrated3. In this talk we demonstrate a protocol to deposit conformal Cu thin film on Co by replacement of Zn overlayer without etching Co. EDTA and BTA are used for corrosion inhibition of Co film once all Zn capping layer is consumed in galvanic displacement with Cu. TEM, XPS and EDS indicate conformal Cu layer deposition Co layer underneath being intact. The second part of the talk focuses on electroless ALD application for deposition of Pt and Rh layers on Cu, Au or Ru substrates. 4 , 5. Surface reflectivity changes during electroless ALD serves as an indicator of surface roughness evolution after each cycle. Surface morphology after 20 electroless ALD cycles is studied by STM and AFM. Cyclic voltammetry illustrates catalytically active Pt and Rh surface after deposition. All electroless ALDs are performed at room temperature in glove box with inert atmosphere. Surface selective, self-terminating deposition and precise control over the film thickness are achieved demonstrating potential of this deposition method for scale up and industrial applications. Ahmadi, K., Wu, D., Dole, N., R. Monteiro, O. & R. Brankovic, S. Tuning Surface Chemoresistivity of Au Ultrathin Films Using Metal Deposition via Surface-Limited Redox Replacement of the Underpotentially Deposited Pb Monolayer. ACS Sensors 4, 2442–2449 (2019).Wu, D. et al. Pb Monolayer Mediated Thin Film Growth of Cu and Co: Exploring Different Concepts. Journal of The Electrochemical Society 166, D3013–D3021 (2019).Kailash Venkatraman, Yezdi Dordi, A. J. Electrochemical doping of thin metal layers employing underpotential deposition and thermal treatment. 1 (2019).Wu, D. et al. Electroless Deposition of Pb Monolayer: A New Process and Application to Surface Selective Atomic Layer Deposition. Langmuir 34, 11384–11394 (2018).Wu, D. et al. Underpotential and Electroless Pb Monolayer Deposition on Ru(0001). Journal of The Electrochemical Society 166, D359–D365 (2019). Figure 1

Impact of deforestation on radiative and thermal regimes of the territory of Ukraine on the base of global climate models data

This paper is dedicated to the influence of partial deforestation with using global retrospective modelling data from The Land Use Model Intercomparison Project (LUMIP) for the territory of Ukraine. This experiment aims to global gradual deforestation and has two phases. The first phase, defined as the pre-industrial period (1850—1899) with constant unchangeable anthropogenic impact. For this period deforestation modelled with further replacement with grass cover with a linear trend 400000 km2/yr or 20 million km2 per 50 years in general. The second phase is next 30 years with no significant changes in forest cover (1900—1929). For conducting this research the data of several global climate models were applied. The results of analysis have demonstrated that a partial deforestation with grass substitution influences the surface reflectivity or albedo and redistribution of shortwave radiative fluxes. In turn, it provokes changes in thermal regime. It was found that the most significant changes in surface reflectivity and the strongest correlation coefficients between albedo and deforestation are in the winter season due to the presence of snow cover. As a result, statistical significant increase of albedo is with maximum values up to 24 %/50 years in some grids in winter. Then in the summer season maximal changes are up to 2.7 %/50 years due to small differences between forest and grass albedos. As a consequence, changes in albedo cause changes in surface and air temperature regimes. Strong dependencies were found in winter between changes in albedo and temperatures with maximum temperature decrease 2.5…2.0 %/50 years. In warm season correlations are weaker in comparison to cold season, but nevertheless, temperatures decrease also take place with maximum values 2.0…1.5 %/50 years. The analysis between deforestation and daily air temperature range has shown that particularly in winter season there is an increase of 0.5...1.5 %/50 years, whereas such tendency is not observed in warm season. Calculations of year air temperature range demonstrated controversial results among climate models, as follows it is hard to make a conclusion about the contribution of forest cover reduction to changes in this index. It was revealed, that global climate models with higher resolution are more sensitive to changes in albedo and, as a consequence to other characteristics than models with coarse ones. It should be noticed that obtained results concern pre-industrial period with minimal anthropogenic impact, when observed a stable snow cover in winter in Ukraine. In the current climate change with significant warming and reduction of snow season duration deforestation can have opposite effects on radiative and thermal regimes that require further studying.

An iterative phase-correction method for low-quality phase-shift images and its application

To solve the issue of phase recovery from low-quality fringe images (where the low quality is caused by noise, non-linear intensity and surface reflectivity changes), an iterative phase-correction method is proposed. The high-phase-precision solution is achieved by regenerating the phase-shift-fringe images from the wrapped phase and performing iterative filtering. In general, compared with conventional methods, the proposed iterative phase-correction method has three main advantages: (a) it suppresses the influence of noise without interference from surface reflectivity; (b) it effectively improves the wrapped phase accuracy, thereby improving 3D measurement accuracy, and (c) it effectively recovers the absolute phase (determined by a multi-frequency heterodyne method) information of surfaces with very low or very high reflectivities, without the additional projection of phase-shift-fringe images. The effectiveness of the proposed iterative phase-correction method is verified by simulation and experiment. The proposed method, applied to binocularly structured light, can improve measurement accuracy and greatly improve the effectiveness of measurement of objects with drastic changes in reflectivity. Based on the calibration accuracy (without gamma calibration of the projector) described in this paper, the root-mean-square deviation of the measurement results for binocularly structured light can reach 0.0094 mm.

Detection and impacts of tiling artifacts in MODIS burned area classification

Since 2000, observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, aboard the Terra and Aqua satellites, have been used to monitor global burned area and its trends. The FireCCI and MCD64A1 products classify burned area using algorithms that detect change in surface reflectance and separately process each ∼10° × 10° MODIS tile. We find that artifacts arise in both products from this tiling procedure. In particular, we find severe tiling artifacts in FireCCI, version 5.1 (FireCCI51) in northwest India and Pakistan, where the classified burned area is disjointed at the latitudinal boundary of two tiles that largely separates the Indian states of Punjab and Haryana. In contrast, this tiling effect is less noticeable in MCD64A1, Collection 6 (C6). As a result, while the average 2003–2019 October-November burned area in Haryana is of similar magnitude across the two products, that for Punjab is 13,381 km2 for MCD64A1 and just 1,486 km2 for FireCCI. We find moderate tiling artifacts in Southeast Asia and Eastern Europe. Our results highlight that additional processing is needed to ensure the continuity of burned area classification in FireCCI and MCD64A1, as well as other products relying on tile-dependent algorithms.

Scaling and discriminability of perceived gloss.

While much attention has been given to understanding biases in gloss perception (e.g., changes in perceived reflectance as a function of lighting, shape, viewpoint, and other factors), here we investigated sensitivity to changes in surface reflectance. We tested how visual sensitivity to differences in specular reflectance varies as a function of the magnitude of specular reflectance. Stimuli consisted of renderings of glossy objects under natural illumination. Using maximum likelihood difference scaling (MLDS), we created a perceptual scaling of the specular reflectance parameter of the Ward reflectance model. Then, using the method of constant stimuli and a standard 2AFC procedure, we obtained psychometric functions for gloss discrimination across a range of reflectance values derived from the perceptual scale. Both methods demonstrate that discriminability is significantly diminished at high levels of specular reflectance, thus indicating that gloss sensitivity depends on the magnitude of change in the image produced by different reflectance values. Taken together, these experiments also suggest that internal sensory noise remains constant for suprathreshold and near-threshold intervals of specular reflectance, which supports the use of MLDS as a highly efficient method for evaluating gloss sensitivity.

The Application of CYGNSS Data for Soil Moisture and Inundation Mapping in Australia

Cyclone global navigation satellite system (CYGNSS) has provided a valuable opportunity for high spatiotemporal monitoring of land surface reflectivity over the past few years. CYGNSS with a constellation of eight microsatellites is able to constantly observe the “scattered” global positioning system signals from the land. In this study, we validate the CYGNSS land reflectivity data in Australia for mapping the spatial extent of the inundated area and for determining temporal changes in surface soil moisture. CYGNSS level 1 data acquired for the period of 2017–2020 is assessed against various measurements, including satellite and ground-based measurements. Empirical mode decomposition is used to better analyze the CYGNSS time series and their relationship with the independent measurements. Furthermore, the mission's ability to capture surface reflectivity changes in response to extreme climatic events is analyzed. The results show that high spatial and temporal resolution CYGNSS data can largely represent the top layer ( $\sim$ 5 cm) soil moisture spatial and temporal variations close to soil moisture active passive. CYGNSS surface reflectivity results are also found to be sensitive to surface water changes and able to depict inundated land surface.

Spectral and diurnal temporal suitability of GOES Advanced Baseline Imager (ABI) reflectance for burned area mapping

TheAdvanced Baseline Imager (ABI) onboard the new generation of National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite (GOES) series provides 10 min, multi-spectral, 500 m to 2 km observations,with significantly improved capabilities compared to the previous GOES sensors. For the first time GOES data are available with sufficient resolution to potentially enable burned area mapping using the reflective wavelength data. In this study GOES-16 ABI unburned and burned reflectance acquired over four extensive burns in the United States and South America were examined. Significant diurnal variations in the ABI reflectance due to surface reflectance anisotropy and changes in the position of the sun at each ABI observation time were observed, with unburned near-infrared (NIR) reflectance that varied by more than 50% over the day, and this variation was greater than the change in reflectance due to fire. The diurnal reflectance variation included locally increased backscatter reflectance acquired under near hot-spot conditions when the solar zenith was close to the view zenith. The spectral and diurnal temporal suitability of the ABI data for burned area mapping was assessed for the red, NIR and short wave infrared (SWIR) bands using the transformed divergence metric applied to the unburned and burned reflectance values for each ABI acquisition time. The separabilities varied diurnally but with no consistent view-solar geometry or time when the separabilities were consistently high among the four sites. The paper results demonstrate that use of ABI reflectance without consideration of, or correction, for surface anisotropy will be unreliable. The most suitable ABI bands to detect burned areas are the NIR, which provided high diurnal separabilities except in the early morning and late evening, and the normalized burn ratio (NBR), computed as the difference between the NIR and SWIR reflectance divided by their sum, that had more consistently high diurnal separability. Implications and recommendations for future ABI burned area mapping research are discussed.

Assessing the effect of spatial resolution on the delineation of management zones for smallholder farming in southern Brazil

Remote sensing estimates of cover-crop aboveground biomass (AGB) have been used to delineate management zones for smallholder farming in southern Brazil. In this study, we investigated the spatial resolution influence on the AGB estimates of rye, calculated from regression relationships with the Normalized Difference Vegetation Index (NDVI), and on the subsequent delineation of management zones using the Management Zone Analyst (MZA) software. Data acquired by an Unmanned Aerial Vehicle (UAV) Parrot Sequoia camera (0.20 m spatial resolution) in Brazil were compared with observations from the PlanetScope (PS) satellite constellation (3 m) and the Operational Land Imager (OLI)/Landsat-8 (30 m). A three-endmember mixture model (green vegetation, soil, and shadow) was applied to surface reflectance data of these instruments for evaluating the cover-crop development at two dates in August 2017. Because of the differences in the technical specifications of the sensors, we resampled the UAV dataset into four levels of spatial resolution (1, 3, 10, and 30 m). Using the UAV map (0.20 m) as a reference, we obtained confusion matrices for the original and resampled data. The results showed that the increasing amounts of rye AGB from the beginning to the end of August promoted significant changes in surface reflectance and in soil-green vegetation fractions calculated at variable spatial resolution. The performance of the regression models to estimate cover-crop AGB was approximately similar in the transition from the sub-metric (0.20 m) to the metric (3 m) spatial resolutions, or from the UAV camera to the PS data. For all datasets, the MZA detected two management zones with zone 2 having higher cover-crop AGB than zone 1. When compared to the UAV management zone map (reference), the PS map had a moderate-to-substantial agreement, while the OLI/Landsat-8 map had a fair-to-moderate concordance. Substantial agreements with the reference map were observed at simulated 1 m and 3 m data, as indicated by Kappa coefficients of 0.73 and 0.63 and overall accuracies of 86.40% and 81.40%, respectively. We conclude that the 3 m spatial resolution data of the PS comprise an alternative to delineate management zones for smallholder farming in southern Brazil when compared to the very-high spatial resolution observations of the UAV cameras.