Resolution Optical Sensors Research Articles

Low cost, high temporal resolution optical fiber-based γ-photon sensor for real-time pre-clinical evaluation of cancer-targeting radiopharmaceuticals

Cancer radiopharmaceutical therapies (RPTs) have demonstrated great promise in the treatment of neuroendocrine and prostate cancer, giving hope to late-stage metastatic cancer patients with currently very few treatment options. These therapies have sparked a large amount of interest in pre-clinical research due to their ability to target metastatic disease, with many research efforts focused towards developing and evaluating targeted RPTs for different cancer types in in vivo models. Here we describe a method for monitoring real-time in vivo binding kinetics for the pre-clinical evaluation of cancer RPTs. Recognizing the significant heterogeneity in biodistribution of RPTs among even genetically identical animal models, this approach offers long-term monitoring of the same in vivo organism without euthanasia in contrast to ex vivo tissue dosimetry, while providing high temporal resolution with a low-cost, easily assembled platform, that is not present in small-animal SPECT/CTs. The method utilizes the developed optical fiber-based γ-photon biosensor, characterized to have a wide linear dynamic range with Lutetium-177 (177Lu) activity (0.5–500 μCi/mL), a common radioisotope used in cancer RPT. The probe’s ability to track in vivo uptake relative to SPECT/CT and ex vivo dosimetry techniques was verified by administering 177Lu-PSMA-617 to mouse models bearing human prostate cancer tumors (PC3-PIP, PC3-flu). With this method for monitoring RPT uptake, it is possible to evaluate changes in tissue uptake at temporal resolutions <1 min to determine RPT biodistribution in pre-clinical models and better understand dose relationships with tumor ablation, toxicity, and recurrence when attempting to move therapies towards clinical trial validation.

Massive sensitisation of Eu3+ photoluminescence in NaMgF3:Eu nanoparticles by 2-thenoyltrifluoroacetone ligand exchange

NaMgF3:0.1%Eu nanocuboids capped with oleic acid were synthesised via a hydrothermal synthesis technique. Subsequent ligand exchange using 2-thenoyltrifluoroacetone resulted in a massive sensitisation of the Eu3+ photoluminescence where the intensity of the 5D0 → 7F2 transition increased by over 40 000×, relative to the oleic acid-capped nanoparticles. Large Judd-Ofelt Ω2 parameters indicated that the sensitised Eu3+ emissions originated primarily from distorted Eu3+ sites near the nanoparticle surfaces. The massive enhancement of the Eu3+ photoluminescence in 2-thenoyltrifluoroacetone sensitised NaMgF3:Eu nanoparticles encourages applications that include ultra-high resolution optical temperature sensors and dosimeters.

Ultrahigh-resolution and ultra-simple fiber-optic sensor with resonant Sagnac interferometer.

The resonant fiber-optic sensor (RFOS) is well known for its high sensing resolution but usually suffers from high cost and system complexity. In this Letter, we propose an ultra-simple white-light-driven RFOS with a resonant Sagnac interferometer. By superimposing the output of multiple equivalent Sagnac interferometers, the strain signal is amplified during the resonance. A 3 × 3 coupler is employed for demodulation, by which the signal under test can be read out directly without any modulation. With 1 km delay fiber and ultra-simple configuration, a strain resolution of 28f ε/Hz at 5 kHz is demonstrated in the experiment, which is among the highest, to the best of our knowledge, resolution optical fiber strain sensors.

Perspective: Improving the accuracy of plant phenology observations and land-cover and land-use detection by optical satellite remote-sensing in the Asian tropics

Recent advances in satellite-borne optical sensors led to important developments in the monitoring of tropical ecosystems in Asia, which have been strongly affected by recent anthropogenic activities and climate change. Based on our feasibility analyses conducted in Indonesia in Sumatra and Sarawak, Malaysia in Borneo, we discuss the current situation, problems, recent improvements, and future tasks regarding plant phenology observations and land-cover and land-use detection. We found that the Multispectral Instrument (MSI) on board the Sentinel-2A/2B satellites with a 10-m spatial resolution and 5-day observational intervals could be used to monitor phenology among tree species. For the Advanced Himawari Imager (AHI) on board the Himawari-8 geostationary satellite with a 1,000-m spatial resolution and 10-min observational intervals, we found that the time-series in vegetation indices without gaps due to cloud contamination may be used to accurately detect the timing and patterns of phenology among tree species, although the spatial resolution of the sensor requires further improvement. We also found and validated that text and pictures with geolocation information published on the Internet, and historical field notes could be used for ground-truthing land cover and land use in the past and present time. The future development of both high frequency (≤ 10 min) and high spatial resolution (≤ 10 m) optical sensors aboard satellites is expected to dramatically improve our understanding of ecosystems in the tropical Asia.

Temperature-immune Fabry-Perot cavity sensor based on an opened hollow-core anti-resonant fiber.

A new approach to conquer the thermal phase drift of an optical fiber Fabry-Perot interferometer (FPI) sensor is proposed and experimentally demonstrated. By employing a hollow-core anti-resonant fiber (HC-ARF) and optimizing the fusion splicing (includes mode field adaptation) between the lead-in single-mode fiber (SMF) and the HC-ARF, a high spectral resolution (λ/Δλ ≈ 3.8 × 104) optical fiber air-cavity FPI sensor with a fringe visibility higher than 7 dB is constructed. To eliminate the thermal phase drift (i.e. temperature crosstalk) of the sensor that originates from the intrinsic thermal expansion effect of the silica material of the HC-ARF, the FPI air cavity is connected to the external environments, by which the effect of air expelling from the cavity with temperature increasing can well compensate the temperature-induced cavity elongation. As a result, the thermal phase drift of the FPI is reduced to zero at a temperature range of ∼ 80-110 °C and within the temperature range of 40-80 °C, the thermal phase drift is still halved compared with the sealed FPI cavity. The nearly zero thermal phase drift of a FPI at such a temperature range has never been achieved before, to our best knowledge. As a proof of concept, a temperature-immune fiber-optic strain sensor is demonstrated. This work offers a new and efficient approach to eliminate the thermal phase drift (i.e. temperature crosstalk) of a fiber-optic device, which may significantly improve the measurement accuracy and detection limit of fiber-optic FPI sensors. Furthermore, the principle and schema can be generalized to a wide variety of fiber-optic devices.

Onorbit Operational Results of Small and High Resolution Optical Sensor (SHIROP)

Onorbit Operational Results of Small and High Resolution Optical Sensor (SHIROP)

Polymer-Based High Diffraction Efficiency and High Resolution Volume Holographic Transmission Gratings

We report on the optical characterization of very high-efficiency and high-resolution holographic volume phase transmission gratings. The gratings are recorded in a new photo-polymerizable mixture made by epoxy-resin and multi-acrylate. The epoxy-resin used is known to make tenacious acrylate-based films. The holographic mixture contains two photo-initiators, the synergic effect of which enables a reliable photo-polymerization process in the visible region of the electromagnetic spectrum. The recorded holograms are mechanically stable, show long-term temporal stability and very high values of diffraction efficiency, coupled with good angular selectivity due to a relatively narrow band of wavelengths. We measured the intensity of the transmitted beam and calculated the intensity of the diffracted beam at different wavelengths, deriving the refractive index modulation and the grating pitch by fitting the experimental data with a slightly modified theoretical approach. These kind of mixtures can be used in several fields of application, such as chemical or bio-sensors, high resolution optical sensors, high-density optical data storage, encryption and security.

Intelligent Rider Optimization Algorithm with Deep Learning Enabled Hyperspectral Remote Sensing Imaging Classification

Hyperspectral imaging (HSI) can be attained by the use of high resolution optical sensors and it comprises several spectral bands of the identical remote sensing target and is treated as a three-dimension (3D) dataset. Recently, deep learning (DL) techniques are gained important attention among research communities for image classification. In this aspect, this study develops an intelligent rider optimization algorithm with deep learning enabled HSI classification model, named IRODL-HSIC technique. The proposed IRODL-HSIC technique aims to categorize the different class labels of the multispectral images. Besides, the IRODL-HSIC technique applies singular value decomposition. Moreover, the ResNet-152 technique was executed as a feature extractor to generate a collection of features. Furthermore, the rider optimization algorithm with cascaded recurrent neural network (CRNN) approach is utilized for the classification process. For ensuring the enhanced performance of the IRODL-HSIC algorithm, a wide range of simulations take place utilizing the multispectral images and the outcomes are examined under different aspects. The extensive comparative study highlighted the better performance of the IRODL-HSIC technique over the recent methods.

PROCEDURE OF EXTRACTING THIN ICE AREAS IN THE SEA OF OKHOTSK USING MODIS DATA

Abstract. The latest IPCC report clearly stated that the human influence is the main reason of sea ice reduction in the Arctic. Importance of sea ice monitoring from space is increasing. In addition, the heat flux of ice in thin ice areas is strongly affected by the ice thickness difference. Therefore, ice thickness is one of the important parameters of sea ice. The authors have been studying on extracting thin ice areas using optical sensor such as MODIS for years. In this paper, the authors summarized the procedure of our study starting from comparing in-situ measurement of ice thickness with high resolution optical sensor RSI data, and finally developing the thin ice area extraction algorithm using MODIS. Estimating ice thickness from optical sensor data is not easy. However, through our study, the authors have verified the possibility of extracting thin ice areas using optical sensor data observed from satellites. In this study, the authors define “thin ice areas” as ice which thickness is less than about 30 cm with reflectance much lower than thick ice. The basic idea of the algorithm is to use the scatterplots of MODIS band 1 &amp;amp; 2 reflectance and extract thin ice areas using the distribution difference of thin ice against water, thick ice, clouds. Not all but most of the thin ice areas could be extracted using the algorithm.

Improvement of VHR Satellite Image Geometry with High Resolution Elevation Models

The number of high and very high resolution (VHR) optical satellite sensors, as well as the number of medium resolution satellites is continuously growing. However, not all high-resolution optical satellite imaging cameras have a sufficient and stable calibration in time. Due to their high agility in rotation, a quick change in viewing direction can lead to satellite attitude oscillation, causing image distortions and thus affecting image geometry and geo-positioning accuracy. This paper presents an approach based on re-projection of regularly distributed 3D ground points from object in image space, to detect and estimate the periodic distortions of Pléiades tri-stereo imagery caused by satellite attitude oscillations. For this, a hilly region was selected as a test site. Consequently, we describe a complete processing pipeline for computing the systematic height errors (deformations, waves) of the satellite-based digital elevation model by using a Lidar high resolution terrain model. Ground points with fixed positions, but with two elevations (actual and corrected) are then re-projected to the satellite images with the aid of the Rational Polynomial Coefficients (RPCs) provided with the imagery. Therefore, image corrections (displacements) are determined by computing the differences between the distinct positions of corresponding points in image space. Our experimental results in Allentsteig (Lower Austria) show that the systematic height errors of satellite-based elevation models cannot be compensated with an usual or even high number of Ground Control Points (GCPs) for RPC bias correction, due to insufficiently known image orientations. In comparison to a reference Lidar Digital Terrain Model (DTM), the computed elevation models show undulation effects with a maximum height difference of 0.88 m in along-track direction. With the proposed method, image distortions in-track direction with amplitudes of less than 0.15 pixels were detected. After applying the periodic distortion compensation to all three images, the systematic elevation discrepancies from the derived elevation models were successfully removed and the overall accuracy in open areas improved by 33% in the RMSE. Additionally, we show that a coarser resolution reference elevation model (AW3D30) is not feasible for improving the geometry of the Pléiades tri-stereo satellite imagery.

Global intercomparison of functional pyrodiversity from two satellite sensors

ABSTRACT Fires create substantial ecosystem disturbances, which affect biodiversity and climate processes. Previously, satellite-derived burned areas were assessed, and fire patches and morphological traits related to fire spread functions identified. The cut-off value in days is a critical parameter for the algorithms used to identify fire patches, as it identifies when two contiguous burned pixels should be grouped together as one patch. Our understanding of how fire patches and their morphologies change with the cut-off values between satellite sensors is currently insufficient for model development and pyrogeographic studies. We have addressed this using the FRY v1.0 global database of fire patch functional traits, with cut-off values of 3, 5, 9, and 14 days, derived from two burned area products, the MERIS Fire_cci version 4.1 and MODIS MCD64A1 collection 6, from two satellite sensors with different spatial and temporal resolutions. We intercompared the FRY products with functional diversity trait methods that assess their morphological pyrodiversity structures and dissimilarities. We have assessed the functional pyrodiversity of the FRY products with four cut-off values, for fire patches ≥ 300 ha, on 1° resolution tiles for the world and Global Fire Emissions Database (GFED) region scales, from 2005–2011. The functional structures and dissimilarities between the FRY products varied with the regions, while cut-off value sensitivity varied with the regions and between the satellite sensor products. These variations in functional structures and dissimilarities mirrored the regional fire spread processes and satellite sensor resolutions. The functional pyrodiversity that varied with the regions and similar cut-off values did not lead to the best agreement between the satellite sensor products. These variations reinforce the need to combine high temporal resolution optical sensors with radar in tropical regions, to consistently meet the monitoring requirements and for international organizations, to enable improved assessments of climate and biodiversity.

On-Board Ship Detection for Medium Resolution Optical Sensors.

In recent years there has been an increased interest in ocean surveillance. The activity includes control and monitoring of illegal fisheries, manmade ocean pollution and illegal sea traffic surveillance, etc. The key problem is how to identify ships and ship-like objects accurately and in a timely manner. In this context, currently, many solutions have been proposed based on high resolution optical and radar remote sensing systems. Most often, these systems suffer from two major limitations viz., limited swath, thereby requiring multiple satellites to cover the region of interest and huge volumes of data being transmitted to ground, even though effective per-pixel information content is minimal. Another limitation is that the existing systems are either simulated on ground or built using the non-space qualified/Commercial Of-The-Shelf (COTS) components. This paper proposes an efficient on-board ship detection system/package connected with medium resolution wide swath optical camera. The methodology adopted has three major components, viz., onboard data processing for improving the radiometric fidelity, followed by a ship detection using modified Constant False Alarm Rate algorithm (CFAR) and a false alarm suppression module to mask false identifications. Finally, the package outputs only the locations of the ships, which is transmitted to the ground. The proposed system reduces the effective volume of data to be transmitted and processed on ground and also significantly cuts down the turnaround time for achieving the end objective. The system is built on radiation hardened Field Programmable Gate Array (FPGA) devices to meet the various engineering constraints such as real-time performance, limited onboard power, radiation hardness, handling of multiple custom interfaces etc. The system is tested with one of the medium resolution Multispectral Visual and Near Infra-Red (MX-VNIR) sensor having a spatial resolution of around 50 m and swath of around 500 Kms, which would be flown with one of the upcoming satellites. The systems performance is also verified on ground with Indian Remote Sensing (IRS) Satellite’s Resourcesat’s Advanced Wide Field Sensor (AWiFS) data and the results are found to be quite encouraging as well as meeting the mission objectives.

Evaluation of Landsat-8 OLI and Sentinel-2 MSI images for estimating the ecological quality of port waters

ABSTRACT Recent research has demonstrated that the TRophic IndeX (TRIX) is informative on the ecological quality of Mediterranean port waters and can be estimated by the processing of multispectral observations collected in situ. The current study investigates the alternative use of multispectral data acquired by medium-high spatial resolution optical satellite sensors, i.e. the Landsat-8 (L8) Operational Land Imager (OLI) and the Sentinel-2 (S2) MultiSpectral Instrument (MSI). The study analyzes the impact of the main factors influencing TRIX estimation using datasets collected in several Mediterranean ports and particularly in those of Civitavecchia and Viareggio (Central Italy). The experimental results indicate that the spectral configurations of OLI and MSI have a marginal impact on TRIX estimation, while major effects are caused by the different spatial and temporal features of the two sensors. The enhanced spatial properties of MSI are important particularly in the smaller port of Viareggio; the higher acquisition frequency of this sensor brings significant advantages for operational monitoring applications. Overall, the accuracy of TRIX estimation based on in situ spectral measurements is reduced significantly by the use of OLI images and only marginally by that of MSI images. Both image types represent an advancement for the operational monitoring of port waters.

Ultrasensitive and high resolution mass sensor by photonic-molecule optomechanics with phonon pump

Whispering gallery mode cavities, due to their high quality factors, small mode volumes, and simple fabrications, have potential applications in photonic devices and ultrasensitive mass sensing. Cavity optomechanic systems based on whispering gallery mode cavities have draw tremendous attention in recent years due to the fact that they reveal and explore fundamental quantum physics and pave the way for potential applications of optomechanical devices. Unfortunately, whispering gallery mode based cavity optomechanics still lies in a single optical mode coupled to a single mechanical mode. Here, in this work, we propose an ultrasensitive and high resolution optical mass sensor by photonic-molecule optomechanics driven by two-tone field, where the optomechanical cavity driven by a weak coherent phonon pump is coupled to an auxiliary optical cavity. The auxiliary cavity plays the role of decreasing the mechanical damping rate while the weak coherent phonon pump to the mechanical mode will improve the intensity of the probe transmission spectrum, which together enhance the mass sensitivity. Compared with the optical mass sensor based on single-cavity optomechanics, the mass sensitivity presented here is improved significantly. We also investigate different parameter regimes which guide us to choose the optimal parameters. Finally, we illustrate the accurate measurement for the mass of nanoparticles, and the photonic-molecule optomechanics system will provide a new platform for exploring the on-chip applications in ultrahigh resolution sensing devices.

Geospatial Technology Methods for Carbon Stock Assessment: A Comprehensive Review

Carbon stock estimation is becoming an important component for fighting against global warming. Assessment of biomass can give an idea of the amount of CO2 that can be removed from the atmosphere by forests and other plantations. Geo-Spatial technologies, including Remote Sensing (RS) and Geographic Information Systems (GIS), offer the mean to enable rapid assessment of terrestrial biomass over large areas in a timely and cost-effective manner, allowing for the estimation of above and below ground biomass. Hence, the deployment of an integrating RS-GIS approach for precision carbon management is of high significance. This article provides a review of various RS and GIS techniques used in forest aboveground biomass mapping and monitoring as well as highpoints the associated challenges and opportunities. The review concluded that the use of RS and GIS in large-scale forest aboveground biomass assessment provides a sound alternative when compared to the use of conventional approaches. It was noted that the freely available moderate resolution optical sensors could be used reliably for estimating forest carbon stock. Furthermore, the integration of multi-sensor data in a GIS environment increased the accuracy of the estimation results. This study helps contributing to the topic in a way that it illustrates the growing developments using geospatial technologies by identifying most sensitive RS variables to measurable biophysical parameters. Furthermore, it demonstrates the usefulness of geospatial technologies for estimating terrestrial carbon sequestered. Finally, gaps, limitations and the need for further studies are underlined.

Integration of synthetic aperture radar and optical satellite data for corn biomass estimation

Efforts to use satellites to monitor the condition and productivity of crops, although extensive, can be challenging to operationalize at field scales in part due to low frequency revisit of higher resolution space-based sensors, in the context of an actively growing crop canopy. The presence of clouds and cloud shadows further impedes the exploitation of high resolution optical sensors for operational monitoring of crop development. The objective of this research was to present an option to facilitate greater temporal observing opportunities to monitor the accumulation of corn biomass, by integrating biomass products from Synthetic Aperture Radar (SAR) and optical satellite sensors. To accomplish this integration, a transfer function was developed using a Neural Network algorithm to relate estimated corn biomass from SAR to that estimated from optical data. With this approach, end users can exploit biomass products to monitor corn development, regardless of the source of satellite data.•The Water Cloud Model (WCM) was calibrated or parametrized for horizontal transmit and horizontal received (HH) and horizontal transmit and vertical received (HV) C-band SAR backscatter using a least square algorithm.•Biomass and volumetric soil moisture were estimated from dual-polarized RADARSAT-2 images without any ancillary input data.•A feed forward backpropagation Neural Network algorithm was trained as a transfer function between the biomass estimates from RADARSAT-2 and the biomass estimates from RapidEye.

Integration of ZiYuan-3 Multispectral and Stereo Data for Modeling Aboveground Biomass of Larch Plantations in North China

Data saturation in optical sensor data has long been recognized as a major factor that causes underestimation of aboveground biomass (AGB) for forest sites having high AGB, but there is a lack of suitable approaches to solve this problem. The objective of this research was to understand how incorporation of forest canopy features into high spatial resolution optical sensor data improves forest AGB estimation. Therefore, we explored the use of ZiYuan-3 (ZY-3) satellite imagery, including multispectral and stereo data, for AGB estimation of larch plantations in North China. The relative canopy height (RCH) image was calculated from the difference of digital surface model (DSM) data at leaf-on and leaf-off seasons, which were extracted from the ZY-3 stereo images. Image segmentation was conducted using eCognition on the basis of the fused ZY-3 multispectral and panchromatic data. Spectral bands, vegetation indices, textural images, and RCH-based variables based on this segment image were extracted. Linear regression was used to develop forest AGB estimation models, where the dependent variable was AGB from sample plots, and explanatory variables were from the aforementioned remote-sensing variables. The results indicated that incorporation of RCH-based variables and spectral data considerably improved AGB estimation performance when compared with the use of spectral data alone. The RCH-variable successfully reduced the data saturation problem. This research indicated that the combined use of RCH-variables and spectral data provided more accurate AGB estimation for larch plantations than the use of spectral data alone. Specifically, the root mean squared error (RMSE), relative RMSE, and mean absolute error values were 33.89 Mg/ha, 29.57%, and 30.68 Mg/ha, respectively, when using the spectral-only model, but they become 24.49 Mg/ha, 21.37%, and 20.37 Mg/ha, respectively, when using the combined model with RCH variables and spectral band. This proposed approach provides a new insight in reducing the data saturation problem.

Generation of Homogeneous VHR Time Series by Nonparametric Regression of Multisensor Bitemporal Images

The availability of multitemporal images acquired by several very high geometrical resolution (VHR) optical sensors makes it possible to build VHR image time series (TS) of images acquired over the same geographical area with a temporal resolution better than the one achievable when considering a single VHR sensor. However, such TS include images showing different characteristics from the geometrical, radiometrical, and spectral viewpoint. Thus, there is a need of methods for building homogeneous VHR optical TS when using multispectral multisensor images. By focusing on the spectral domain, we propose a method to transform a VHR image into the spectral domain of another image in the same multisensor TS but acquired by a different sensor. To this end, a prediction-based approach relying on a nonparametric regression method is employed to mitigate sensor-dependent spectral differences. The impact of possible changes occurred on the ground is mitigated by training the prediction model on unchanged samples, only. Experimental results obtained on VHR optical multisensor images confirm the effectiveness of the proposed approach.

Improving the AVHRR Long Term Data Record BRDF Correction

The Long Term Data Record (LTDR) project has the goal of developing a quality and consistent surface reflectance product from coarse resolution optical sensors. This paper focuses on the Advanced Very High Resolution Radiometer (AVHRR) part of the record, using the Moderate Resolution Imaging Spectrometer (MODIS) instrument as a reference. When a surface reflectance time series is acquired from satellites with variable observation geometry, the directional variation generates an apparent noise which can be corrected by modeling the bidirectional reflectance distribution function (BRDF). The VJB (Vermote, Justice and Bréon, 2009) method estimates a target’s BRDF shape using 5 years of observation and corrects for directional effects maintaining the high temporal resolution of the measurement using the instantaneous Normalized Difference Vegetation Index (NDVI). The method was originally established on MODIS data but its viability and optimization for AVHRR data have not been fully explored. In this study we analyze different approaches to find the most robust way of applying the VJB correction to AVHRR data, considering that high noise in the red band (B1) caused by atmospheric effect makes the VJB method unstable. Firstly, our results show that for coarse spatial resolution, where the vegetation dynamics of the target don’t change significantly, deriving BRDF parameters from 15+ years of observations reduces the average noise by up to 7% in the Near Infrared (NIR) band and 6% in the NDVI, in comparison to using 3-year windows. Secondly, we find that the VJB method can be modified for AVHRR data to improve the robustness of the correction parameters and decrease the noise by an extra 8% and 9% in the red and NIR bands with respect to using the classical VJB inversion. We do this by using the Stable method, which obtains the volumetric BRDF parameter (V) based on its NDVI dependency, and then obtains the geometric BRDF parameter (R) through the inversion of just one parameter.

Spectral Adjustment Model's Analysis and Application to Remote Sensing Data

Differences in the relative spectral response functions of sensors lead to data inconsistencies that should be harmonized before multisensor exploitation. In this paper, we use spectral libraries to simulate satellite data and build models to correct them. We then explore and compare different models for coarse and medium spatial resolution optical sensors, including moderate resolution imaging spectroradiometer, advanced very high resolution radiometer (AVHRR), visible infrared imaging radiometer suite, multispectral instrument aboard Sentinel-2, and Operational Land Imager aboard Landsat 8. We found that optimal correction of different bands depends on the model used. For the green and near infrared bands, a multilinear land cover dependent regression improves the accuracy by up to 80.9%. For the red band, a novel exponential dependence of the spectral band adjustment factor with the normalized difference vegetation index (NDVI) provides an accuracy improvement of up to 72.8%. The best way to correct the NDVI value is to use the corrected NIR and red bands using these models. We apply the proposed methods to 445 BELMANIP2 sites using AVHRR data from the long-term data record from 1982–2017. High NDVI pixels result in 30-year trends varying up to 0.06 when comparing uncorrected to spectrally adjusted NDVI. Further application of these methods to NASA's Harmonized Landsat and Sentinel 2 product shows that for the red band and NDVI, our proposed method provides improved accuracy (54.6% and 62.5%) over the linear spectral adjustment currently used.