Cross-calibration of MODIS with ETM+ and ALI sensors for long-term monitoring of land surface processes

Carbon dioxide (CO2)&#160;is an important greenhouse gas, and it accounts for about 20% of the present-day anthropogenic greenhouse effect. Atmospheric CO2 is cycled between the terrestrial biosphere and the atmosphere through various land-surface processes and thus links the atmosphere and terrestrial biosphere through positive and negative feedback. Since multiple trace gas elements are linked by common biogeochemical processes, multi-species analysis is useful for reinforcing our understanding and can help in partitioning CO2 fluxes. For example,&#160;in the northern hemisphere, CO2 has a distinct seasonal cycle mainly regulated by plant photosynthesis and respiration and it&#160;has a distinct negative correlation with the&#160;seasonal cycle of the &#948;13C isotope of CO2,&#160;due to a stronger isotopic fractionation&#160;associated with terrestrial photosynthesis.&#160;Therefore, multi-species flask-data measurements are useful for the long-term analysis of various green-house gases. Here we try to&#160;infer the complex interaction between the atmosphere and the terrestrial biosphere by&#160;multi-species analysis using&#160;atmospheric flask measurement data from different NOAA flask measurement sites across the northern hemisphere.This study focuses on the long-term changes in the seasonal cycle of CO2 over the northern hemisphere and tries to attribute the observed changes to driving land-surface processes through a combined analysis of the &#948;13C seasonal cycle. For this we generate metrics of different parameters of the CO2&#160;and &#948;13C&#160;seasonal cycle like the seasonal cycle amplitude given by the peak-to-peak difference of the cycle (indicative of the amount of CO2 taken up by terrestrial uptake),&#160; the intensity of plant productivity inferred from the slope of the seasonal cycle during the growing season , length of growing season and the start of the growing season. We analyze the inter-relation between these metrics and how they change across latitude and over time.&#160;We hypothesize that the CO2 seasonal cycle amplitude is controlled both by the intensity of plant productivity and period of the active growing season and that the timing of the growing season can affect the intensity of plant productivity. We then quantify these relationships, including their variation over time and latitudes and describe the effects of an earlier start of the growing season on the intensity of plant productivity and the CO2&#160;uptake by plants.

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OLCI A/B Tandem Phase Analysis, Part 3: Post-Tandem Monitoring of Cross-Calibration from Statistics of Deep Convective Clouds Observations

Remote Sensing ◽

10.3390/rs12183105 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3105

Author(s):

Nicolas Lamquin ◽

Ludovic Bourg ◽

Sébastien Clerc ◽

Craig Donlon

Keyword(s):

Phase Analysis ◽

Absolute Calibration ◽

Inflexion Point ◽

Convective Clouds ◽

Deep Convective Clouds ◽

Long Term Monitoring ◽

The Cross ◽

Cross Calibration ◽

Term Monitoring

This study is a follow-up of a full methodology for the homogenisation and harmonisation of the two Ocean and Land Colour Instrument (OLCI) payloads based on the OLCI-A/OLCI-B tandem phase analysis. This analysis provided cross-calibration factors between the two instruments with a very high precision, providing a ‘truth’ from the direct comparison of simultaneous and collocated acquisitions. The long-term monitoring of such cross-calibration is a prerequisite for an operational application of sensors harmonisation along the mission lifetime, no other tandem phase between OLCI-A and OLCI-B being foreseen due to the cost of such operation. This article presents a novel approach for the monitoring of the OLCI radiometry based on statistics of Deep Convective Clouds (DCC) observations, especially dedicated to accurately monitor the full across-track dependency of the cross-calibration of OLCI-A and OLCI-B. Specifically, the inflexion point of DCC reflectance distributions is used as an indicator of the absolute calibration for each subdivision of the OLCI Field-of-View. This inflexion point is shown to provide better precision than the mode of the distributions which is commonly used in the community. Excess of saturation in OLCI-A high radiances is handled through the analysis of interband relationships between impacted channels and reference channels that are not impacted by saturation. Such analysis also provides efficient insights on the variability of the target’s response as well as on the evolution of the interband calibration of each payload. First, cross-calibration factors obtained over the tandem period allows to develop and validate the approach, notably for the handling of the saturated pixels, based on the comparison with the ‘truth’ obtained from the tandem analysis. Factors obtained out of (and far from) the tandem period then provides evidence that the cross-calibration reported over the tandem period (1–2% bias between the instruments) as well as inter-camera calibration residuals persist with very similar proportions, to the exception of the 400 nm channel and with slightly less precision for the 1020 nm channel. For all OLCI channels, relative differences between the cross-calibration factors obtained from the tandem analysis and the factors obtained over the other period are below 1% from a monthly analysis, even below 0.5% from a multi-monthly analysis). This opens the way not only to an accurate long-term monitoring of the OLCI radiometry but also, and precisely targeted for this study, to the monitoring of the cross-calibration of the two sensors over the mission lifetime. It also provides complementary information to the tandem analysis as the calibration indicators are traced individually for each sensor across-track, confirming and quantifying inter-camera radiometric biases, independently for both sensors. Assumptions used in this study are discussed and validated, also providing a framework for the adaptation of the presented methodology to other optical sensors.

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SMOSREX: A long term field campaign experiment for soil moisture and land surface processes remote sensing

Remote Sensing of Environment ◽

10.1016/j.rse.2006.02.021 ◽

2006 ◽

Vol 102 (3-4) ◽

pp. 377-389 ◽

Cited By ~ 146

Author(s):

Patricia de Rosnay ◽

Jean-Christophe Calvet ◽

Yann Kerr ◽

Jean-Pierre Wigneron ◽

François Lemaître ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Surface ◽

Surface Processes ◽

Land Surface Processes ◽

Field Campaign ◽

Term Field

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Cross-Calibration and Long-Term Monitoring of the Microwave Radiometers of ERS, TOPEX, GFO, Jason, and Envisat

Marine Geodesy ◽

10.1080/01490410490465265 ◽

2004 ◽

Vol 27 (1-2) ◽

pp. 279-297 ◽

Cited By ~ 41

Author(s):

R. SCHARROO ◽

J. L. LILLIBRIDGE ◽

W. H. F. SMITH ◽

E. J. O. SCHRAMA

Keyword(s):

Long Term Monitoring ◽

Cross Calibration ◽

Microwave Radiometers ◽

Term Monitoring

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'Long-term' land surface processes: erosion, tectonics and climate history in mountain belts

TERRA- 1: Understanding The Terrestrial Environment ◽

10.1201/9781482272406-10 ◽

2003 ◽

pp. 35-50

Keyword(s):

Land Surface ◽

Surface Processes ◽

Land Surface Processes ◽

Climate History ◽

Mountain Belts

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THE APPLICATION OF THE COVARIANCE METHOD ANALYSING THE DIGITAL IMAGES OF LAND SURFACE / ŽEMĖS DANGOS POKYČIŲ NUSTATYMAS ATLIEKANT FOTOGRAFINIŲ VAIZDŲ PALYGINIMĄ KOVARIACIJOS METODU / ИЗМЕНЕНИЯ ПОЧВЕННО-РАСТИТЕЛЬНОГО ПОКРОВА, ВЫЯВЛЕННЫЕ ПРИ СРАВНЕНИИ ЦИФРО ВЫХ ИЗОБРАЖЕНИЙ КОВАРИАЦИОННЫМ МЕТОДОМ

Geodesy and Cartography ◽

10.3846/13921541.2011.626260 ◽

2011 ◽

Vol 37 (3) ◽

pp. 105-110 ◽

Cited By ~ 2

Author(s):

Jurgita Milieškaitė

Keyword(s):

Correlation Coefficient ◽

Land Surface ◽

Temporal Evolution ◽

Digital Images ◽

Remote Access ◽

Access Methods ◽

Absolute Value ◽

Long Term Monitoring ◽

Term Monitoring

Accuracy issues of identification possibilities and analyzing digital images of land surface are examined using a covariance method. Digital images received using remote access methods are treated by the computer programes developed in the Matlab 7 software package environment. The paper investigates the opportunity to automatically compare two digital images of fragments and determines the interdependence of comparable images. The interdependence of two images is verified according to the calculated values of the correlation coefficient. Considering a more absolute value of the correlation coefficient between compared digital images, dependency is higher and vice versa. Such method could be applied to long-term monitoring in order to control the temporal evolution of selected images. Santrauka Aprašomas metodas, kuris galėtų būti taikomas atliekant ilgalaikę stebėseną – stebint pasirinktų objektų kitimą laikui bėgant. Nagrinėjama galimybė automatizuotai palyginti du skaitmeninių vaizdų fragmentus ir nustatyti šių lyginamų vaizdų tarpusavio priklausomybę. Skaitmeniniai vaizdai, gauti nuotoliniais metodais, apdorojami pagal sudarytą kompiuterinę programą Matlab 7 programinio paketo operatorių aplinkoje. Šia programa tarpusavyje lyginami du skaitmeniniai vaizdai. Jų tarpusavio priklausomybė įvertinama šia programa apskaičiuotomis koreliacijos koeficientų reikšmėmis. Kai koreliacijos koeficiento absoliučioji reikšmė didesnė, priklausomybė didesnė, ir atvirkščiai. Pagal tai galima įvertinti dviejų skaitmeninių vaizdų arba vieno vaizdo dviejų dalių (fragmentų) tarpusavio priklausomybę (panašumą). Резюме Описывается метод, который может быть применен для долгосрочного мониторинга – наблюдения за изменениями выбранных объектов во времени. Изучается возможность автоматически сравнивать между собой два фрагмента цифровых изображений и определять взаимозависимость этих сравниваемых изображений. Цифровые изображения, полученные дистанционными методами, обрабатываются по созданной компьютерной программе Matlab 7 в среде операторов программного пакета. По этой программе сопоставляются два цифровых изображения, взаимозависимость которых оценивается по рассчитанным программой значениям коэффициентов корреляции. При более высоких абсолютных значениях коэффициента корреляции взаимозависимость более высокая и наоборот. По этим значениям можно оценить взаимозависимость (сходство) двух цифровых изображений или двух частей (фрагментов) одного изображения.

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