High-resolution Remote Sensor Research Articles

Combining a climate-permafrost model with fine resolution remote sensor products to quantify active-layer thickness at local scales

Quantification of active-layer thickness (ALT) over seasonally frozen terrains is critical to understand the impacts of climate warming on permafrost ecosystems in cold regions. Current large-scale process-based models cannot characterize the heterogeneous response of local landscapes to homogeneous climatic forcing. Here we linked a climate-permafrost model with a machine learning solution to indirectly quantify soil conditions reflected in the edaphic factor using high resolution remote sensor products, and then effectively estimated ALT across space and time down to local scales. Our nine-year field measurements during 2014–2022 and coincident high resolution airborne hyperspectral, lidar, and spaceborne sensor products provided a unique opportunity to test the developed protocol across two permafrost experiment stations in lowland terrains of Interior Alaska. Our developed model could explain over 60% of the variance of the field measured ALT for estimating the shallowest and deepest ALT in 2015 and 2019, suggesting the potential of the designed procedure for projecting local varying terrain response to long-term climate warming scenarios. This work will enhance the National Aeronautics and Space Administration’s Arctic-Boreal Vulnerability Experiment’s mission of combining field, airborne, and spaceborne sensor products to understand the coupling of permafrost ecosystems and climate change.

Reduction and reconstruction strategy of active thermal control system based on unsupervised learning and thermo-optics for spaceborne high-resolution remote sensor

The development of small Earth observation satellites with high performance optical remote sensors has received much attention in recent years due to its responsiveness and immediacy. As one of the most resource-intensive subsystems of spacecrafts, the active thermal control system (ATCS) should be optimized; however, manual thermal design does not guarantee an optimal solution and requires months. For this purpose, we presented a ATCS’s reduction and reconstruction strategy based on an unsupervised learning framework, in which factor loadings are applied to combine kernel-based principal component analysis and Gaussian mixture model clustering. This strategy was implemented on a spaceborne high-resolution remote sensor (HRS) with 0.5 m ground sample distance. As a result, with the cumulative variance contribution rate > 90 % and 10 Gaussian distributions, the reconstructed ATCS of HRS had a 78.6 % reduction rate and took only 3 h, which suggests the high efficiency and low computational cost of the proposed strategy. Finally, the performed orbital thermal and thermo-optical integrated analyses indicated that temperature fluctuations were within ± 0.02–0.6 °C and optical surface errors were within the tolerance. The average modulation transfer function in the tangential and sagittal directions decreased by only 2 % and 2.5 %, respectively, and the radius of the spot root mean square was considerably smaller than the size of one pixel (8.75 μm). These results prove the feasibility and validity of the proposed strategy. This study solved the problem of identifying the optimal ATCS for optical remote sensors and significantly reducing the ATCS’s resource occupancy, which provided a reliable research basis for the development of high-performance small Earth observation satellites. Additionally, the proposed strategy is versatile and can be used in thermal engineering applications of various complex spacecrafts and payloads.

A comprehensive calibration site for high resolution remote sensors dedicated to quantitative remote sensing and its applications

A comprehensive calibration site for high resolution remote sensors dedicated to quantitative remote sensing and its applications

Mapping the Forage Nitrogen-Phosphorus Ratio Based on Sentinel-2 MSI Data and a Random Forest Algorithm in an Alpine Grassland Ecosystem of the Tibetan Plateau

Nondestructive and accurate estimating of the forage nitrogen–phosphorus (N:P) ratio is conducive to the real-time diagnosis of nutrient limitation and the formulation of a management scheme during the growth and development of forage. New-generation high-resolution remote sensors equipped with strategic red-edge wavebands offer opportunities and challenges for estimating and mapping forage N:P ratio in support of the sustainable utilization of alpine grassland resources. This study aims to detect the forage N:P ratio as an ecological indicator of grassland nutrient content by employing Sentinel-2 multispectral instrument (MSI) data and a random forest (RF) algorithm. The results showed that the estimation accuracy (R2) of the forage N:P ratio model established by combining the optimized spectral bands and vegetation indices (VIs) is 0.49 and 0.59 in the vigorous growth period (July) and the senescing period (November) of forage, respectively. Moreover, Sentinel-2 MSI B9 and B12 bands contributed greatly to the estimation of the forage N:P ratio, and the VIs (RECI2) constructed by B5 and B8A bands performed well in the estimation of the forage N:P ratio. Overall, it is promising to map the spatial distribution of the forage N:P ratio in alpine grassland using Sentinel-2 MSI data at regional scales. This study will be potentially beneficial in implementing precise positioning of vegetation nutrient deficiency and scientific fertilization management of grassland.

A Permanent Bar Pattern Distributed Target for Microwave Image Resolution Analysis

The characterization and understanding of microwave remote sensing image quality is essential to the monitoring performance of sensors and the proper usage of the acquired image. Point targets (e.g., passive corner reflectors and active transponders) have been widely used for microwave image resolution analysis. However, the analysis results based on point targets do not include the effects of speckle and thermal noise that are rare for point targets but common for distributed targets. Since distributed targets are common in remote sensing imaging scenes and their distinguishability is of interest in practice, the concept of an optical bar pattern target was extended to the microwave band, and a microwave bar pattern target was designed and permanently built at the National Calibration and Validation Site for High-Resolution Remote Sensors. In this letter, the main design idea is first introduced. Different backscatter coefficients of the bars were achieved for different surface roughness made by black rough gravel and a white smooth concrete plate, where the gravel size was designed per the Rayleigh roughness criterion. The experimental results using C-band airborne SAR and X-band KOMPSAT-5 SAR images are presented. A quantitative analysis shows that this target could roughly evaluate the image resolution of high-resolution microwave imaging sensor and would be a good complement to point targets.

Discussion on the Method of High-resolution Remote Sensor Data Interpretation of Haloxylon Ammodendron in Ulan Buh Desert

According to ALOS,SPOT5 high-resolution remote sensing image analysis and field investigation,we have completed the remote sensing information visual extraction and vegetation mapping in Ulan Buh Desert,while combined with GPS field records,we have finished accuracy evaluation,the result was satisfactory.The accuracy of visual interpretation was about 93.3%,the area Haloxylon Ammodendron was 165 410.62 hm2,accounting for Ulan Buh Desert 17.27% of total land area.Meanwhile,methods of remote sensing image interpretation,which were suitable for Haloxylon forest in the Ulan Buh Desert,were also discussed.

Characterizing the spatial structure of vegetation communities in the Mojave Desert using geostatistical techniques

Geostatistical techniques are used to evaluate spatial characteristics of vegetation communities mapped within a portion of the Mojave Desert, California. Spatial structures of different vegetation communities were characterized using nugget, range and sill parameters of spherical model variograms. Model variograms reveal different vegetation communities have distinctive spatial properties that are quantified effectively by the parameters of these models. Model variograms were fit to experimental variograms calculated from plant width values of ground based vegetation data collected for 96 1000×2 m transects. The maximum model variogram range for all communities was 39 m, suggesting transects must be at least this long to classify them unambiguously into the communities currently recognized. Experimental variograms were calculated for all plants in each of eight mapped vegetation communities. The Bush Seepweed Series has a distinctively long range, for example, and the Creosote Bush Series has a distinctively high nugget and sill. These findings support use of high-resolution remote sensors and geostatistics for determining vegetation community structure. Spatial pattern information produced by these methods could improve broad scale vegetation classifications produced by low-resolution remote sensing systems.