How Well Do We Know Europa’s Topography? An Evaluation of the Variability in Digital Terrain Models of Europa

Jupiter’s moon Europa harbors one of the most likely environments for extant extraterrestrial life. Determining whether Europa is truly habitable requires understanding the structure and thickness of its ice shell, including the existence of perched water or brines. Stereo-derived topography from images acquired by NASA Galileo’s Solid State Imager (SSI) of Europa are often used as a constraint on ice shell structure and heat flow, but the uncertainty in such topography has, to date, not been rigorously assessed. To evaluate the current uncertainty in Europa’s topography we generated and compared digital terrain models (DTMs) of Europa from SSI images using both the open-source Ames Stereo Pipeline (ASP) software and the commercial SOCET SET® software. After first describing the criteria for assessing stereo quality in detail, we qualitatively and quantitatively describe both the horizontal resolution and vertical precision of the DTMs. We find that the horizontal resolution of the SOCET SET® DTMs is typically 8–11× the root mean square (RMS) pixel scale of the images, whereas the resolution of the ASP DTMs is 9–13× the maximum pixel scale of the images. We calculate the RMS difference between the ASP and SOCET SET® DTMs as a proxy for the expected vertical precision (EP), which is a function of the matching accuracy and stereo geometry. We consistently find that the matching accuracy is ~0.5 pixels, which is larger than well-established “rules of thumb” that state that the matching accuracy is 0.2–0.3 pixels. The true EP is therefore ~1.7× larger than might otherwise be assumed. In most cases, DTM errors are approximately normally distributed, and errors that are several times the derived EP occur as expected. However, in two DTMs, larger errors (differences) occur and correlate with real topography. These differences primarily result from manual editing of the SOCET SET® DTMs. The product of the DTM error and the resolution is typically 4–8 pixel2 if calculated using the RMS image scale for SOCET SET® DTMs and the maximum images scale for the ASP DTMs, which is consistent with recent work using martian data sets and suggests that the relationship applies more broadly. We evaluate how ASP parameters affect DTM quality and find that using a smaller subpixel refinement kernel results in DTMs with smaller (better) resolution but, in some cases, larger gaps, which are sometimes reduced by increasing the size of the correlation kernel. We conclude that users of ASP should always systematically evaluate the choice of parameters for a given dataset.

Evaluating the Consistency and Continuity of Pixel-Scale Cloud Property Data Records From Aqua and SNPP

The Aqua, SNPP, and JPSS satellites carry a combination of hyperspectral infrared sounders (AIRS, CrIS) and high-spatial-resolution narrowband imagers (MODIS, VIIRS). They provide an opportunity to acquire high-quality long-term cloud data records and are a key component of the existing Program of Record of cloud observations. By matching observations from sounders and imagers across different platforms at pixel scale, this study evaluates the self-consistency and continuity of cloud retrievals from Aqua and SNPP by multiple algorithms, including the AIRS Version-7 retrieval algorithm and the Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) Version-2 for sounders, and the Standard Aqua-MODIS Collection-6.1 and the NASA MODIS-VIIRS continuity cloud products for imagers. Metrics describing detailed statistical distributions at sounder field of view (FOV) and the joint histograms of cloud properties are evaluated. These products are found highly consistent despite their retrieval from different sensors using different algorithms. Differences between the two sounder cloud products are mainly due to cloud clearing and treatment of clouds in scenes with unsuccessful atmospheric profile retrievals. The sounder subpixel cloud heterogeneity evaluated using the standard deviation of imager retrievals at sounder FOV shows good agreement between the standard and continuity products from different satellites. However, impact of algorithm and instrument differences between MODIS and VIIRS is revealed in cloud top pressure retrievals and in the imager cloud distribution skewness. Our study presents a unique aspect to examine NASA’s progress toward building a continuous cloud data record with sufficient quality to investigate clouds’ role in global environmental change.

Detecting the Turning Points of Grassland Autumn Phenology on the Qinghai-Tibetan Plateau: Spatial Heterogeneity and Controls

Autumn phenology, commonly represented by the end of season (EOS), is considered to be the most sensitive and crucial productivity indicator of alpine and cold grassland in the Qinghai-Tibetan Plateau. Previous studies typically assumed that the rates of EOS changes remain unchanged over long time periods. However, pixel-scale analysis indicates the existence of turning points and differing EOS change rates before and after these points. The spatial heterogeneity and controls of these turning points remain unclear. In this study, the EOS turning point changes are extracted and their controls are explored by integrating long time-series remote sensing images and piecewise regression methods. The results indicate that the EOS changed over time with a delay rate of 0.08 days/year during 1982–2015. The rates of change are not consistent over different time periods, which clearly highlights the existence of turning points. The results show that temperature contributed most strongly to the EOS changes, followed by precipitation and insolation. Furthermore, the turning points of climate, human activities (e.g., grazing, economic development), and their intersections are found to jointly control the EOS turning points. This study is the first quantitative investigation into the spatial heterogeneity and controls of the EOS turning points on the Qinghai-Tibetan Plateau, and provides important insight into the growth mechanism of alpine and cold grassland.

An Adaptive-Parameter Pixel Unmixing Method for Mapping Evergreen Forest Fractions Based on Time-Series NDVI: A Case Study of Southern China

Spectral unmixing remains the most popular method for estimating the composition of mixed pixels. However, the spectral-based unmixing method cannot easily distinguish vegetation with similar spectral characteristics (e.g., different forest tree species). Furthermore, in large areas with significant heterogeneity, extracting a large number of pure endmember samples is challenging. Here, we implement a fractional evergreen forest cover-self-adaptive parameter (FEVC-SAP) approach to measure FEVC at the regional scale from continuous intra-year time-series normalized difference vegetation index (NDVI) values derived from moderate resolution imaging spectroradiometer (MODIS) imagery acquired over southern China, an area with a complex mixture of temperate, subtropical, and tropical climates containing evergreen and deciduous forests. Considering the cover of evergreen forest as a fraction of total forest (evergreen forest plus non-evergreen forest), the dimidiate pixel model combined with an index of evergreen forest phenological characteristics (NDVIann-min: intra-annual minimum NDVI value) was used to distinguish between evergreen and non-evergreen forests within a pixel. Due to spatial heterogeneity, the optimal model parameters differ among regions. By dividing the study area into grids, our method converts image spectral information into gray level information and uses the Otsu threshold segmentation method to simulate the appropriate parameters for each grid for adaptive acquisition of FEVC parameters. Mapping accuracy was assessed at the pixel and sub-pixel scales. At the pixel scale, a confusion matrix was constructed with higher overall accuracy (87.5%) of evergreen forest classification than existing land cover products, including GLC 30 and MOD12. At the sub-pixel scale, a strong linear correlation was found between the cover fraction predicted by our method and the reference cover fraction obtained from GF-1 images (R2 = 0.86). Compared to other methods, the FEVC-SAP had a lower estimation deviation (root mean square error = 8.6%). Moreover, the proposed method had greater estimation accuracy in densely than sparsely forested areas. Our results highlight the utility of the adaptive-parameter linear unmixing model for quantitative evaluation of the coverage of evergreen forest and other vegetation types at large scales.

Automatic Assembly of Bolts and Nuts Based on Machine Vision Recognition

In this study, based on the computer vision technology, we developed a recognition system for nuts positioning to complete the automatic bolt assembly part of the automatic production line. The actual image of the nut was captured by an industrial camera, which would be processed by the following edge detection and Hough circle transformation. After that, the coordinates of the nuts were obtained in the pixel scale. Finally, the real position of nuts would be fed back to the robot arm, according to which the automatic assembly of the bolt would be completed. This computer vision based recognition system is an indispensable part for the efficiency and accuracy improvement of automatic production line.

MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning

The High-Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter provides remotely sensed imagery at the highest spatial resolution at 25–50 cm/pixel of the surface of Mars. However, due to the spatial resolution being so high, the total area covered by HiRISE targeted stereo acquisitions is very limited. This results in a lack of the availability of high-resolution digital terrain models (DTMs) which are better than 1 m/pixel. Such high-resolution DTMs have always been considered desirable for the international community of planetary scientists to carry out fine-scale geological analysis of the Martian surface. Recently, new deep learning-based techniques that are able to retrieve DTMs from single optical orbital imagery have been developed and applied to single HiRISE observational data. In this paper, we improve upon a previously developed single-image DTM estimation system called MADNet (1.0). We propose optimisations which we collectively call MADNet 2.0, which is based on a supervised image-to-height estimation network, multi-scale DTM reconstruction, and 3D co-alignment processes. In particular, we employ optimised single-scale inference and multi-scale reconstruction (in MADNet 2.0), instead of multi-scale inference and single-scale reconstruction (in MADNet 1.0), to produce more accurate large-scale topographic retrieval with boosted fine-scale resolution. We demonstrate the improvements of the MADNet 2.0 DTMs produced using HiRISE images, in comparison to the MADNet 1.0 DTMs and the published Planetary Data System (PDS) DTMs over the ExoMars Rosalind Franklin rover’s landing site at Oxia Planum. Qualitative and quantitative assessments suggest the proposed MADNet 2.0 system is capable of producing pixel-scale DTM retrieval at the same spatial resolution (25 cm/pixel) of the input HiRISE images.

Upscaling Evapotranspiration from a Single-Site to Satellite Pixel Scale

It is of great significance for the validation of remotely sensed evapotranspiration (ET) products to solve the spatial-scale mismatch between site observations and remote sensing estimations. To overcome this challenge, this paper proposes a comprehensive framework for obtaining the ground truth ET at the satellite pixel scale (1 × 1 km resolution in MODIS satellite imagery). The main idea of this framework is to first quantitatively evaluate the spatial heterogeneity of the land surface, then combine the eddy covariance (EC)-observed ET (ET_EC) to be able to compare and optimize the upscaling methods (among five data-driven and three mechanism-driven methods) through direct validation and cross-validation, and finally use the optimal method to obtain the ground truth ET at the satellite pixel scale. The results showed that the ET_EC was superior over homogeneous underlying surfaces with a root mean square error (RMSE) of 0.34 mm/d. Over moderately and highly heterogeneous underlying surfaces, the Gaussian process regression (GPR) method performed better (the RMSEs were 0.51 mm/d and 0.60 mm/d, respectively). Finally, an integrated method (namely, using the ET_EC for homogeneous surfaces and the GPR method for moderately and highly heterogeneous underlying surfaces) was proposed to obtain the ground truth ET over fifteen typical underlying surfaces in the Heihe River Basin. Furthermore, the uncertainty of ground truth ET was quantitatively evaluated. The results showed that the ground truth ET at the satellite pixel scale is relatively reliable with an uncertainty of 0.02–0.41 mm/d. The upscaling framework proposed in this paper can be used to obtain the ground truth ET at the satellite pixel scale and its uncertainty, and it has great potential to be applied in more regions around the globe for remotely sensed ET products’ validation.

Comprehensive Insights into Spatial-Temporal Evolution Patterns, Dominant Factors of NDVI from Pixel Scale, as a Case of Shaanxi Province, China

Based on long term NDVI (1982–2015), climate, topographic factors, and land use type data information in Shaanxi Province, multiple methods (linear regression, partial and multiple correlation analysis, redundancy analysis and boosted regression trees method) were conducted to evaluate the spatial-temporal change footprints and driving mechanisms in the pixel scale. The results demonstrated that (1) the overall annual average and seasonal NDVI in this region showed a fluctuating upward trend, especially in spring. The difference between the end of season (eos) and start of season (sos) gradually increased, indicating the occurrence of temporal “greening” across most Shaanxi Province. (2) The overall spatial distribution of annual mean NDVI in Shaanxi Province was prominent in the south and low in the north, and 98.83% of the areas had a stable and increasing trend. Pixel scale analysis reflected the spatial continuity and heterogeneity of NDVI evolution. (3) Trend and breakpoint evaluation results showed that evolutionary trends were not homogeneous. There were obvious breakpoints in the latitude direction of NDVI evolution in Shaanxi Province, especially between 32–33°N and in the north of 37° N. (4) Compared with precipitation, the annual average temperature was significantly correlated with the vegetation indices (annual NDVI, max NDVI, time integrated NDVI) and phenology metrics (sos, eos). (5) Considering the interaction between environmental variables, the NDVI evolution was dominated by the combined influence of climate and geographic location factors in most areas.