Preamble

Remote ensing ffects nvariants urface tudies Hongliang ,2,3,* 1 LREIS, Institute Geographic Sciences Natural Resources Research, Chinese Academy Sciences, Beijing 100101, China

2 College

Resources Environment, University Chinese Academy Sciences, Beijing 100049, China

3 Jiangsu

Center Collaborative Innovation Geographical Information Resource Development Application, Nanjing, 210023, China Contribution Statement Hongliang FANG:

Conceptualization, Formal analysis, Methodology, Resources, Writing author:

Hongliang (E-mail:

Abstract

Objective intend provide meta-review various remote sensing effects invariants.

Methods

characterization, underlying principles, potential applications selected group remote sensing effects examined.

Results

suite effects addressed. remote sensing invariants analyzed.

Limitations Potential directions future studies discussed.

Conclusions

synthesis represents concerted effort advance theoretical understanding fundamental principles remote sensing science.

Keywords

emote sensing effects emote sensing invariants heoretical remote sensing emote sensing science

1. I

ntroduction Remote sensing obtains information about Earth distance without physical contact target. characteristics information sensor, whether near-surface, airborne, spaceborne, epend factors, particularly, reflectance emittance target, nature magnitude atmosphere, topography ground, geometry sun-target-sensor system.

Consequently, capability retrieve information determined these factors collectively referred remote sensing effects.

There commonly agreed definition remote sensing effect since effect itself broad term. generally defined factor phenomenon, events considered remote sensing processes. theory, difficult disentangle effects since closely related other. ever, there exist effects critical obtaining quality information widely studied.

These effects focus paper. addition remote sensing effects, there features remain essentially unchanged during remote sensing analysis. property commonly utilized parameter retrieval, where parameters invariant while deriving other variables. example seudo-invariant calibration sites (PICS) commonly radiometric calibration where radiation properties these sites assumed invariant time.

Remote sensing effects invariants exist through aspects remote sensing study, including atellite measured radiance, radiative transfer process, physical parameter retrieval, product validation applications.

Although studie covered various effects invariants individually, there currently comprehensive synthesis remote sensing effects invariants. objective paper provide meta-

analysis

various remote sensing effects invariants. study classifie synthetically analyze current progresses, challenges, future prospects study remote sensing effect invariants. ultimate enhance understanding these concept advance theoretical remote sensing studies.

Section provide overview selected remote sensing effects. intention present exhaustive description known effects comprehensive information, readers referred cited

references

effect. Section offer synthetic

analysis

remote sensing effects. Sections explores remote sensing invariants.

Section concludes paper.

2. Characterization

remote sensing effects

2.1. Remote

sensing effects various links large number remote sensing effects proposed investigated (Appendix different degrees completeness advancement. remote sensing process

treated chain events steps final output Remote sensing effects stinct roles throughout chain beginning electromagnetic source, solar radiation, thermal emission, active radiation (LiDAR radar). llumination effect indicates properties radiation distribution luminance whether directionally uniform varies weather conditions properties target, material composition, homogeneity, roughness, water content, salinity, temperature, texture, reflectance emittance, anisotropy properties central remote sensing signal processing.

Moreover, relative position motion state targets cause radiance differences different wavelengths. location, altitude, environmental conditions disease, draught, dust, impact remote sensing acquisition. field biophysical measurements, optical instruments, gital hemispherical photography (DHP), affected exposure setting, blooming vignetting effects, segment size, format, environmental contamination LAI-2200 standard field index measurement instrument, particularly susceptible effect LiDAR measurements, occlusion saturation effects considered Woody non-green element effect require attention measurement using LAI-2200 LiDAR. general, field measurements subject sampling effects, whether random, systematic, stratified.

Satellite remote sensing influenced effects, ensor spectral wavelength, spectral response, sensitivity, field-of-view, radiometric, spatial spectral resolutions, active passive observation modes, observation geometry, temporal variation, orbital shift, others.

Recent studies found products derived MODIS affected orbital drift, especially fraction absorbed photosynthetically active radiation FAPAR because variations solar illumination angle light conditions Remote sensing models simulate process atmosphere vegetation system Typically, radiation decomposed direct diffuse components. model account photon interaction atmospheric vegetation elements. understory

background

important vegetation remote sensing, especially sparse canopy. water surface, air-water interface effect considered simulati coupled water system Polarization effect explicitly orporated polariz models factors affect remote sensing storage, processing, transmission. compression prior transmission storage reduce quality critical details. ransmission delays partial communication issues

result

incomplete outdated datasets. image classification, different classifiers introduce varying uncertainties final product interpretation inherently involves artificial effect cause systematic errors.

Scale differences, temporal coherence, spectral consistency disparities between remote sensing products reference introduce errors product evaluation applications.

Connections among remote sensing effects Remote sensing effects isolated phenomena rather interconnected other. effects exhibit similarities equivalences studied collectively, while others demonstrate opposing characteristics.

These relationships broadly categorized follows. Generic integrated effects Generic effects those should considered across multiple aspects remote sensing scaling effect example exists field measurement satellite observation product generation evaluation, applications.

Another example temporal effect, which affects instrument target observer environment conditions human factor permeates nearly remote sensing process contrast generic effects, certain effects particular importan specific applications These effects independent, texture effect, which commonly image classification. clumping effect while predominantly significant local scale contribute landscape regional scale effects integration several sub-effects.

Topographic effects contain elevation, slope, aspect effect coupled directional effects affecting surface reflectance.

Spatial temporal effects usually analyzed together spatio-temporal effects. latitude, longitude, topographic, elevation effects subsumed spatial effects. djacency effect combination atmospheric surface effects, primary other secondary.

Directional effect category geometric effect which affected Earth shape rotation, sun-target-sensor configurations terrain influence geo-reference projection.

Temporal effects usually combined variations spectral, angular, textural characteristics. mountainous areas, topographic effects incorporated atmospheric correction surface reflectance estimation cover classification [11-13] Equivalent, facilitating neutralizing effects effects literally equivalent. example, heterogeneity effect mixing effect often interchangeable different field phenology seasonal effect similar other subcategories temporal effects. irectional effects closely related angular effect, glint effect specular effect, non-Lambertian effect, hotspot darkspot effect shading effect others. effects mutually reinforce other remote sensing while others compensate other. biochemical components (e.g., chlorophyll content) canopy structural properties (e.g., jointly influence canopy reflectance, which makes difficult decouple parameter retrieval.

Similarly, canopy clumping, woody components non-green

foliage collectively complicate retrieval optical remote sensing. practice, clumping woody effects compensated forest estimation [10,14] Beneficial detrimental effects Depending purpose, remote sensing effects either beneficial detrimental.

Beneficial effects helpful user; example, temporal effect beneficial series

analysis

thematic classification. effects beneficial utilized remote sensing studies. usefulness effect depends purpose application.

Traditional visual interpretation digital classification capitalized spatial patterns, spectral characteristics, temporal variations, while emporal effects commonly utilized global cover classification Detrimental effects introduce complications user. coupled atmosphere surface system, atmospheric influence hinders surface detection versa. couple soil- vegetation system, detecting needs consider other effect However, bright

background

effect enhance vegetation detection quantification.

Thermally emitted radiance surface depends surface temperature emissivity; estimating either needs account mutual dependence

3. Overview

selected remote sensing effects section focuses selected group effects significant attention surface remote sensing.

These effects well-defined extensively studied literature.

Their characteristics summarized below examples references.

Atmospheric effect Atmosphere effect joint

result

kinds atmospheric components, atmospheric molecules, aerosol water vapor, ozone, methane, carbon monoxide, nitrous oxide, carbon dioxide atmospheric effects include scattering, absorption, cloud cover, aerosol, water vapor, refraction, thermal effects.

Atmospher effects modify signals sensors satellites other platforms, making challenging interpret accurately without compensati them.

Atmospheric tions impact optical instruments canopy biophysical structural measurements, LAI-2200, AccuPAR.

Therefore, diffuse conditions generally recommended optimal performance, particularly forests, avoid impact direct irradiance [17-19] Among these instruments, found robust sensitivity illumination conditions

Atmospheric correction technique remove reduce atmospheric distortions, surface parameters accurately retrieved Atmospheric corrections enhance image quality improve classification accuracy umerous studies reviewed evaluated tmospheric correction

methods

[23-25] Aerosols water vapor which exhibit greate spatial temporal varia bility other atmospheric components, introduce significant uncertainties atmospheric correction. erefore accurately characteri these components essential effective atmospheric correction.

Various techniques, including physical modeling learning

methods

proposed provide processing solutions kinds illumination conditions.

Commonly atmospheric correction algorithms include ATmospheric CORrection (ATCOR3) Landsat Surface Reflectance (LaSRC) Sen2Cor implemented SeNtinel Application Platform (Sen2Cor-SNAP) Atmospheric models, MODTRAN commonly simulate compensate effects scattering, absorption, other atmospheric phenomena.

Remote sensing contaminated atmospheric effects processed using variou filtering approaches [33-35] Combining multiple bands using images acquired different times mitigate atmospheric influence.

Specialized vegetation indices atmospherically resistant vegetation index (ARVI) infrared simple ratio developed minimize atmospheric impac Notably, tmospheric correction necessary classification change detection applications training target maintain consistent relative scale Validati atmospheric correction

results

crucial ensure processed remote sensing accurately represent surface reflectance radiance.

Field-measured surface reflectan those SpecNet HyperNav critical validation corrected data. there concurrent field measurements, atmospheric parameters derived satellite images reconstruct atmospheric conditions verify corrections [41,42] combining visual inspection, quantitative

analysis

cross-validation, ensure atmospheric correction improve accuracy downstream applications cover classification environmental monitoring.

Background

effect

Background

effects refer mixture target

background

information sensor ackground effect primarily arise soil, snow, understory, water background. forest environment

background

refer materials below canopy understory plants litter, grass lichen soil, snow, water, their mixtures [43,44] Typically forests, lichen layer cover ground surface beneath grass Similarly, agricultural field layer commonly exists beneath canopy.

ackground materials influence vegetation canopy reflectance consequently affec canopy parameter retrieval.

Combining information different bands eliminate

background

effects significantly enhance canopy information extraction.

Several specifically developed purpose including soil-adjusted vegetation index (SAVI) modified soil-adjusted vegetation index (MSAVI) reduced simple ratio (RSR) normalized difference phenology index (NDPI) orest

background

information retrieved emote sensing technique especially multiangle sensors [45,49,50] canopy reflectance models,

background

contributions represented different schemes [51-53] example, MODIS algorithm patterns effective ground reflectance parameterize contribution sub-canopy surface (soil and/or understory) practice,

background

characteristics medium density forests usually considered similar within geographical although local ations occur between adjacent stands differ densities overstory coverage (e.g., 70%),

background

little effect canopy reflectance albedo

background

reflectance estimated remote sensing retrieve overstory parameters However,

background

information retrieval forests partly influenced non-green materials canopy (trunks branches) dense forest (e.g., there ability retrieve

background

reflectivity. Furthermore, eparating different understory components still difficult; model-retrieved

background

reflectance represent ground conditions effective value model inversion [59,60] attention necessary complicated backgrounds [61,62] However, challenging snow-covered using optical satellite sensors, particularly during snowmelt normalized difference index (NDSI) identify whether

background

present other hand, cover benefits wintertime forest estimation airborne image providing uniformly bright

background

hemispherical image

analysis

[62,65] Clumping effect Canopy clumping effect characterizes spatial distribution leaves needles within vegetation canop which critical determining transmi ssion interception light precipitatio [10,66,67] Canopy clumping effect usually quantified using canopy clumping index (CI), defined ratio effective (LAIe) ndscape ecolo aggregation index measure spatial aggregation ecological adjacencies class patches [68-70] estimated field using direct, indirect optical, allometric

methods

Direct

methods

derive separately estimating Indirect

methods

estimate through

analysis

canopy fractions while allometric

methods

relationships other biophysical

parameter assive optical sensors active LiDAR systems, including errestrial, airborne, spaceborne platforms, successfully applied estimat following similar rinciples field measurements [71,72] Current global products generated through empirical relationship normalized difference hotspot darkspot (NDHD) index derived POLDER, MODIS, [73-75] lumping effect scale-dependent tends tensify higher spatial resolution [76,77] broadleaf forests foliage clumping patterns within-crown between-crown scales coniferous forests, foliage clumping described shoot, branch, crown, landscape levels Clumping effect generally increases (decreasing value) elevation angle canopy height, primarily because larger upper canopy lower canopy.

Seasonally, canopy clumping pronounced during growing season compared early later rowing periods clumping effect significantly influences field measurements, remote sensing modeling parameter retrieval should considered canopy reflectance surface model critical partitioning total sunlit shaded components estimation gross primary production (GPP) solar-induced fluorescence (SIF) surface evaporation [80,81] Seasonal clumping variations explore their effects canopy radiation absorption dynamics [82,83] However, interannual clumping variability fully considered current surface models climate change studies.

Future studies should focus implementing automated wireless measurement techniques develop advanced remote sensing estimation methods, enhancing fundamental understanding clumping characteristics, improving clumping parameterization surface models.

Directional effect Directional effect represents variation object properties angles, variations solar illumination, surface characteristics observed radiances.

Surface irectional effect trinsic surface property crucial modeling estimation surface parameters.

Surface directional property usually characterized bidirectional reflectance factor (BRF) bidirectional reflectance distribution function (BRDF) which measured using various instruments field vegetation canopy, directional distribution represents another crucial directional property obtained through photographic (e.g., radiometric (e.g.,

LAI-2200) field measurement techniques [85] .

Satellite observations acquire off-nadir angles inherently incorporate directional effects surface reflectance measurements surfaces constant properties, apparent reflectance varies solar zenith angle Correcting directional effects particularly important time-series

analysis

generation long-term records [87,88] influence irectional

effect propagate surface reflectance therefore impacts estimation canopy biophysical variables using approach. limit impact directional effect common option normalized difference vegetation index (NDVI enhanced vegetation index (EVI).

However, still contain signi directional signatures Directional effects effectively reduced angular normalization models Various canopy reflectance models developed characterize these directional variations [52,91,92] MODIS processing chain, instance, employs kernel-driven model produce nadir BRDF-adjusted reflectance (NBAR) products [93,94] Additional metrics anisotropic factor anisotropy index developed quantify surface reflectance anisotropy. onsideration directional effects important trong non-Lambertian scattering properties vegetation surfaces directional influence model parameters canopy reflectance Multi-angle observations generally improve retrieval accuracy compared single-angle measurements [97,98] demonstrated MODIS product derived directional reflectance [54,99] However, magnitude angular effects tends decrease increasing values [100] researchers suggested using angularly normalized direction based indices estimation [101-103] Heterogeneity effect Heterogeneity effect refer different objects study target, example, mixed pixel surface classification.

Surface heterogeneities associated horizontal vertical arrangemen target components.

Therefore, comprehensive characterization effect needs account horizontal vertical dimensions.

Surface heterogeneity effect scale- dependent directly related resolution sensor. greater spatial detail registered image, greater sensitivity detecting internal variations category contained under larger pixel [104] Vegetation heterogeneity determined horizontal vertical irection horizontal direction, surface mixture vegetation expressed fractional vegetation cover which usually determined along transects field measurements vertical direction, heterogeneity stems variations biophysical biochemical properties throughout canopy profile. oliage density profiles measured various vertical sampling

methods

[105] LiDAR radar sensors particularly effective capturing three- dimensional heterogeneity patterns [106] horizontal vertical heterogeneity characteristics incorporated canopy models [107-109] Multiple approaches exist quantifying spatial heterogeneity satellite footprint scales [110] Field-based

methods

include spatial variogram

analysis

assessing autocorrelation patterns [93,111]

High-resolution imagery combined pixel unmixing techniques mitigate heterogeneity effects decomposing mixed pixels constituent endmembers respective abundance fractions.

However, increase spatial resolution always improve discrimination features internal heterogeneity within categories increase [104,112] Since greater heterogeneity means greater mixing similar classes greater confusions, increase spatial resolution complicate digital classification.

Surface heterogeneity significantly impacts multiple aspects remote sensing including field sampling, radiometric calibration, surface classification, remote sensing modeling retrieval representative example cover classification, where selection appropriate spatial resolution depends specific research objectives. regional lobal agricultural monitoring, moderate spatial resolution sensors (e.g., MODIS) provide optimal balance between volume temporal coverage [113] Conversely, highly fragmented resolution sensors Sentinel- Landsat prove insufficient meter-resolution necessary Regarding visual analysis, higher spatial resolution generally enables accurate interpretation imagery [114] Saturation effect aturation effect refers phenomenon where sensor readings reach their maximum detection limit longer accurately represent further increas signal intensity.

Saturation effect occurs estimation canopy properties, e.g., spectral data. vegetation develops, indices spectral measurements often sensitivity canopy changes saturate medium-to-high values during growth stages [115] demonstrates earlier saturation greater light attenuation visible spectrum caused scattering absorption processes [91,116] aturation threshold usually defined point where reaches 90-95% maximum potential value [117,118] field, usually estimated canopy fraction following Beer-Lambert [119,120] where canopy fraction projection function canopy zenith angle respectively shows follows sympotot relationship canopy fraction would saturate values Documented saturation thresholds vegetation type: approximately forests [121, paddy [123] giant [124] saturation effect persists applied derive LiDAR

Seasonal variations saturation influenced canopy clumping dynamics, particularly during growing periods addition saturation effects similarly affect estimation chlorophyll content [126] canopy water content [127] canopy photosynthesis above ground biomass radiation intensity photosynthesis increases rapidly increase incident radiation; however, increase becomes slower radiation intensity high, i.e., photosynthesis tends saturate radiation intensity [128, aturation effect adversely impacts interpretation

analysis

causing inconsistent spectral responses [130] reduce saturation effect, e.g., Dynamic Range Vegetation Index (WDRVI) retriev

methods

explored [131-133] red-edge channels useful improve estimation; owever, channels sensitive chlorophyll content problem estimation [134-136] Incorporating thermal infrared (TIR) bands [137] texture information [138, LiDAR technology [140, shown partly alleviate saturation issues dense canopies.

Additionally, non-parametric machine-learning algorithms, e.g., suport vector machine [142] Gaussian processes regression (GPR) [143] shown potential reducing saturation effects.

However, remains difficult solve intrinsic problem.

Scaling effect emote sensing often collected platforms operating different spatial scales, e.g., ield, airborne, spaceborne sensors, thus, characteristics change differences spatial resolution [144] scaling effect needs considered surface characterization, modeling, product generation validation.

Canopy reflectance surface models their hierarchical forms, integrate different scales simulate small-scale features contribute larger-scale processes [145] validation, direct comparison between sparsely sampled field measurements moderate-resolution satellite products suffer problem scale-mismatch [146] address issue, recommended scale estimates derived resolution imagery moderate resolution ixels ereby bridging scale between ground measurement satellite products [146, magnitude scaling increases model nonlinearity surface heterogeneity [148-151] Raffy [148] estimated scali amplitude transfer function conve Islam [150] quantified scaling errors comparing upscaling approaches: calculating resolution before upscaling, upscaling reflectance first calculating coarse resolution.

Garrigues [152] proposed

method

estimate scaling based degree transfer function nonlinearity intra-pixel spatial heterogeneity. understand influence scaling effects, various techniques developed Remote sensing classification different spatial scales investigated using etrics accuracy, Kappa

coefficient score assess scale nfluence classification

results

Machine learning techniques, onvolutional eural etworks andom orests upport ector achines trained various resolutions analyze scale effects. avelet transforms ractal model found useful examining relationship between spatial scale surface complexity [153, Geostatistical tools riging model spatial continuity different scales, predict intermediate spatial scales understand scale affects spatial variability [155] Space nalysis chniques Gaussian pyramids multi-resolution image pyramids model spatial features multiple scales understand image features (e.g., edges, textures, shapes) evolve spatial resolution changes Scalable models, allometric methods, applied different scales above ground biomass estimation [156] Temporal effect Temporal effect broadly refers changes variation Earth surface caused natural human factors.

Temporal effects fundamental studying seasonal long-term vegetation dynamics field measurements, temporal variations monitor through continuous automat measurements surface parameters.

Satellite emote sensing provides instantaneous measurement during overpass which integrated daily measurements further combined generate continuous products.

Temporal effects limit effectiveness generality empirical

methods

estimat canopy biophysical variables studies found improved relationships oning growing season phases separated maximum [157] while others reported significant improvement [158] relationships between biophysical variables should carefully considered during senescence stage, especially estimation emporal variation surface considered remote sensing models.

Several researchers suggested temporally resistant bands [160] ncorporate temporal factor statistical models [161] temporal information model considered enhance expla natory power.

Rebelo [162] proposed temporal kernel-based model change detection. isotropic model varies cubic function time, while shape parameters remain constant model essentially combine directional temporal effects improve prediction power series

analysis

widely examining analyzing temporal effects [163, process includes change detection trend

analysis

different temporal scales (daily, monthly, seasonally, yearly, long-term). series

analysis

indered temporal smooth harmonization needed reduce noise improve accuracy purpose harmonization address sensor degradation individual sensor cross-sensor differences multiple sensors [165] temporal filtering, interpolation, construction

ntegration techniques proposed btain temporally continuous spatial resolution satellite image Multi-sensor fusion algorithms developed increase product temporal resolution accurac Spatial Temporal Adaptive Reflectance Fusion Model (STARFM) provides obtain resolution product blending MODIS Landsat common acquisition [166] Essentially, STARFM provides fusion scaling temporal effects.

Temporal mismatch first issues considered temporal analysis. nterpolation smoothing alter original observations bring artifacts amplitude frequency Sensor degradation affect product quality compli series

analysis

[167] Temporal stability important metric series analysis. lobal Observation System defined product stability maximum acceptable change systematic error decadal timescales [168] practice, other forms stability metrics, coefficient variation [169] change accuracy [170] yearly drift [171] used.

Topographic effect Topographic effect refers influence caused variations elevation, slope, aspect, terrain, roughness openness Earth surface.

Local topography significantly modulates surface illumination conditions surface BRDF, which affects field measurements, cover classification, surface modeling parameter retrieval, especially resolution (<100 remote [172] Surface microtopography related surface roughness which important interpretation remote sensing imagery [173, Ground survey remote sensing methods, LiDAR, stereo satellite imagery aerial photogrammetry, common employed determine surface topography [175, lobal topographic available digital elevation model those Shuttle Radar Topographic Mission SRTM) [177] ASTER Global Digital Elevation Model (GDEM).

However, retrieving accurate topograph information dense forest environment remains challenging [178] Topographic correction necessary field measurements remote sensing acquired topographically complicated areas topographic correction create harmonized radiometric stable remote sensing Numerous topographic correction

methods

developed roughly classified physical, semi-empirical, empirical models [180] simple mitigate topographic effect build different models different slope, aspect, elevation classes [181] thorough create inherently robust different kinds topographic conditions [182] Different topographic correction

methods

evaluated diverse conflicting

conclusions

reported [180, diverse

conclusions

could attributed limited ground truth evaluation strategies largely depend selected images

benefits topographic correction shown application cover classification biophysical parameter retrieval airborne LiDAR [185] example, [186] proposed refined albedo estimation algorithm mountain areas tegra approach accounts various terrain conditions (e.g. slope, aspect, elevation, vegetation structure).

Carmon [187] reported incorporating dynamic topography directly joint surface atmospheric model during retrieval process could reduce errors retrieved surface reflectance.

Topographic correction converts physically observed signal hypothetical horizontal plane.

Since hypothetical plane physically exist, impossible validate correction effect iveness absence reference data.

Although simulation studies offer insights inevitably introduce artifacts.

Moreover, current correction

methods

consider spectral wavelength variations; therefore, rther correction

methods

developed different wavelengths. Human effect nfluence human factors permeates nearly spects remote sensing including

experiment

design implementation collection analysis, research documentation, policy recommendation. utility remote sensing fundamentally depend expertise knowledge human users. interpretation relies imagery characteristics interpreter knowledge, skill, experience.

Although automatic image processing techniques significantly improved recent years, relying computer remains inadequate remote sensing

analysis

[188] Serious errors occur because insufficient human interac artificial anomalie Therefore, remote sensing products China National Use/cover Database [189] GlobeLand30 [190] adopted hybrid approaches incorporating human interpretations quality assurance Human effects individual, organization, societal levels. esearch organizations termine their remote sensing objectives based organization structure available resources, management approaches Professional communit committee within remote sensing field establish standard rotocol requirements remote sensing activities. development lassification algorithms, classification systems, proliferation cover products largely driven various national international initiatives. uman-induced cover change environmental climate change crucial research themes remote sensing. olitical social factors critical successful development management remote sensing programs, those sustainable development goals [191] Sustained overnment funding essential maintain space programs supporting remote sensing research

policy access Landsat since example decisions dramatically expand application [192]

4. Perspectives

remote sensing effect studies Remote sensing effects suppressed using various forms remote sensing effects, illumination, atmospheric, cloud shadow, topographic effects, similar influence across multi spectral bands reduced using ratio vegetation index (RVI) NDVI. example, partially cancel bidirectional effect observed radiances [193] while difference vegetation index (DVI) helps reduce scale effect [194] However, effectiveness generality empirical mitigate remote sensing effects constrained factors vegetation type, sun-surface-sensor geometry, chlorophyll content,

background

reflectance, atmospheric conditions example, sensitive

background

effect showing positive biase soils reduce influence, modified proposed, soil-adjusted vegetation index (SAVI) transformed (TSAVI) [195] modified (MSAVI) Kaufman developed specifically correct atmospheric effect particularly aerosol vegetation remote sensing. pectral signal recorded pixel comes surrounding areas consequence multiple effects instrument optics, atmospheric effects, image resampling.

These effects characterized using point spread function which quantifies sensor response point signals [196, Spatiotemporal fusion

methods

address problem blend temporally sparse fine-resolution images temporally dense coarse-resolution image These

methods

leverage spectral, spatial, temporal properties accomplish verse fusion tasks under different environmental conditions using different sensor [198] general solution orporate remote sensing effects directly physical canopy reflectance models.

Essentially, canopy reflectance models integrate various effects induced background, leaf, canopy, observation geometry, environment. example, Verhoef [199] developed simple model simulate effects canopy structure, surface heterogeneity,

background

canopy reflectance. Canopy reflectance models further integrated atmospheric models simulate satellite observations [200] [201] systematically quantified uncertaint sources daily VIIRS nighttime light radiance product found uncertainty dominated angular atmospheric effects.

Specialized software developed their subsequent research correct cloud, atmosphere, terrain, snow, lunar, stray light effects Day/Night (DNB) radiances [201, potential future study angular, atmosphere, adjacency, scaling, saturation, temporal effects could incorporated surface reflectance

modeling, e.g., kernel-based model similar Rebelo [162] models could evaluated using field actual images.

Several unification schemes various degrees complexity integration developed surface models. coupled surface atmosphere models attempt integrate different kinds effects sophisticated manner. formulation surface processes needs carefully consider effects surface heterogeneity, influence surface processes planetary boundary layer stability moist convection, large-scale observational parameter specifications [203] Carmon [187, demonstrated scheme incorporate topography joint surface atmospheric modeling. joint modeling scheme improved atmospher surface propert inversion provided accurate surface reflectance estimates. above sections focused fundamental effects surface remote sensing. important indirect effects addressed e.g., ecological, geographic, hydrological, greenhouse, fertilization effects because their tangential relationship direct remote sensing observations important effect ectral variability caused different sensors. ensure spectral stability between sensors, cross-sensor correction spectral normalization performed reflectance comparability continuity large-scale vegetation monitoring [205-207] Other minor effects, lateral radiation effect, canopy [208] Further studies these effects performed broader context

5. Remote

sensing invariants Remote sensing invariants categorized primary types: spectral, spatial, temporal, directional, thematical invariants, different dimensions remote sensing data.

These invariants relate either field measurement processes external environmental conditions driving factors.

Spectral invariants Spectral invarian refer properties objects similar reflectance, emittance, transmittance, absorptance different wavelengths. field reflectance measurement white reference panel assum constant reflectance (100%) shortwave range. canopy modeling, leaves

background

considered black absorb incident radiation wavelengths. polarization considered modeling, means polarization property assumed spectrally invariant.

Spectral invariance refers static relationships between different bands. relationship useful restoration.

MODIS aerosol algorithms traditionally assumed

fixed ratio between surface reflectance visible channels [209-211] Similarly, reflectance blue, green bands expressed using various linear relationships reflectance [212] decades, spectral invariant theory gained increasing attention canopy reflectance modeling Spectral invariants theory represent canopy variables remain constant different wavelengths, fraction, canopy interceptance, photon escape recollision probabilities [215-217] invariants determined field measurements remote sensing studies based empirical relationship other biophysical biochemical variables, spectral scale transformations, direct relationship other structural parameters different levels. provides simple canopy spectral modeling biophysical biochemical parameter retrieval Spatial scale invariants scale invarian refers properties remain consistent despite location scale changes geography, closer areas similar another those further [218] spatial similarity property frequently restoration missing values, e.g., using neighboring pixel value estimation remote sensing variables, common procedure establish relationship between canopy biophysical biochemical variable representative sites. relationship applied larger predict these variables remote sensing data. process, empirical relationship derived locally assumed invariant large [219] Scale invariants represent those parameters whose characteristics change spatial scales [220] value parameter independent scale sampling, parameter considered scale-invariant, vegetation coverage height. these parameters, spatial scale included parameter definition variation spatial precision taken account sampling process.

Spatial invariance exists vertical direction. canopy simulation models, one-layer turbid-medium model assume properties invariant within layer, whereas models consider vertical variation properties [221, Similarly, surface models, one-layer model assumes constant vertical canopy profile whereas multi-layer models consider vertical canopy microclimate variations [223, Several indices scale-invariants remote sensing processing modeling Scale invariant features widely applied remote sensing image matching handle geometric distortions [225] Scale-invariant feature transform (SIFT) algorithms [226] extract distinct points image, which remain invariant affine transformations illumination changes. ractional dimension quantify self-organizing propert certain feature essential

nderstanding scaling photosynthetic processes individual canopy levels [108] Temporal invariants Temporal invariants, invariants, refer features patterns change specific periods. frame could hour, week, month, year, longer Remote sensing sensors often assumed stable within given frame trend analysis.

However, since sensor degrade time, radiometric matching between different dates performed based series control pixels assumed invariant through time. should noted temporal window expands probability feature variation increase. instrument calibration, cloud detection atmospheric correction, surface properties considered stable specific window feature allows better classification interpretation surface materials. standard MODIS algorithm, eight biome types priori information constrain structural optical parameter vegetation [228] comparison model simulation physical reality environment properties commonly constant space [229] surface model simulations, onstant [230] index (SAI) values [231, used. spatially uniform, spectrally stable temporally invariant location widely sensor calibration radiometric correction.

These sites support simulations satellite sensor measurements ntegrat platforms contribute satellite program continuity [233] Directional invariants Directional invariance relates properties remain stable under different observation solar angles. irectional invariance essential texture analysis, pattern recognition, feature extraction extract features sensitive these variations [138, Directional invariance common assumption quantitative remote sensing studies. typical example Lambertian assumption white reference panel field reflectance measurement Likewise, atmosphere ground interaction, surface usually assumed Lambertian. atmospheric correction, solar illumination decomposed direct isotropic diffuse radiation Satellites geosynchronous orbit observe areas Earth constant angles. kernel-driven model isotropic kernel represent nadir-view nadir-sun reflectance proper model, angularly dependent variables normalized obtain angular invariant measure albedo.

Various shape indices, structural scattering index (SSI) [234] anisotropic index (AFX) [235] Hotspot Dark-spot index (HDS) [102] proposed describe relationship between different directions.

Vermote [236] suggested yearly shape variations limited linked NDVI.

Shuai [237] assumed surface shape time-invariant. Rotational invariance another directional invariance property essential object detection, classification, change detection [238, Rotational invariance focuses ensuring object fundamental properties remain consistent despite changes orientation.

Thematical invariants Thematical invariants refer spectral reflectance, shape remain istent different surface types, atmospheric conditions, vegetation properties.

Landsat estimate global reflectance, assumed shape ground objects constant similar cover types [237] [240] proposed modified aerosol vegetation index (AFRI) which affected aerosol presence.

Fernandes reported robust atmospher species variability within forests offers better estimation compared Several studies suggested reflectance unaffected chlorophyll variations estimation densely vegetated areas Further investigations found normalized difference red-edge index (NDRE 710~780 insensitive chlorophyll concentration canopy clumping estimation [134] minimize variable

background

effect, different kinds developed, SAVI, (section These considered invariant

background

variation. Carmon [187] proposed unified topographic atmospheric correction approach obtained terrain invariant reflectance estimates.

Synthesis remote sensing invariants related remote sensing effects. feature affected effect, considered invariant specific effect.

Literally, temporal invariants considered unaffected temporal effects. object exhibit invarian multiple dimensions simultaneously example, surface radiance remain invariant spectral, spatial, temporal, directional dimensions. geometric pattern roads buildings remain invariant different viewing angles, observation atmospheric conditions.

Other remote sensing invariants explored. example relationship invariant, which represents constant correlations remote sensing analysi example, phenology study, onset greenness usually defined maximum during spring growth [243-245] cause threshold stable consistent across different ecosystems.

Remote sensing invariance features critical remote sensing modeling, parameter estimation, product generation validation, surface Earth system models.

Certainly, these invariants maintain relative validity under specific assumption understanding invariant properties,

researchers focus other changing properties, cover environmental conditions.

Remote sensing invariants disrupted external factors environmental change thus, should tored constantly.

6. Conclusion

paper provides synthetic overview various effects invariants surface remote sensing.

Remote sensing effects invariants fundamental remote sensing science involve different factors atmosphere, surface, water, instrument, human Remote sensing effects invariants crucial modeling parameter retrieval, feature interpretation, various applications. provide framework remote sensing systems,

method

ologie algorithms, products, applications. Significant esearch effort carried better understanding, quanti ication, mitigation adaptation different remote sensing effects. unification multiple effects further pursued. concepts effects invariants intertwined. description invariants description invariant effects. quantification effects requires determination invariants.

Thus, identification invariants entree identification effects.

Studying remote sensing effects invariants provides greatest potential advancement remote sensing science.

Continued efforts necessary future quantification, evaluation, validation effects invariants.

Remote sensing effects invariants involve broad diversified disciplines effects invariants review complete. presented framework offers perspective understanding remote sensing should stimulate aborative study theoretical remote sensing.

ACKNOWLEDGEMENTS study partly supported National Research Development Program China (2023YFF1303903, 2024YFF1308102) National Natural Science Foundation China (42471398) during final writing manuscript. topic first presented National Academic Forum Quantitative Remote Sensing (Changchun, Jilin, China, 2025).

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Appendix

Remote sensing effects graduate students Every spring 2025, lecturing class named Geo-analysis Remote Sensing first-year graduate students College Resources Environment, University Chinese Academy Sciences (UCAS). course given lecturers different specialties. topic about vegetation remote sensing. graduate students different backgrounds, taken introductory remote sensing classes. class, first examples common remote sensing effects, atmospheric effects, directional effects, scaling effects asked students other remote sensing effects think students feedback phenomenal. 2025, different effects submitted seatwork shown brevity purpose) proposed effects purged because repetition irrelevance.

Table lists remote sensing effects forward seatwork. table shows important remote sensing effects young graduate students.

Table remote sensing effects submitted graduate seatwork (2022-2025).

BRDF: bidirectional reflectance distribution function; remote sensing.

Spring, Spring, Spring, Spring, emporal tmosphere djacency ngular ngular tmosphere otspot emporal pixel eat-island emporal otspot ngular opography tmosphere emporal patial djacency opography opography emperature osphere patial olarization patial djacency djacency pectral eat-island pectral olarization oppler eometric distortion adiation pectral adiation pectral variability istance olarization -island cular reflectance iewing oppler hermal infrared henology opography pixel otspot island ropagation atching cology eflection adiance ultispectral ed-edge irectional oppler ngular eflection henology ulti-path reenhouse coustic eometry pectral confusion ed-edge nstantaneous temperature cattering cattering

Tables Table remote sensing effects submitted graduate seatwork (2022-2025).

BRDF: bidirectional reflectance distribution function; remote sensing.

Spring, Spring, Spring, Spring, emporal tmosphere djacency ngular ngular tmosphere otspot emporal pixel eat-island emporal otspot ngular opography tmosphere emporal patial djacency opography opography emperature osphere patial olarization patial djacency djacency pectral eat-island pectral olarization oppler eometric distortion adiation pectral adiation pectral variability istance olarization -island cular reflectance iewing oppler hermal infrared henology opography pixel otspot island ropagation atching cology eflection adiance ultispectral ed-edge irectional oppler ngular eflection henology ulti-path reenhouse coustic eometry pectral confusion ed-edge nstantaneous temperature cattering cattering