Local Incidence Angle Research Articles

A new model for high-accuracy monitoring of water level changes via enhanced water boundary detection and reliability-based weighting averaging

Accurate measurement of water levels is essential for effectively managing reservoirs to proactively mitigate flooding and drought. Nonetheless, the inaccuracies in measurements derived from gauging station and remote sensing images impose constraints to water resource management. In this study, we developed a novel water level estimation model which utilizes solely the altitude of reliable water boundary pixels to improve the accuracy. The enhanced water boundary detection, incorporating preprocessing steps such as image filtering, resampling, and polarization multiplication, was applied to achieve sub-pixel precision in detecting water boundaries. The water boundary pixels located in layover and shadow regions, which could be misidentified due to distortion error, are eliminated based on backward geolocation. Ambiguous water boundaries, potentially indicating land with low intensity, were defined by computing their absolute derivatives, and removed. Finally, to enhance water level precision, the model computed water levels by averaging the altitudes of boundary pixels with weighting factors of local incidence angle, derivatives of detected water boundaries, and altitude distribution. Compared with the previous studies utilizing water boundaries, the proposed model demonstrated outstanding performance in improving the accuracy, up to 1/40th smaller than the spatial resolution of SAR images in Mean Absolute Error (MAE). The validation was executed over the results from >700 Sentinel-1 against the in-situ measurements obtained from multiple reservoirs and streams with significant water level fluctuations in the Korean peninsula. In this process, we found that the water boundaries located on the layover and shadow regions significantly influence the dispersion of altitude of reliable water boundary pixels. This study demonstrates the proposed model relying on remote sensing data without in-situ measurements, which holds potential applicability under situations where in-situ data are unavailable.

Sputtering yield increase with fluence in low-energy argon plasma-tungsten interaction

The increase of tungsten sputtering yield with fluence was investigated during plasma (≤100 eV) irradiation. This analysis focused on the combined effects of surface binding energy and surface morphology caused by Ar retention. As post-mortem analysis, Ar concentration was measured with SIMS (Secondary Ion Mass Spectroscopy) and TDS (Thermal Desorption Spectroscopy). The Ar concentration saturated at 21 % of W number density during the sputtering process. The corresponding change in surface morphology causes a change in the local ion incident angle, which leads to a sputtering yield increase of 10 %. The increased Ar concentration leads to a decrease in surface binding energy and a change in surface morphology which increases W sputtering yield from 0.02 to 0.03 by ion energy 80 eV. Over time, W sputtering yield reaches saturation as a function of saturated Ar concentration. This result implies that the synergistic role of Ar concentration and surface morphology on sputtering yield. This sputtering yield enhancement occurs more seriously in the realistic condition of plasma-facing materials that face low-energy plasma.

Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China

Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data.

Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17

Abstract. A physical–statistical framework to estimate snow water equivalent (SWE) and snow depth from synthetic aperture radar (SAR) measurements is presented and applied to four SnowSAR flight-line data sets collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, snow density and snow depth, and physical model parameters. Prior distributions are derived from multilayer snow hydrology predictions driven by downscaled numerical weather prediction (NWP) forecasts. To reduce the signal-to-noise ratio, SnowSAR measurements at 1 m resolution were upscaled by simple averaging to 30 and 90 m resolution. To reduce the number of physical parameters, the multilayer snowpack is transformed for Bayesian inference into an equivalent one- or two-layer snowpack with the same snow mass and volume backscatter. Successful retrievals meeting NASEM (2018) science requirements are defined by absolute convergence backscatter errors ≤1.2 dB and local SnowSAR incidence angles between 30 and 45∘ for X- and Ku-band VV-pol backscatter measurements and were achieved for 75 % to 87 % of all grassland pixels with SWE up to 0.7 m and snow depth up to 2 m. SWE retrievals compare well with snow pit observations, showing strong skill in deep snow with average absolute SWE residuals of 5 %–7 % (15 %–18 %) for the two-layer (one-layer) retrieval algorithm. Furthermore, the spatial distributions of snow depth retrievals vis-à-vis lidar estimates have Bhattacharya coefficients above 94 % (90 %) for homogeneous grassland pixels at 30 m (90 m resolution), and values up to 76 % in mixed forest and grassland areas, indicating that the retrievals closely capture snowpack spatial variability. Because NWP forecasts are available everywhere, the proposed approach could be applied to SWE and snow depth retrievals from a dedicated global snow mission.

Bird's Nest-Shaped Sb2 WO6 /D-Fru Composite for Multi-Stage Evaporator and Tandem Solar Light-Heat-Electricity Generators.

Herein, an integrated solar-thermal-power protocol is presented at a micro-nanoscopic level to maximize the energy utilization efficiency involving utilization period and utilization patterns, and the nexus of freshwater production and nanogeneration is realized. This sophisticated vaporization device is constructed with the merits of thermally confined evaporation space in favor of recycling latent heat of condensation and optimizing light absorption based on the local sunlight angle of incidence. Inspired by a bird's nest, Sb2 WO6 /D-Fructose composites are prepared as photothermal absorbers to achieve a superior water evaporation rate of 2.78kgm-2 h-1 in the Multi-stage evaporator. In addition, a synergistic tandem photo thermal-electric device with a combination of solar-driven water evaporation and further waterflow-driven hydrovoltaic generation, which can output a stable voltage of up to 360.8mV with effective utilization of steam energy and a limited water source, is exploited. Such integrated configurations pave a pathway for clean water production and renewable power generation simultaneously toward energy issues.

Design of soft x-ray varied-line-spacing (VLS) high-dispersion laminar-type grating coated with super-mirror-type (SMT) multilayer for flat-field spectrograph in a region of 2-4 keV.

A soft x-ray varied-line-spacing (VLS) laminar-type spherical grating with a super-mirror-type (SMT) multilayer was designed for a soft x-ray high resolution flat-field spectrograph in a region of 2-4keV. The effective groove density of the designed VLS grating is 3200 lines/mm, and the local groove density varies from 2700 to 3866 lines/mm. The geometrical imaging property was evaluated by numerical calculations. The resolving power estimated by means of ray tracing was up to ∼103. For the evaluation of diffraction efficiency, the SMT multilayer structure designed for 3200 lines/mm in our previous work, Koike etal., Rev. Sci. Instrum. 94, 045109 (2023), was employed, and the numerical calculation was performed considering the local groove density of VLS grooves and the local incidence angle being affected by the curvature of the spherical surface and the geometrical relation between the source and incidence point on the grating. The results showed that the SMT multilayer-coated grating exhibited about an order of magnitude higher diffraction efficiency compared with an Au-coated grating.

Estimation of Forest Height Using Google Earth Engine Machine Learning Combined with Single-Baseline TerraSAR-X/TanDEM-X and LiDAR

Forest height plays a crucial role in various fields, such as forest ecology, resource management, natural disaster management, and environmental protection. In order to obtain accurate and efficient measurements of forest height over large areas, in this study, Terra Synthetic Aperture Radar-X and the TerraSAR-X Add-on for Digital Elevation Measurement (TerraSAR-X/TanDEM-X), Sentinel-2A, and Shuttle Radar Topography Mission (SRTM) data were used, and various feature combinations were established in conjunction with measurements from Light Detection and Ranging (LiDAR). Classification and regression tree (CART), gradient-boosting decision tree (GBDT), random forest (RF), and support vector machine (SVM) algorithms were employed to estimate forest height in the study area. Independent validation on the basis of LiDAR forest height samples showed the following results: (1) Regarding feature combinations, the combination of coherence and decorrelation of volume scattering provided by TerraSAR-X/TanDEM-X data outperformed the combination of backscatter coefficient and local incidence angle, as well as the combination of coherence, decorrelation of volume scattering, backscatter coefficient, and local incidence angle. The best results (R2 = 0.67, RMSE = 2.89 m) were achieved with the combination of coherence and decorrelation of volume scattering using the GBDT and RF algorithms. (2) In terms of machine learning methods, the GBDT algorithm proved suitable for estimating forest height. The most effective approach for forest height mapping involved combining the GBDT algorithm with coherence, decorrelation of volume scattering, and a small amount of LiDAR forest height data, used as training data.

Performance of tungsten plasma facing components in the stellarator experiment W7-X: Recent results from the first OP2 campaign

The transition to reactor-relevant materials for the plasma facing components (PFCs) is an important and necessary step to provide a proof of principle that the stellarator concept can meet the requirements of a future fusion reactor by demonstrating high performance steady-state operation. As a first step to gain experience with tungsten as plasma-facing material in the Wendelstein 7-X (W7-X) stellarator, graphite tiles coated with an approximately 10 µm MedC tungsten layer (NILPRP Bucharest) were installed to complete the ECRH beam dump area in two of the five plasma vessel modules over an area of approximately one square meter each. In addition, tungsten baffle tiles are installed (40 tiles in total) with either bulk tungsten as part of NBI shine-through target or with a tungsten heavy alloy (W95-Ni3.5-Cu1.5) to replace the graphite tiles that were previously thermally overloaded. Based on an advanced diffusive field line tracing method and EMC3-Eirene simulations, the overloaded baffle tiles were redesigned by making the tiles thinner (i.e. moving the plasma-facing surface (PFS) away from the hot plasma region) and by reducing the local angle of incidence through toroidal displacement of the watershed. Significant erosion of the tungsten tiles can only be expected if sputtering by impurity ions such as carbon or oxygen ions contributes. However, the resulting central concentration of tungsten and the corresponding radiation losses are expected to be marginal. The expected deposition of carbon on the tungsten surfaces in the baffle regions mitigates further the possible tungsten enrichment in the core plasma. In OP2.1, no adverse effects of the installed tungsten PFCs on the plasma performance were observed during normal plasma operation. With the design changes made in the baffle area, the predicted heat load reductions could be experimentally confirmed.

Numerical investigations on the mechanisms of the tip leakage vortex cavitation development in a cryogenic inducer with large eddy simulation

Liquid hydrogen is considered clean energy and is usually pressurized by cryogenic pumps in various industries. To ensure the safe operation of cryogenic pumps, the inducer is installed in front of the pump to improve the impeller inlet pressure but causes cavitation instabilities. This paper aims to investigate the mechanisms of the tip leakage vortex (TLV) cavitating flow in a cryogenic inducer with liquid nitrogen. The large eddy simulations model was used to analyze the thermodynamic effects on the tip leakage vortex cavitation (TLVC). The cavity structure and the pulsation mechanisms of the TLVC were analyzed through the flow characteristics and the vorticity transportation process. The predicted cavitation performance is in good agreement with the experimental measurements. The numerical results showed that the TLVC is suppressed and forms the separation point between the primary TLVC and the secondary TLVC due to the thermodynamic effects. The inhibition rate of the vapor volume fraction at the leading edge is 30%. The pressure fluctuations are caused by the propagation pattern of the detached cavity interacting with the adjacent blade periodically. The velocity triangles near the detached cavity were proposed to reveal the development of the TLVC. It indicates that TLVC instability is caused by the periodic coupling effect of the cavity development, the flow rate magnitude, and the local incidence angle variation. The vorticity transport equation is utilized to investigate the interaction of cavitation and vortex. Comparing the three terms reveals that the stretching and bending term dominates in the vorticity production of the TLV cavitating flow. The dilatation term controls the transportation of vorticity inside the TLV cavity, while the contribution of the baroclinic torque term is negligible in comparison to the other terms. This study provides a reference for optimizing the TLV cavitating flow and instabilities for designing the cryogenic pump.

Classification of Land Cover in Complex Terrain Using Gaofen-3 SAR Ascending and Descending Orbit Data

Synthetic aperture radar (SAR) image is an effective remote sensing data source for geographic surveys. However, accurate land cover mapping based on SAR image in areas of complex terrain has become a challenge due to serious geometric distortions and the inadequate separation ability of dual-polarization data. To address these issues, a new land cover mapping framework which is suitable for complex terrain is proposed based on Gaofen-3 data of ascending and descending orbits. Firstly, the geometric distortion area is determined according to the local incident angle, based on analysis of the SAR imaging mechanism, and the correct polarization information of the opposite track is used to compensate for the geometric distortion area, including layovers and shadows. Then, the dual orbital polarization characteristics (DOPC) and dual polarization radar vegetation index (DpRVI) of dual-pol SAR data are extracted, and the optimal feature combination is found by means of Jeffries–Matusita (J-M) distance analysis. Finally, the deep learning method 2D convolutional neural network (2D-CNN) is applied to classify the compensated images. The proposed method was applied to a mountainous region of the Danjiangkou ecological protection area in China. The accuracy and reliability of the method were experimentally compared using the uncompensated images and the images without DpRVI. Quantitative evaluation revealed that the proposed method achieved better performance in complex terrain areas, with an overall accuracy (OA) score of 0.93, and a Kappa coefficient score of 0.92. Compared with the uncompensated image, OA increased by 5% and Kappa increased by 6%. Compared with the images without DpRVI, OA increased by 4% and Kappa increased by 5%. In summary, the results demonstrate the importance of ascending and descending orbit data to compensate geometric distortion and reveal the effectiveness of optimal feature combination including DpRVI. Its simple and effective polarization information compensation capability can broaden the promising application prospects of SAR images.

Surface morphology and carbon structure effects on sputtering: Bridging scales between molecular dynamics simulations and experiments

Experiments report large scatter in the sputtering yield of carbon materials under the bombardment of low-energy noble gas ions. Here, we conduct scale-bridging molecular dynamics (MD) simulations on the xenon bombardment of carbon substrates across ion energies of 75 eV to 2 keV, and at ion incidence angles of 0°–75°, to resolve uncertainties in the sputtering data. Results show rapid amorphization of the carbon subsurface with ion bombardment, but the structural characteristics (sp/sp2/sp3 bond proportion, atomic density) eventually plateau once steady-state sputtering is achieved. In addition, we obtain virtually indistinguishable “steady-state” amorphous structures across the range of ion energies and ion incidence angles, as well as for several different carbon structures, which suggests that the steady-state sputtering yield data is independent of the initial carbon structure and prior sputtering history. By accounting for changes in the local incidence angle, surface shielding, and redeposition of the sputterants associated with surface morphology effects, we demonstrate that the sputtering predictions from MD are in perfect agreement with prior experiments across the range of ion energies and incidence angles. Using a Bayesian approach based on the MD data, we calibrate the parameters of a semi-empirical sputtering model for different surface morphologies.

The Use of Random Forest Regression for Estimating Leaf Nitrogen Content of Oil Palm Based on Sentinel 1-A Imagery

For obtaining a spatial map of the distribution of nitrogen nutrients from oil palm plantations, a quite complex Leaf Sampling Unit (LSU) is required. In addition, sample analysis in the laboratory is time consuming and quite expensive, especially for large plantation areas. Monitoring the nutrition of oil palm plants can be achieved using remote-sensing technology. The main obstacles of using passive sensors in multispectral imagery are cloud cover and shadow noise. This research used C-SAR Sentinel equipped with active sensors that can overcome cloud barriers. A model to estimate leaf nitrogen nutrient status was constructed using random forest regression (RFR) based on multiple polarization (VV-VH) and local incidence angle (LIA) data on Sentinel-1A imagery. A sample of 1116 LSU data from different islands (i.e., Sumatra, Java, and Kalimantan) was used to develop the proposed estimation model. The performance evaluation of the model obtained the averaged MAPE, correctness, and MSE of 9.68%, 90.32% and 11.03%, respectively. Spatial maps of the distribution of nitrogen values in certain oil palm areas can be produced and visualized on the web so that they can be accessed easily and quickly for various purposes of oil palm management such as fertilization planning, recommendations, and monitoring.

The influence of Sentinel-1 SAR sub-swath on the recorded backscatter time-series over managed hemiboreal forests

Abstract The view angle range of Sentinel-1 SAR in the Interferometric Wide swath (IW) measurement mode is 29.1° – 46.0°. The dependence of backscatter on the arbitrary local incidence angle is usually corrected using a linear regression model where the incidence angle is a predictor variable. We analysed the whole time series of Sentinel-1 SAR VV-polarised backscatter over the flat 15×15 km test site in Laeva, Estonia (26° 26′ 43″ E; 58° 31′ 56″ N). Time series containing measurements from three different orbits were constructed for 3,159 stands from nighttime data and for 1,105 stands from daytime data. We can confirm that daytime backscatter is systematically greater than nighttime backscatter. We found a significant deviation from linearity in the backscatter dependence on local incidence angle. The empirical finding may be caused by the microwave scattering dependence on local incidence angle or by the influence of Sentinel-1 SAR sub-swath configuration in the Terrain Observation with Progressive Scans SAR (TOPSAR) method that is used for the measurements.

Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and Leaf Area Index

In cotton, an optimal balance between vegetative and reproductive growth will lead to high yields and water-use efficiency. Remote sensing estimations of vegetation variables such as crop coefficient (Kc), Leaf Area Index (LAI), and crop height during plant development can improve irrigation management. Optical and Synthetic Aperture Radar (SAR) satellite imagery can be a useful data source since they provide synoptic cover at fixed time intervals. Furthermore, they can better capture the spatial variability in the field compared to point measurements. Since clouds limit optical observations at times, the combination with SAR can provide information during cloudy periods. This study utilized optical imagery acquired by Sentinel-2 and SAR imagery acquired by Sentinel-1 over cotton fields in Israel. The Sentinel-2-based vegetation indices that are best suited for cotton monitoring were identified, and the most robust Sentinel-2 models for Kc, LAI, and height estimation achieved R2 = 0.879, RMSE= 0.0645 (MERIS Terrestrial Chlorophyll Index, MTCI); R2 = 0.9535, RMSE= 0.8 (MTCI); and R2 = 0.8883, RMSE= 10 cm (Enhanced Vegetation Index, EVI), respectively. Additionally, a model based on the output of the SNAP Biophysical Processor LAI estimation algorithm was superior to the empirical LAI models of the best-performing vegetation indices (R2 =0.9717, RMSE=0.6). The most robust Sentinel-1 models were obtained by applying an innovative local incidence angle normalization method with R2 = 0.7913, RMSE= 0.0925; R2 = 0.6699, RMSE= 2.3; R2 = 0.6586, RMSE= 18 cm for the Kc, LAI, and height estimation, respectively. This work paves the way for future studies to design decision support systems for better irrigation management in cotton, even at the sub-plot level, by monitoring the heterogeneous development of the crop from space and adapting the irrigation accordingly to reach the target development at different growth stages during the season.

Remote sensing‐based retrieval of soil moisture content using stacking ensemble learning models

AbstractMachine learning combined with multisource remote sensing data to assess soil moisture content (SMC) has attracted considerable attention in SMC studies, but the retrieval results still remain uncertain. The purpose of this study is to combine multiple single machine learning models with integrated learning algorithms and propose an SMC retrieval method based on multiple differentiated models under a stacking integrated learning architecture. First, 19 factors, including: radar backscattering coefficient, vegetation index, and drought index, that affect SMC were extracted from SENTINEL‐1, LANDSAT, and terrain factors. Those with the highest importance scores were selected as retrieval factors using the Boruta algorithm combined with four single machine learning methods—classified regression tree, random forest, gradient boosting decision tree (GBDT), and extreme random tree. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and the generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that radar backscattering coefficient, temperature, vegetation drought index, land surface temperature, enhanced vegetation index, and solar local incident angle were the most important environmental variables for soil moisture retrieval. A comparison of the four machine learning methods in April and August showed that the GBDT model revealed the highest SMC retrieval accuracy, with root mean square error values of 1.87% and 1.64%, respectively. The stacking models were more accurate than the optimal single machine learning model, especially when using GBM. The multifactor integrated model constructed using spectral indices, radar backscatter coefficients, and topographic data exhibited high accuracy in soil surface moisture retrieval in an arid zone, providing a reference for land desertification studies and ecological environment management in the study region.

Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles.

Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition's geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with and RMSE CV = 0.88 m2 m-2. The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloudprone agri-environments.

Spanwise distribution of the loads on a hydrofoil working in the wake of an upstream propeller

The spanwise distribution of the loads acting on a hydrofoil working in the wake of a propeller is analyzed both as a function of the propeller load and the incidence of the hydrofoil. The analysis is based on data from Large Eddy Simulations, conducted on cylindrical grids consisting of O(109) points. Although very large coherent structures are shed by the propeller at outer and inner radii (tip and hub vortices), the average loads on the hydrofoil are characterized by peaks correlating with the radial coordinate of maximum load of the propeller blades, equivalent to about 70% of their outer radius. The azimuthal velocity of the propeller wake causes a displacement of the stagnation point in front of the hydrofoil, affecting substantially the pressure fields on its port and starboard sides and, as a result, the loads it experiences. This flow physics suggests that twisted geometries, able to decrease the local incidence angle, can produce substantial benefits on the stresses experienced by rudders operating in the wake of upstream propellers.

Topographic Correction of the SELENE MI Images with the LOLA DEM around Shackleton Crater

Due to the complicated terrain and extreme illumination condition in the lunar south polar region, topography has an impact on the reflectance of the area. This may result in the misinterpretation of the properties of the lunar surface. Therefore, an image obtained across Shackleton Crater by the Multi-band Imager (MI) onboard the first Japanese lunar orbiter SELENE (SELenological and ENgineering Explorer) was utilized to evaluate the topographic effect on the reflectance of the polar region. Three methods—i.e., b correction, C correction, and Minnaert correction—were applied to the MI image to reduce the topographic effect. It was found that the reflectance result of C correction suffers from overcorrection. The topographic effect is enlarged, rather than suppressed, in the reflectance derived with Minnaert correction. Comparatively, b correction is the more appropriate method for the topographic correction of the MI image across the study area. The reflectance of the sunlit slope of the crater wall in the study area decreased by ~60%. The reflectance of the area outside of the crater, with a gentler slope compared to the crater wall, decreased by ~20%. Meanwhile, according to the correlation of the cosine of the local solar incidence angle and the corrected reflectance, the topographic effect was seemingly not completely eliminated in the MI image. However, by analyzing the spectral absorption features of the 1250 nm band, we can attribute this “residual” effect to the different compositions inside and outside of Shackleton Crater, most likely caused by a high concentration of plagioclase. Topographic correction of the MI images over NASA candidate landing regions was also conducted. The results suggest that the topographic effect can contribute significantly to the reflectance of the sunlit slopes, and should not be neglected in the analysis of the reflectance images. This study highlights the topographic impact on the reflectance of the lunar south pole’s surface. In addition, our results also suggest that the compositional differences under various terrains should be considered in the evaluation of the topographic correction of specific regions, such as Shackleton Crater’s inner walls.

Integrating an attention-based deep learning framework and the SAFY-V model for winter wheat yield estimation using time series SAR and optical data

Information on the spatial distribution of yields can be obtained over a large area by using remote sensing (RS) data. Combining Synthetic Aperture Radar (SAR), being sensitive to above ground biomass and soil moisture in all weather conditions, and optical data can improve the usability of RS data and provide a basis for pixel-based crop yield estimation (YE). In this study, an Upscaled Convolutional Gated Recurrent Unit model incorporated an attention mechanism (UpSc-AConvGRU model) was proposed to improve the estimation accuracy of the winter wheat growth parameter, Leaf Area Index (LAI). Gap filling the time series of optical data was done with backscatter coefficients, local incidence angles and polarimetric decomposition information from Sentinel-1 SAR imagery. The time series LAI estimated by the UpSc-AConvGRU model and Vegetation Temperature Condition Index (VTCI) retrieved from Sentinel-3 optical imagery were then used as state variables of the SAFY-V model to estimate winter wheat yield. The results showed that the proposed UpSc-AConvGRU model incorporated the Convolutional Block Attention Module (CBAM) can effectively improve the accuracy of LAI estimation, with RMSEs ranging from 0.413 to 0.699 m2 m2 for LAI estimated within main growth stages (MGSs) of winter wheat. The correlation between estimated LAI and Sentinel-3 retrieved LAI was generally higher at irrigated farmland compared to rain-fed farmland. The estimated LAI was closest to Sentinel-3 retrieved LAI at the green-up and late heading-filling stage of winter wheat, followed by the jointing and early heading-filling stage, and finally the milk maturity stage. There was good agreement between the SAFY-V model estimated and field measured winter wheat yields (R2 = 0.546, RMSE = 0.757 t ha−1), and the estimated yields at the pixel scale in the Guanzhong Plain, PR China were satisfactory. This study combined deep learning and crop growth modeling, proposed a new pixel scale winter wheat YE method.

Multicriteria Accuracy Assessment of Digital Elevation Models (DEMs) Produced by Airborne P-Band Polarimetric SAR Tomography in Tropical Rainforests

The penetration capability of P-band radar waves through dense vegetation, along with the ability of tomography to separate the contributions of different layers in a vertical reflectivity profile, make P-band radar tomography a promising tool for digital terrain modeling in forested areas, specifically in dense tropical forests under which terrain topography remains poorly known. This paper aims to assess the overall quality of a digital terrain model (DTM) produced using tomographic processing of airborne P-band SAR imagery acquired during the TropiSAR campaign in French Guiana. Many quality descriptors are used to evaluate the quality of this DTM. Position and slope accuracies are computed based on a lidar DTM considered as the reference, and the impact of several parameters on these accuracies is studied, namely, slope, slope orientation, off-nadir angle and local incidence angle. The realism of the landforms is also studied according to geomorphological criteria. The results of this multicriteria accuracy assessment show the high potential of P-band SAR tomography in depicting the topography under forests, despite the intrinsic limitations related to the slant range geometry: the absolute elevation error is around 2 m; the slope is overestimated with an error of about 16°, mainly due to a processing artifact for which easy and direct solutions exist. Indeed, this error is equal to about 3° in flat artifact-free areas. These errors vary depending on the acquisition parameters and the local topography. The shapes are globally well preserved. These results are also discussed in the frame of the upcoming BIOMASS mission developed by the European Space Agency (ESA) and expected to be launched in 2024.