David Landgrebe, Professor of Electrical Engineering and Director of the Purdue University Laboratory for Applications of Remote Sensing (LARS), was a primary innovator in the field of digital image analysis and remote sensing of the environment. He and his LARS colleagues, along with a selected few other researchers at institutions including the University of Michigan, University of California Berkley, NASA Goddard Space Flight Center, and NASA Johnson Space Center, defined and developed remote sensing technology to monitor the Earth's terrestrial environment. This research led to the Landsat program, which has continued to monitor the Earth's land areas for a half century. These technologies have defined new fields of scientific query in digital image analysis, biophysical remote sensing, as well as remote sensing science and applications. Dr. Landgrebe's contributions to these research areas were substantial and profound. Understanding the early evolution of work is critical to understanding how this technology is still advancing today. The authors hope that current and future students of these fields will benefit from understanding how this all began.

As of 2015, global carbon flux analyses indicate that 30% of anthropogenic carbon emissions are taken up by an unidentified carbon “sink” likely through vegetation on land. Until the sink can be localized and studied, the processes underlying the carbon uptake cannot be identified, and its future response to a rapidly changing climate cannot be predicted. Satellite remote sensing combined with terrestrial carbon models have shown promise to locate and quantify the magnitude of the land carbon sink, understand it's underlying causes, and how vegetation might respond to climate change.

Resolving the debate surrounding the nature and controls of seasonal variation in the structure and metabolism of Amazonian rainforests is critical to understanding their response to climate change. In situ studies have observed higher photosynthetic and evapotranspiration rates, increased litterfall and leaf flushing during the Sunlight-rich dry season. Satellite data also indicated higher greenness level, a proven surrogate of photosynthetic carbon fixation, and leaf area during the dry season relative to the wet season. Some recent reports suggest that rainforests display no seasonal variations and the previous results were satellite measurement artefacts. Therefore, here we re-examine several years of data from three sensors on two satellites under a range of sun positions and satellite measurement geometries and document robust evidence for a seasonal cycle in structure and greenness of wet equatorial Amazonian rainforests. This seasonal cycle is concordant with independent observations of solar radiation. Weattribute alternative conclusions to an incomplete study of the seasonal cycle, i. e. the dry season only, and to prognostications based on a biased radiative transfer model. Consequently, evidence of dry season greening in geometry corrected satellite data was ignored and the absence of evidence for seasonal variation in lidar data due to noisy and saturated signals was misinterpreted as evidence of the absence of changes during the dry season. Our results, grounded in the physics of radiative transfer, buttress previous reports of dry season increases in leaf flushing, litterfall, photosynthesis and evapotranspiration in well-hydrated Amazonian rainforests.

The Amazon rainforest is a critical hotspot for bio-diversity, and plays an essential role in global carbon, water and energy fluxes and the earth's climate. Our ability to project the role of vegetation carbon feedbacks on future climate critically depends upon our understanding of this tropical ecosystem, its tolerance to climate extremes and tipping points of ecosystem collapse. Satellite remote sensing is the only practical approach to obtain observational evidence of trends and changes across large regions of the Amazon forest; however, inferring these trends in the presence of high cloud cover fraction and aerosol concentrations has led to widely varying conclusions. Our study provides a simple and direct statistical analysis of a measurable change in daily and composite surface reflectance obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) based on the noise level of data and the number of available observations. Depending on time frame and data product chosen for analysis, changes in leaf area need to exceed up to 2 units leaf area per unit ground area (expressed as m2m−2) across much of the basin before these changes can be detected at a 95% confidence level with conventional approaches, roughly corresponding to a change in NDVI and EVI of about 25%. A potential way forward may be provided by advanced multi-angular techniques, such as the Multi-Angle Implementation of Atmospheric Correction Algorithm (MAIAC), which allowed detection of changes of about 0.6–0.8units in leaf area (2–6% change in NDVI) at the same confidence level. In our analysis, the use of the Enhanced Vegetation Index (EVI) did not improve accuracy of detectable change in leaf area but added a complicating sensitivity to the bi-directional reflectance, or view geometry effects.

. The new Sunlit Canopy Adjusted Vegetation Index (SCAVI) uses subpixel scale spectral mixture analysis (SMA) principles for improved biophysical parameter estimation. SCAVI and a new NDVI-modified extension (SCAVI+N) were formulated after soil-adjusted vegetation index (VI) equations and tested using NASA COVER airborne multispectral data for boreal forest black spruce stands in Superior National Forest, Minnesota, USA. Fifteen VIs and 3 SMA fractions were compared. SCAVI was the top-ranked VI for each of leaf area index (LAI: r2 = 0.72), net primary productivity (NPP: 0.72), and biomass (BIO: 0.63), with an overall r2 = 0.69 being >10% higher than the next-ranked VI. Shadow-adjusted vegetation indices (SHAVI, SHAVI+N) were also formulated but had low predictive capabilities. The best result of all variables was from SMA shadow fraction with overall r2 = 0.78 (LAI 0.79, NPP 0.80, BIO 0.74). The importance of endmember-based analysis was clear, because these occupied the top 6 of the 18 rankings. In the absence of SMA or physically based canopy reflectance modeling (CRM), SCAVI might represent a preferred VI and offers advantages of computational simplicity and involving only 1 endmember. Recommendations included testing SCAVI for other areas, refinement of equations, developing other Endmember-Adjusted Vegetation Indices (EAVIs), and the integration of VI, SMA, and CRM concepts.Résumé. Le nouvel indice de végétation ajusté pour le couvert forestier ensoleillé «Sunlit Canopy Adjusted Vegetation Index» (SCAVI) utilise des principes de l’analyse de mélange spectral «spectral mixture analysis» (SMA) à l’échelle du sous-pixel pour une meilleure estimation des paramètres biophysiques. SCAVI et une nouvelle extension modifiée du NDVI (SCAVI+N) ont été formulés à partir des équations de l'indice de végétation «vegetation index» (VI) ajusté pour le sol et testés à l'aide des données multispectrales aéroportées de NASA COVER pour des peuplements d'épinettes noires dans la forêt boréale, dans Superior National Forest, Minnesota, États-Unis. Quinze VIs et 3 fractions de SMA ont été comparés. Le SCAVI a été classé au premier rang des VIs pour l'indice de surface foliaire «leaf area index» (LAI: r2 = 0,72), la productivité primaire nette «net primary productivity» (NPP: 0,72) et la biomasse (BIO: 0,63), avec un r2 global de 0.69 qui était >10 % plus élevé que le deuxième VI. Des indices de végétation ajustés pour les ombres (SHAVI, SHAVI+N) ont également été formulés mais ont montré des capacités prédictives faibles. Le meilleur résultat de toutes les variables était pour la fraction d'ombre SMA avec un r2 global de 0.78 (LAI 0,79; NPP 0,80; BIO 0,74). L'importance d’une analyse fondée sur des endmembers fut claire, car les analyses qui en incluaient ont obtenu les 6 premiers rangs sur 18. En l'absence de la SMA ou de modélisation de la réflectance du couvert végétal à base physique «canopy reflectance modeling» (CRM), SCAVI peut représenter un VI préféré et offre les avantages de la simplicité de calcul et de l’utilisation d’un seul endmember. Les recommandations incluent de tester SCAVI pour d’autres régions, le raffinement d'équations, le développement d'autres indices de végétation ajustés pour les endmembers «Endmember-Adjusted Vegetation Indices» (EAVIs), et l'intégration des concepts de VI, SMA, et CRM.

Gross primary productivity is an excellent metric of how much forests act as carbon dioxide sinks but currently have up to 40% uncertainty in their global estimates. A large proportion of the uncertainty has been attributed to artifacts in the sun-sensor geometry of monolithic spacecrafts leading to insufficient sampling of the bi-directional reflectance of vegetation. This paper proposes to use small satellite clusters with spectrometers as a new measurement solution to improve angular sampling locally and scale up measurements globally. Initial observing system simulations with four satellites launched as secondary payloads via the ISS and operating in different imaging modes show error estimates of less than 12% when compared to dense airborne measurements, a 50% improvement to the worst case error produced by corresponding monoliths.

Vegetation carbon uptake and respiration constitute the largest carbon cycle of the planet with an annual turnover in the order of 120 GT. Currently, neither ecosystem carbon uptake (through photosynthesis) nor ecosystem carbon release (through respiration) can be measured directly during the daytime. Instead, flux-tower measurements rely on nighttime respiration based on the assumption of zero carbon uptake which are then projected to daytime using an exponential relationship to soil temperature at shallow soil depth. As an alternative to this approach, R could possibly also be determined from combining daytime eddy covariance measurements of net ecosystem production (NEP) and spectral observations of gross primary production (GPP). In previous work, we have shown that multi-angular observations can be used to determine GPP from the absorbed photosynthetically active radiation (APAR) and spectrally obtained observations of light-use efficiency (ε). The difference of NEP and GPP suggests that daytime respiration is greater and more dynamic than conventional estimates derived from nighttime flux values. Our findings also suggest that an accelerated ecosystem metabolism results in an exponential increase in respiration which eventually diminishes net ecosystem production. Respiration was also closely related to air and soil temperature. We conclude that tower-level spectral measurements provide considerable new insights into ecosystem fluxes as they allow independent yet complementary measurements of different aspects of the carbon and energy cycle.

Radiative transfer (RT) models were used to simulate reflectance at TM bands and radar beckscattering coefficients at L-bands at acquisition conditions of LANDSAT and PALSAR on the acquisition dates. The forest stands aged from 5 to 250 years simulated from a forest growth model ZELIG at test sites were used to feed the RT models to build a look-up table. A simulated optical and SAR dataset was first used to test the LUT method and the results were promising. SAR data is better for biomass estimation and optical for canopy fractional cover estimation. Combined use of the data didn't improve the biomass estimation and slightly improved the fractional cover estimation. The inversion results from real TM and PALSAR data were presented and discussed.

The Collection 6 (C6) MODIS (Moderate Resolution Imaging Spectroradiometer) land and atmosphere data sets are scheduled for release in 2014. C6 contains significant revisions of the calibration approach to account for sensor aging. This analysis documents the presence of systematic temporal trends in the visible and near-infrared (500 m) bands of the Collection 5 (C5) MODIS Terra and, to lesser extent, in MODIS Aqua geophysical data sets. Sensor degradation is largest in the blue band (B3) of the MODIS sensor on Terra and decreases with wavelength. Calibration degradation causes negative global trends in multiple MODIS C5 products including the dark target algorithm's aerosol optical depth over land and Ångström exponent over the ocean, global liquid water and ice cloud optical thickness, as well as surface reflectance and vegetation indices, including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). As the C5 production will be maintained for another year in parallel with C6, one objective of this paper is to raise awareness of the calibration-related trends for the broad MODIS user community. The new C6 calibration approach removes major calibrations trends in the Level 1B (L1B) data. This paper also introduces an enhanced C6+ calibration of the MODIS data set which includes an additional polarization correction (PC) to compensate for the increased polarization sensitivity of MODIS Terra since about 2007, as well as detrending and Terra–Aqua cross-calibration over quasi-stable desert calibration sites. The PC algorithm, developed by the MODIS ocean biology processing group (OBPG), removes residual scan angle, mirror side and seasonal biases from aerosol and surface reflectance (SR) records along with spectral distortions of SR. Using the multiangle implementation of atmospheric correction (MAIAC) algorithm over deserts, we have also developed a detrending and cross-calibration method which removes residual decadal trends on the order of several tenths of 1% of the top-of-atmosphere (TOA) reflectance in the visible and near-infrared MODIS bands B1–B4, and provides a good consistency between the two MODIS sensors. MAIAC analysis over the southern USA shows that the C6+ approach removed an additional negative decadal trend of Terra ΔNDVI ~ 0.01 as compared to Aqua data. This change is particularly important for analysis of vegetation dynamics and trends in the tropics, e.g., Amazon rainforest, where the morning orbit of Terra provides considerably more cloud-free observations compared to the afternoon Aqua measurements.

Significance Understanding the sensitivity of tropical vegetation to changes in precipitation is of key importance for assessing the fate of the Amazon rainforest and predicting atmospheric CO 2 levels. Using improved satellite observations, we reconcile observational and modeling studies by showing that tropical vegetation is highly sensitive to changes in precipitation and El Niño events. Our results show that, since the year 2000, the Amazon forest has declined across an area of 5.4 million km 2 as a result of well-described reductions in rainfall. We conclude that, if drying continues across Amazonia, which is predicted by several global climate models, this drying may accelerate global climate change through associated feedbacks in carbon and hydrological cycles.

The BOREAS HYD-9 team collected several data sets containing precipitation and strearnflow measurements over the BOREAS study areas. This data set contains the measurements from the tipping bucket rain gauges at the BOREAS NSA and SSA. These measurements were submitted in 15-minute and 1-hour intervals. Only the 15-minute interval data set was loaded into the data base tables. Data were collected from the tipping bucket gauges from mid-April until mid-October in 1994, 1995, and 1996. The data are available in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884) or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

The Remote Sensing Science (RSS)-19 team collected Compact Airborne Spectrographic Imager (CASI) images from the Chieftain Navaho aircraft in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI was deployed as a site-specific optical sensor during Boreal Ecosystem-Atmosphere Study (BOREAS) field campaigns. Image data were collected with CASI on 36 days during five field campaigns between February and September 1994, primarily at flux tower sites located at study sites near Thompson, Manitoba, and Prince Albert, Saskatchewan. A variety of CASI data collection strategies were used to meet the following scientific objectives: 1) canopy bidirectional reflectance, 2) canopy biochemistry, 3) spatial variability, and 4) estimates of up and downwelling Photosynthetically Active Radiation (PAR) and spectral albedo, as well as changes along transects across lakes and transects the Northern Study Area (NSA) and Southern Study Area (SSA). The images are stored as binary image files. A subset of the 1994 CASI acquisitions have been compressed and included on the BOREAS CD-ROM set. This subset includes three images for the NSA-OBS (Old Black Spruce) site on 06 Jun 94, 08 Aug 94, and 06 Sep 94, one image for the SSA-OBS site on 24 Jul 94; and one image for the NSA-Fen site on 08 Aug 94. The CASI imagery on the BOREAS CD-ROMs have been compressed using the Gzip program. The rest of the 1994 BOREAS CASI archive are not contained on the BOREAS CD-ROM set. Inventory listing files are supplied on the CD-ROM to inform users of the data that were collected. The RSS-19 1994 CASI images are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Radiative transfer (RT) models provide an improved theoretical and physical basis for deriving biophysical structural information compared with statistically-based empirical methods. It has been used in various studies, mainly in optical remote sensing applications. This study is to explore the potential of applying the physically-based approach to multi-sensor data in forest parameters estimation. Optical reflectance model and radar backscatter models were used to create a look up table by simulation of multi-spectral reflectance at LANDSAT bands and radar backscattering coefficients and the height of scattering phase center in L-band from the forest stands generated by a forest growth model. As a first step, a simulated data set was used to test the look up table method. The results showed that optical reflectance, radar backscatter and interferometric SAR signature have their own advantage in deriving different parameters, and combined use of these data improved the estimation results. In next step of our study, real data, such as LANDSAT data, ALOS PALSAR data will be used in look-up table inversion. The field measurements and parameters derived from lidar data will be used for assessing the accuracy of the forest biophysical parameters from the physically-based algorithms.

Surface energy balance is a major determinant of land surface temperature and the Earth's climate. To date, there is no approach that can produce effective, physically consistent, global and multi-decadal energy-water flux data over land. Net radiation (R-n) can be quantified regionally using satellite retrievals of surface reflectance and thermal emittance with errors <10%. However, consistent, useful retrieval of latent heat flux (lambda E) from remote sensing is not yet possible. In theory, lambda E could be inferred as a residual of R-n, ground heat (G) and sensible heat (H) fluxes (R-n-H-G). However, large uncertainties in remote sensing of both H and G result in low accuracies for lambda E. Where vegetation is the dominant surface cover, lambda E is largely driven by transpiration of intercellular water through leaf stomata during the photosynthetic uptake of carbon. In these areas, satellite retrievals of photosynthesis (GPP) could be used to quantify transpiration rates through stomatal conductance. Here, we demonstrate how remote sensing of GPP could be applied to obtain lambda E from passive optical measurements of vegetation leaf reflectance related to the photosynthetic rate independent of knowledge of H, R-n and G. We validate the algorithm using five structurally and physiologically diverse eddy flux sites in western and central Canada. Results show that transpiration and H were accurately predicted from optical data and highly significant relationships were found between the energy budget obtained from eddy flux measurements and remote sensing (0.64 <= r(2) <= 0.85). We conclude that spaceborne estimates of GPP could significantly improve not only estimates of the carbon balance but also the energy balance over land. (c) 2013 Elsevier Inc. All rights reserved.