A critical review on applications of hyperspectral remote sensing in crop monitoring

Abstract

Summary Numerous technologies have contributed to the recent development of agriculture, especially the advancement in hyperspectral remote sensing (HRS) constituted a revolution in crop monitoring. The widespread use of HRS to obtain crop parameters suggests the need for a review of research advances in this area. HRS offers new theories and methods for studying crop parameters, but much work needs to be done both experimentally and theoretically before we can truly understand the physical and chemical processes that predict these crop parameters. The study focuses on the following elements: 1) The article provides a relatively comprehensive introduction to HRS and how it can be applied to crop monitoring; 2) Current state-of-the-art techniques are summarized and analyzed to inform further advances in crop monitoring; 3) Opportunities and challenges for crop monitoring applications using HRS are discussed, and future research is summarized. Finally, through a comprehensive discussion and analysis, the article proposes new directions for using HRS to study crop characteristics, such as new data mining techniques including deep learning provide opportunities for efficient processing of large amounts of HRS data; combining the temporal and dynamic characteristics of crop parameters and vegetation growth processes will greatly improve the accuracy of crop parameter detection and monitoring; multidata fusion and multiscale data assimilation will become HRS monitoring. Multidata fusion and multiscale data assimilation will become another research hotspot for HRS monitoring of crop parameters.

The Weight of Hyperion and PRISMA Hyperspectral Sensor Characteristics on Image Capability to Retrieve Urban Surface Materials in the City of Venice

“…Despite the fact that research has been carried out for about 40 years in this field, there are many shortcomings yet to be overcome in all of the steps included in hyperspectral image pre-processing [ 49 , 72 , 73 ], processing [ 9 , 46 , 74 ], and validation [ 30 , 31 , 75 ]. However, not only the pre-processing, processing, and validation steps, but also sensor characteristics determine the capability of hyperspectral images.…”

“…An updated list of satellite hyperspectral sensors includes 12 existing sensors; among them, 8 sample the spectrum from visible (VIS) to near infrared (NIR) [ 9 ]: the Compact High Resolution Imaging Spectrometer—CHRIS—aboard the PROBA platform was launched in October 2001 and funded by the European Space Agency [ 10 ]; the Atmospheric Infrared Sounder—AIRS—onboard the AQUA platform was launched in May 2002, and the overall project was managed by the JPL [ 11 ]; the Hyperspectral Imaging Satellite—HySI—onboard the IMS-1 platform was launched in April 2008 and funded by the Indian Space Research Organization (ISRO) [ 12 ]; the Hyperspectral Imager—HSI—onboard the HJ-1A platform was launched in September 2008 and funded by the China Center for Resources Satellite Data and Application (CRESDA) [ 13 ]; the Hyperspectral Imager for the Coastal Ocean—HICO—onboard the ISS platform was launched in September 2009 and funded by the United States Office of Naval Research [ 14 ]; the Hyperspectral Imager—HSI—onboard the FLORA platform was launched in 2016 [ 9 ]; Orbital hyperspectral satellites—OHSs—onboard OVS-1A/B platforms were launched in June 2017 [ 15 ]; the DLR Earth Sensing Imaging Spectrometer—DESIS—onboard the ISS platform was launched in August 2018 and funded by the German Aerospace Center (DLR) [ 16 ].…”

“…The four remaining sensors sample the spectrum from VIS to short-wave infrared (SWIR), including the Hyperion sensor [ 9 ]: the Advanced Hyperspectral Imager—AHSI—onboard the GF-5 platform was launched in May 2018 and funded by the People’s Republic of China [ 17 ]; PRecursore IperSpettrale of the Application Mission—PRISMA—was launched in March 2019 and funded by the Italian Space Agency (ASI) [ 18 ]; the Hyperspectral Imager Suite—HISUI—onboard the ALOS3 platform was launched in December 2019 and developed by the Japanese Ministry of Economy, Trade, and Industry (METI) [ 19 ]; the Environmental Mapping and Analysis Program—EnMAP-HIS—was launched in April 2022 by the DLR [ 20 ]. It is interesting to note not only that most new satellite hyperspectral sensors sample the spectrum from VIS to SWIR, but also that most countries are investing in this technology.…”

Enhancing Feature Detection and Matching in Low-Pixel-Resolution Hyperspectral Images Using 3D Convolution-Based Siamese Networks

“…With recent advancements in technology, sensors have become smaller, making them more accessible and mountable on different platforms, including manned aircraft, Unmanned Aerial Vehicles (UAVs), and laboratory-based acquisition systems. In the remote sensing domain, being able to mount these acquisition systems on UAVs has opened up whole new streams of research, in the agriculture remote sensing domain [ 6 , 7 , 8 , 9 ] as well as in such application areas forestry [ 10 , 11 ], environmental monitoring [ 12 , 13 ], security [ 14 ], and geology [ 15 ].…”

Understanding the Temporal Dynamics of Coherence and Backscattering Using Sentinel-1 Imagery for Crop-Type Mapping

“…It can complement or replace traditional field surveys. With the advantages of high resolution, low cost, and wide coverage, it has the ability to obtain crop planting structure, area, and spatial distribution in a timely and accurate manner [2], [9], [10]. In particular, synthetic aperture radar (SAR) imagery excels as an active microwave remote sensing technique, and it has the following advantages over optical imagery [11]: 1) all-weather, all-day and all-time data acquisition capability, suitable for temporal analysis; 2) strong penetration capabilities through vegetation, soil, and dry snow with minimal atmospheric interference; 3) multiple polarization channels enhancing sensitivity to observe earth surface scattering mechanisms; 4) interferometric capabilities enabling temporal information extraction form coherence analysis.…”