Search Results (7)

Extended shortwave infrared (eSWIR) detectors operating at high temperatures are widely utilized in planetary science. A high-performance eSWIR based on pBin InAs/GaSb/AlSb type-II superlattice (T2SL) grown on a GaSb substrate is demonstrated. It achieves the optimization of the device’s optoelectronic performance by adjusting the p-type doping concentration in the AlAs_0.1Sb_0.9/GaSb barrier. Experimental and TCAD simulation results demonstrate that both the device’s dark current and responsivity grow as the doping concentration rises. Here, the bulk dark current density and bulk differential resistance area are extracted to calculate the bulk detectivity for evaluating the photoelectric performance of the device. When the barrier concentration is 5 × 10¹⁶ cm⁻³, the bulk detectivity is 2.1 × 10¹¹ cm·Hz^1/2/W, which is 256% higher than the concentration of 1.5 × 10¹⁸ cm⁻³. Moreover, at 300 K (−10 mV), the 100% cutoff wavelength of the device is 1.9 μm, the dark current density is 9.48 × 10⁻⁶ A/cm², and the peak specific detectivity is 7.59 × 10¹⁰ cm·Hz^1/2/W (at 1.6 μm). An eSWIR focal plane array (FPA) detector with a 320 × 256 array scale was fabricated for this purpose. It demonstrates a remarkably low blind pixel rate of 0.02% and exhibits an excellent imaging quality at room temperature, indicating its vast potential for applications in infrared imaging. Full article

Highly sensitive infrared photodetectors are needed in numerous sensing and imaging applications. In this paper, we report on extended short-wave infrared (e-SWIR) avalanche photodiodes (APDs) capable of operating at room temperature (RT). To extend the detection wavelength, the e-SWIR APD utilizes a higher indium (In) composition, specifically In_0.3Ga_0.7As_0.25Sb_0.75/GaSb heterostructures. The detection cut-off wavelength is successfully extended to 2.6 µm at RT, as verified by the Fourier Transform Infrared Spectrometer (FTIR) detection spectrum measurement at RT. The In_0.3Ga_0.7As_0.25Sb_0.75/GaSb heterostructures are lattice-matched to GaSb substrates, ensuring high material quality. The noise current at RT is analyzed and found to be the shot noise-limited at RT. The e-SWIR APD achieves a high multiplication gain of $M~190$ at a low bias of $V_{bias}=-2.5\mathrm{V}$ under illumination of a distributed feedback laser (DFB) with an emission wavelength of 2.3 µm. A high photoresponsivity of $\mathfrak{R}>140\mathrm{A}/\mathrm{W}$ is also achieved at the low bias of $V_{bias}=-2.5\mathrm{V}$. This type of highly sensitive e-SWIR APD, with a high internal gain capable of RT operation, provides enabling technology for e-SWIR sensing and imaging while significantly reducing size, weight, and power consumption (SWaP). Full article

Canopy water content is a fundamental indicator for assessing the level of plant water stress. The correlation between changes in water content and the spectral reflectance of plant leaves at near-infrared (NIR) and short-wave infrared (SWIR) wavelengths forms the foundation for developing a new remote sensing index, the Infrared Canopy Dryness Index (ICDI), to monitor forest dryness that can be used to predict the consequences of water stress. This study introduces the index, that uses spectral reflectance analysis at near-infrared wavelengths, encapsulated by the Normalized Difference Infrared Index (NDII), in conjunction with specific canopy conditions as depicted by the widely recognized Normalized Difference Vegetation Index (NDVI). Development of the ICDI commenced with the construction of an NDII/NDVI feature space, inspired by a conceptual trapezoid model. This feature space was then parameterized, and the spatial region indicative of water stress conditions, referred to as the dry edge, was identified based on the analysis of 10,000 random observations. The ICDI was produced from the combination of the vertical distance (i.e., under consistent NDVI conditions) from an examined observation to the dry edge. Comparisons between data from drought-affected and non-drought-affected control plots in the Australian Northern Jarrah Forest affirmed that the ICDI effectively depicted forest dryness. Moreover, the index was able to detect incipient water stress several months before damage from an extended drought and heatwave. Using freely available satellite data, the index has potential for broad application in monitoring the onset of forest dryness. This will require validation of the ICDI in diverse forest systems to quantify the efficacy of the index. Full article

Metasurfaces, composed of micro-nano-structured planar materials, offer highly tunable control over incident light and find significant applications in imaging, navigation, and sensing. However, highly efficient polarization devices are scarce for the extended shortwave infrared (ESWIR) range (1.7~2.5 μm). This paper proposes and demonstrates a highly efficient all-dielectric diatomic metasurface composed of single-crystalline Si nanocylinders and nanocubes on SiO₂. This metasurface can serve as a nanoscale linear polarizer for generating polarization-angle-controllable linearly polarized light. At the wavelength of 2172 nm, the maximum transmission efficiency, extinction ratio, and linear polarization degree can reach 93.43%, 45.06 dB, and 0.9973, respectively. Moreover, a nonpolarizing beam splitter (NPBS) was designed and deduced theoretically based on this polarizer, which can achieve a splitting angle of ±13.18° and a phase difference of π. This beam splitter can be equivalently represented as an integration of a linear polarizer with controllable polarization angles and an NPBS with one-bit phase modulation. It is envisaged that through further design optimization, the phase tuning range of the metasurface can be expanded, allowing for the extension of the operational wavelength into the mid-wave infrared range, and the splitting angle is adjustable. Moreover, it can be utilized for integrated polarization detectors and be a potential application for optical digital encoding metasurfaces. Full article

As a direct indicator of vegetation photosynthesis, the fraction of absorbed photosynthetically active radiation (FPAR) serves as a critical input in a series of land surface models. While existing satellite FPAR products are generally at coarse resolutions ranging from 250 m to 1 km, operational FPAR products at fine resolution are urgently needed in studying land surface processes at the plot scale. However, existing methods for estimating fine-resolution FPAR were mainly designed for Landsat data, and few studies have attempted to develop algorithms for Sentinel-2 data. In particular, the operational estimation of decameter FPAR has a higher requirement for the algorithms in terms of generalizability, efficiency, accuracy, and adaptability to Sentinel-2 data. In this paper, we developed a retrieval chain on the Google Earth Engine (GEE) platform to estimate FPAR by learning the relationship between MODIS FPAR and Sentinel-2 surface reflectance. Scale-consistent multilinear models were used to model the relationship between MODIS FPAR and Sentinel-2 surface reflectance, and the model coefficients were regressed from the selected training samples. To account for the spectral and spatial characteristics of the Sentinel-2 data, we designed criteria for selecting training samples and compared different band combinations. Three strategies for band combination were used: (1) green, red, and near infrared (NIR) bands at 10 m resolution (i.e., three bands); (2) green, red, NIR, and red edge (RE) 1, RE2, and RE3 bands at 20 m resolution (i.e., five bands); and (3) green, red, NIR, RE1, RE2, RE3, shortwave infrared1 (SWIR1) and SWIR2 bands at 20 m resolution (i.e., eight bands). Meanwhile, the official Sentinel Application Platform (SNAP) method has also been implemented to estimate the Sentinel FPAR at 10 m and 20 m resolutions for comparison. Both methods were applied to the western Guanzhong area, Shaanxi Province, China, for FPAR estimation of all cloud-free Sentinel-2 images in 2021. The results show that the scaling-based method using five bands at 20 m resolution was the most accurate compared to the in situ measurements (RMSE = 0.076 and R² = 0.626), which outperformed the SNAP method at 10 m and 20 m resolutions and the scaling-based method using other strategies. The results of the scaling-based method using all three strategies were highly consistent with the MODIS FPAR product, while the SNAP method systematically underestimated FPAR values compared to the MODIS FPAR products. The proposed method is more ready-to-use and more efficient than SNAP software. Considering that the service of the MODIS sensor is overdue, the proposed method can be extended to alternatives to MODIS products, such as VIIRS and Sentinel-3 data. Full article

Effective mapping and monitoring of soil moisture content (SMC) in space and time is an expected application of remote sensing for agricultural development and drought mitigation, particularly in the context of global climate change impact, given that agricultural drought is occurring more frequently and severely worldwide. This study aims to develop a regional algorithm for estimating SMC by using Landsat 8 (L8) imagery, based on analyses of the response of soil reflectance, by corresponding L8 bands with the change of SMC from dry to saturated states, in all 103 soil samples taken in the central region of Vietnam. The L8 spectral band ratio of the near-infrared band (NIR: 850–880 nm, band 5) versus the short-wave infrared 2 band (SWIR2: 2110 to 2290 nm, band 7) shows the strongest correlation to SMC by a logarithm function (R² = 0.73 and the root mean square error, RMSE ~ 12%) demonstrating the high applicability of this band ratio for estimating SMC. The resultant maps of SMC estimated from the L8 images were acquired over the northern part of the Central Highlands of Vietnam in March 2015 and March 2016 showed an agreement with the pattern of severe droughts that occurred in the region. Further discussions on the relationship between the estimated SMC and the satellite-based retrieved drought index, the Normal Different Drought Index, from the L8 image acquired in March 2016, showed a strong correlation between these two variables within an area with less than 20% dense vegetation (R² = 0.78 to 0.95), and co-confirms the bad effect of drought on almost all areas of the northern part of the Central Highlands of Vietnam. Directly estimating SMC from L8 imagery provides more information for irrigation management and better drought mitigation than by using the remotely sensed drought index. Further investigations on various soil types and optical sensors (i.e., Sentinel 2A, 2B) need to be carried out, to extend and promote the applicability of the prosed algorithm, towards better serving agricultural management and drought mitigation. Full article

Cloud and snow detection is one of the most significant tasks for remote sensing image processing. However, it is a challenging task to distinguish between clouds and snow in high-resolution multispectral images due to their similar spectral distributions. The shortwave infrared band (SWIR, e.g., Sentinel-2A 1.55–1.75 µm band) is widely applied to the detection of snow and clouds. However, high-resolution multispectral images have a lack of SWIR, and such traditional methods are no longer practical. To solve this problem, a novel convolutional neural network (CNN) to classify cloud and snow on an object level is proposed in this paper. Specifically, a novel CNN structure capable of learning cloud and snow multiscale semantic features from high-resolution multispectral imagery is presented. In order to solve the shortcoming of “salt-and-pepper” in pixel level predictions, we extend a simple linear iterative clustering algorithm for segmenting high-resolution multispectral images and generating superpixels. Results demonstrated that the new proposed method can with better precision separate the cloud and snow in the high-resolution image, and results are more accurate and robust compared to the other methods. Full article