Fusing Active and Passive Remotely Sensed Soil Moisture Products Using an Improved Double Instrumental Variable Method

Yang, Zekun; Chen, Xi; Cui, Yaokui; Lv, Feng; Yao, Zhaoyuan; Li, Sien; Wu, Lifeng; Fan, Junfeng; Geng, Xiaozhuang; Fan, Wenjie

doi:10.1109/tgrs.2022.3207753

Cited by 2 publications

(1 citation statement)

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“…Remote sensing technologies are of great concerns with its non‐intrusiveness, wide‐coverage, and high‐resolution preponderances in numerous fields, including geography, agriculture, land surveying, and environmental monitoring. [ 1 , 2 , 3 ] Hyperspectral imaging is an essential technique in remote sensing, primarily aimed at classifying and recognizing the image using hyperspectral data. [ 4 , 5 ] Hyperspectral imaging is usually accomplished by spectrometers which contain bulky optical components (e.g., tunable filter arrays, [ 6 ] Fourier transform interferometers, [ 7 ] dispersive grating [ 8 ] ).…”

Section: Introductionmentioning

confidence: 99%

Spectrometer‐Less Remote Sensing Image Classification Based on Gate‐Tunable van der Waals Heterostructures

Yu,

Zhong,

Xiong

et al. 2024

Advanced Science

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Remote sensing technology, which conventionally employs spectrometers to capture hyperspectral images, allowing for the classification and unmixing based on the reflectance spectrum, has been extensively applied in diverse fields, including environmental monitoring, land resource management, and agriculture. However, miniaturization of remote sensing systems remains a challenge due to the complicated and dispersive optical components of spectrometers. Here, m‐phase GaTe0.5Se0.5 with wide‐spectral photoresponses (250–1064 nm) and stack it with WSe2 are utilizes to construct a two‐dimensional van der Waals heterojunction (2D‐vdWH), enabling the design of a gate‐tunable wide‐spectral photodetector. By utilizing the multi‐photoresponses under varying gate voltages, high accuracy recognition can be achieved aided by deep learning algorithms without the original hyperspectral reflectance data. The proof‐of‐concept device, featuring dozens of tunable gate voltages, achieves an average classification accuracy of 87.00% on 6 prevalent hyperspectral datasets, which is competitive with the accuracy of 250–1000 nm hyperspectral data (88.72%) and far superior to the accuracy of non‐tunable photoresponse (71.17%). Artificially designed gate‐tunable wide‐spectral 2D‐vdWHs GaTe0.5Se0.5/WSe2‐based photodetector present a promising pathway for the development of miniaturized and cost‐effective remote sensing classification technology.

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