Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging

Hyperspectral cameras onboard unmanned aerial vehicles (UAVs) have recently emerged for monitoring crop traits at the sub-field scale. Different physical, statistical, and hybrid methods for crop trait retrieval have been developed. However, spectra collected from UAVs can be confounded by various issues, including illumination variation throughout the crop growing season, the effect of which on the retrieval performance is not well understood at present. In this study, four retrieval methods are compared, in terms of retrieving the leaf area index (LAI), fractional vegetation cover (fCover), and canopy chlorophyll content (CCC) of potato plants over an agricultural field for six dates during the growing season. We analyzed: (1) The standard look-up table method (LUTstd), (2) an improved (regularized) LUT method that involves variable correlation (LUTreg), (3) hybrid methods, and (4) random forest regression without (RF) and with (RFexp) the exposure time as an additional explanatory variable. The Soil–Leaf–Canopy (SLC) model was used in association with the LUT-based inversion and hybrid methods, while the statistical modelling methods (RF and RFexp) relied entirely on in situ data. The results revealed that RFexp was the best-performing method, yielding the highest accuracies, in terms of the normalized root mean square error (NRMSE), for LAI (5.36%), fCover (5.87%), and CCC (15.01%). RFexp was able to reduce the effects of illumination variability and cloud shadows. LUTreg outperformed the other two retrieval methods (hybrid methods and LUTstd), with an NRMSE of 9.18% for LAI, 10.46% for fCover, and 12.16% for CCC. Conversely, LUTreg led to lower accuracies than those derived from RF for LAI (5.51%) and for fCover (6.23%), but not for CCC (16.21%). Therefore, the machine learning approaches—in particular, RF—appear to be the most promising retrieval methods for application to UAV-based hyperspectral data.

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A Sentinel-1 based European crop parcel map using 2018 in-situ LUCAS Copernicus observations

10.5194/egusphere-egu2020-18703 ◽

2020 ◽

Author(s):

Verhegghen Astrid ◽

d'Andrimont Raphaël ◽

Lemoine Guido ◽

Strobl Peter ◽

van der Velde Marijn

Keyword(s):

Remote Sensing ◽

European Union ◽

Land Cover ◽

Growing Season ◽

Earth Observation ◽

Soft Wheat ◽

High Quality ◽

In Situ Data ◽

Crop Type

Efficient near-real time and wall-to-wall land monitoring is now possible with unprecedented detail because of the fleet of Copernicus Sentinel satellites. This remote sensing paradigm is the consequence of the freely accessible, global, Copernicus data, combined with affordable cloud computing. However, to translate this capacity in accurate products, and to truly benefit from the high spatial detail (~10m) and temporal resolution (~5 days in constellation) of the Sentinels 1 and 2, high quality and timely in-situ data remains crucial. Robust operational monitoring systems are in need of both training and validation data.&#160;Here, we demonstrate the potential of Sentinel 1 observations and complementary high-quality in-situ data to generate a crop type map at continental scale. In 2018, the Land Cover and Land Use Area frame Survey (LUCAS) carried out in the European Union contained a specific Copernicus module corresponding to 93.091 polygons surveyed in-situ. In contrast to the usual LUCAS point observation, the Copernicus protocol provides data on the extent of homogeneous land cover for a maximum size of 100 x 100 m, making it meaningful for remote sensing applications. After filtering the polygons to retrieve only high quality sample, a sample was selected to explore the accuracy of crop type maps at different moments of the 2018 growing season over Europe. The time series of 10 days VV and VH were classified using Random Forest models. The crops that were mapped correspond to the 13 major crops in Europe and are those that are monitored and forecast by the JRC MARS activities (soft wheat, maize, rapeseed, barley, potatoes, ...). Overall, reasonable accuracies were obtained (~80%). Although no a priori parcel delineation was used, it was encouraging to observe the relative homogeneity of pixel classification results within the same parcel. In the context of forecasting, we specifically assessed at what time in the growing season accuracies moved beyond a set threshold for the different crops. This ranged from May for winter crops such as soft wheat, and September for summer crops such as maize.&#160;Our results contribute to the discussion regarding the usefulness, benefits, as well as weaknesses, of the newly acquired LUCAS Copernicus data. Doing so, this study demonstrates the potential of in-situ surveys such as LUCAS Copernicus module&#160; specifically targeted for Earth Observation applications. Future improvements to the LUCAS Copernicus survey methodology are suggested. Importantly, now that LUCAS has been postponed to 2022, and aligned with the Copernicus space program, we advocate for a European Union wide systematic and representative in-situ sample campaign relevant for Earth Observation applications, beyond the traditional LUCAS survey.&#160;

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Retrieving and Validating Leaf and Canopy Chlorophyll Content at Moderate Resolution: A Multiscale Analysis with the Sentinel-3 OLCI Sensor

Remote Sensing ◽

10.3390/rs13081419 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1419

Author(s):

Charlotte De Grave ◽

Luca Pipia ◽

Bastian Siegmann ◽

Pablo Morcillo-Pallarés ◽

Juan Pablo Rivera-Caicedo ◽

...

Keyword(s):

Chlorophyll Content ◽

Spatial Resolution ◽

Spatial Scales ◽

Fluorescence Signal ◽

Scaling Analysis ◽

Terrestrial Vegetation ◽

Area Index ◽

In Situ Data ◽

Linear Behavior

ESA’s Eighth Earth Explorer mission “FLuorescence EXplorer” (FLEX) will be dedicated to the global monitoring of the chlorophyll fluorescence emitted by vegetation. In order to properly interpret the measured fluorescence signal, essential vegetation variables need to be retrieved concomitantly. FLEX will fly in tandem formation with Sentinel-3 (S3), which conveys the Ocean and Land Color Instrument (OLCI) that is designed to characterize the atmosphere and the terrestrial vegetation at a spatial resolution of 300 m. In support of FLEX’s preparatory activities, this paper presents a first validation exercise of OLCI vegetation products against in situ data coming from the 2018 FLEXSense campaign. During this campaign, leaf chlorophyll content (LCC) and leaf area index (LAI) measurements were collected over croplands, while HyPlant DUAL images of the area were acquired at a 3 m spatial resolution. A multiscale validation strategy was pursued. First, estimates of these two variables, together with the combined canopy chlorophyll content (CCC = LCC × LAI), were obtained at the HyPlant spatial resolution and were compared against the in situ measurements. Second, the fine-scale retrieval maps from HyPlant were coarsened to the S3 spatial scale as a reference to assess the quality of the OLCI vegetation products. As an intermediary step, vegetation products extracted from Sentinel-2 data were used to compare retrievals at the in-between spatial resolution of 20 m. For all spatial scales, CCC delivered the most accurate estimates with the smallest prediction error obtained at the 300 m resolution (R2 of 0.74 and RMSE = 26.8 μg cm−2). Results of a scaling analysis suggest that CCC performs well at the different tested spatial resolutions since it presents a linear behavior across scales. LCC, on the other hand, was poorly retrieved at the 300 m scale, showing overestimated values over heterogeneous pixels. The introduction of a new LCC model integrating mixed reflectance spectra in its training enabled to improve by 16% the retrieval accuracy for this variable (RMSE = 10 μg cm−2 for the new model versus RMSE = 11.9 μg cm−2 for the former model).

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Evaluation of Earth System Model and Atmospheric Inversion using Total Column CO2 Observations from GOSAT and OCO-2

10.21203/rs.3.rs-53348/v2 ◽

2020 ◽

Author(s):

Prabir K Patra ◽

Tomohiro Hajima ◽

Ryu Saito ◽

Naveen Chandra ◽

Yukio Yoshida ◽

...

Keyword(s):

Remote Sensing ◽

Greenhouse Gases ◽

Satellite Remote Sensing ◽

Inverse Model ◽

Earth System Model ◽

System Model ◽

Earth System ◽

Area Index ◽

In Situ Data

Abstract The measurements of one of the major greenhouse gases, carbon dioxide (CO2), are being made using dedicated satellite remote sensing since the launch of the greenhouse gases observing satellite (GOSAT) by Japan Aerospace Exploration Agency (JAXA) in 2009 and National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2). In the past 10 years, estimation of CO2 fluxes from land and ocean using the earth system models (ESMs) and inverse modelling of in situ atmospheric CO2 data have also made significant progress. In this article, we attempt, for the first time, to evaluate the CO2 fluxes simulated by an earth system model (MIROC-ES2L) using GOSAT observations and the fluxes estimated by an inverse model (MIROC4-Inv) for the period 2009-2014. Further, we use the OCO-2 measurements for testing the consistency of inversion results for the period 2014-2018, along with the GOSAT data. Both MIROC-ES2L and MIROC4-Inv fluxes are used in the MIROC4-atmospheric chemistry transport model (referred to as ACTM_ES2LF and ACTM_InvF, respectively) for calculating CO2 concentrations that are sampled at the time and location of the satellite measurements. Our results suggest the inverse model using in situ data are more consistent with the OCO-2 retrievals, compared to those of the GOSAT XCO2 data, suggesting possible improvements in the present GOSAT retrieval system by better accounting for the degradation correction of the Thermal And Near infrared Sensor for carbon Observations - Fourier Transform Spectrometer (TANSO-FTS). The ACTM_ES2LF simulation shows a slightly weaker seasonal cycle for the meridional profiles of CO2 fluxes, compared to that from the ACTM_InvF. This difference is revealed by greater XCO2 differences for ACTM_ES2LF vs GOSAT, compared to those of ACTM_InvF vs GOSAT. Using remote sensing based global products of leaf area index (LAI) and gross primary productivity (GPP) over land, we show a weaker sensitivity of MIROC-ES2L biospheric activities to the weather and climate in the tropical regions. Our results clearly suggest the usefulness of XCO2 measurements by satellite remote sensing for evaluation of large-scale ESMs, which so far remained untested by the sparse in situ data.

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Evaluation of Earth Systems Models and Atmospheric Inversions using Total Column CO2 Observations from GOSAT and OCO-2

10.21203/rs.3.rs-53348/v1 ◽

2020 ◽

Author(s):

Prabir K Patra ◽

Tomohiro Hajima ◽

Ryu Saito ◽

Naveen Chandra ◽

Yukio Yoshida ◽

...

Keyword(s):

Remote Sensing ◽

Greenhouse Gases ◽

Atmospheric Chemistry ◽

Satellite Remote Sensing ◽

Seasonal Cycle ◽

Inverse Model ◽

Earth System ◽

Area Index ◽

In Situ Data

Abstract The measurements of one of the major greenhouse gases, carbon dioxide (CO 2 ), are being made using dedicated satellite remote sensing since the launch of the greenhouse gases observing satellite (GOSAT) by JAXA in 2009 and NASA’s Orbiting Carbon Observatory-2 (OCO-2). In the past 10 years, estimation of CO 2 fluxes from land and ocean using the earth system models (ESMs) and inverse modelling of in situ atmospheric CO 2 data have also made significant progress. In this article, we attempt, for the first time, to evaluate the CO 2 fluxes simulated by an earth system model (MIROC-ES2L) using GOSAT observations and the fluxes estimated by an inverse model (MIROC4-Inv) for the period 2009-2014. Further, we use the OCO-2 measurements for testing the consistency of inversion results for the period 2014-2018, along with the GOSAT data. Both MIROC-ES2L and MIROC4-Inv fluxes are used in the MIROC4-atmospheric chemistry transport model (referred to as ACTM_ES2LF and ACTM_InvF, respectively) for calculating CO 2 concentrations that are sampled at the time and location of the satellite measurements. Our results suggest the inverse model using in situ data are more consistent with the OCO-2 retrievals, compared to those of the GOSAT XCO 2 data, suggesting possible improvements in the present GOSAT retrieval system by better accounting for the degradation correction of the TANSO-FTS. The ACTM_ES2LF simulation shows a slightly weaker seasonal cycle for the meridional profiles of CO 2 fluxes, compared to that from the ACTM_InvF. This difference is revealed by greater ACTM_ES2LF vs GOSAT differences, compared to those of ACTM_InvF vs GOSAT. We also find that the simulated seasonal cycle amplitude of XCO 2 by ACTM_ES2LF are slightly weaker compared to those observed by GOSAT or ACTM_InvF. Using remote sensing based global products of leaf area index (LAI) and gross primary productivity (GPP) over land, we show a weaker sensitivity of MIROC-ES2L biospheric activities to the weather and climate in the tropical regions. Our results clearly suggest the usefulness of XCO 2 measurements by satellite remote sensing for evaluation of large-scale ESMs, which so far remained untested by the sparse in situ data.

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Mapping of Crop Stress Related to Soil Degradation within Rainfed Mediterranean Agricultural Areas using Hyperspectral Optical and Thermal Data

10.5194/egusphere-egu2020-19342 ◽

2020 ◽

Author(s):

Robert Milewski ◽

Thomas Schmid ◽

Paula Escribano ◽

Eyal Ben-Dor ◽

Marcos Jiménez Michavila ◽

...

Keyword(s):

Soil Erosion ◽

Soil Properties ◽

Leaf Area Index ◽

Land Management ◽

Soil Degradation ◽

Crop Yields ◽

Growing Season ◽

Hyperspectral Data ◽

Area Index ◽

Agricultural Areas

Hyperspectral data acquired for different seasons provide the means to derive relevant plant biophysical properties during the growing season over agricultural areas, as well as determine soil properties, when the soils are exposed, e.g. during fallow or after harvesting. This combined information can give a detailed insight on the effect of soil degradation on vegetation growth and finally crop yield. In the Mediterranean region, land use practices for crop cultivation have a long history exploiting soils as a natural resource. The soils are an essential factor contributing to agricultural production of rainfed crops such as cereals, olive groves and vineyards. Inadequate land management is endangering soil quality and productivity, and in turn crop quality and productivity are affected. Therefore, the main objective of this work is to map crop stress related to soil degradation and land management practices within a Mediterranean environment focusing on hyperspectral data within the visible, near-infrared, and short-wave infrared as well as thermal infrared (0.4-12 &#181;m) and test the transferability of the methods used to future hyperspectral space-borne sensors such as PRISMA, EnMAP, SHALOM, CHIME and SBG.In this framework, CASI and AHS hyperspectral imagery have been obtained during the growing season within the Camarena agricultural area located in central Spain. The area is characterized by a Mediterranean climate, a gently undulating relief, evolved soils and traditional rainfed agriculture area. In this environment a combination of tillage erosion as a result of plowing practices, as well as water erosion, has led to the exposure of different soil horizons at the surface with contrasting soil properties. These surface properties have been previously characterized as erosion stages of the same cultivated area in a fallow state. Simultaneous to the airborne acquisitions, intensive field campaigns took place for the characterization of soil and crop variability. This included field spectroradiometry measurements of the different surface covers and vegetation parameters such as Leaf Area Index (LAI), leaf chlorophyll content, plant biomass and grain yield in locations with variable soil erosion and deposition stages from low to very high eroded soils. First results based on random forest modeling between the soil erosion stage mapping and the AHS/CASI remote sensing imagery of the growing season indicate a strong link between the soil conditions and the spectral properties of the crops. Furthermore, biophysical parameters derived from the imagery in the green season such as Leaf Area Index and Leaf Water Content correlated also well with the soil erosion stages. For selected test sites it could be shown that low crop yields are associated with 1) highly eroded areas, where exposure of the calcite rich bedrock can cause deficiency in nutrient uptake and 2) very sandy accumulation areas that are depleted in nutrients and have low potential for water retention. Whereas highest crop yields are associated with clay and iron rich, moderately to low eroded soils. This study integrates optical VNIR-SWIR-TIR spectral domain and present preliminary results that emphasize the strong influence of soil quality on crop stress and production.

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Coupling bioturbation activity to metal (Fe and Mn) profiles in situ

Biogeosciences ◽

10.5194/bg-10-2365-2013 ◽

2013 ◽

Vol 10 (4) ◽

pp. 2365-2378 ◽

Cited By ~ 17

Author(s):

L. R. Teal ◽

E. R. Parker ◽

M. Solan

Keyword(s):

Statistical Modelling ◽

Time Lapse ◽

Simulation Modelling ◽

Sediment Profile ◽

Infaunal Community ◽

In Situ Data ◽

Fe And Mn ◽

Spatio Temporal ◽

Mechanistic Basis

Abstract. The relative contributions that species assemblages, abiotic variables, and their interactions with one another make to ecosystem properties are recognised but are seldom considered simultaneously, within context, and at the appropriate spatio-temporal scales. Here, we combine fluorescent time-lapse sediment profile imaging (f-SPI) and diffusion gradient thin gels (DGT) to examine, in situ, the link between an important benthic ecosystem process (bioturbation) and the availability (profiles) of Fe and Mn. Whilst the combination of these methodologies (fg-SPI) was successful in gathering high-resolution in situ data of bioturbation activity and Fe/Mn profiles simultaneously, we show that the mechanistic basis of how the infaunal community mediate Fe and Mn is difficult to reconcile because of the spatio-temporal differences between particle and porewater mixing. This mismatch means that the consideration of these mechanistic processes in isolation is likely to limit our interpretative capacity of how infaunal communities mediate various biogeochemical processes in the natural environment. Moreover, the combination of multiple technologies, process based simulation modelling and generalised additive statistical modelling achieved here, emphasises the importance of simultaneously considering additional factors that influence benthic chemistry, in particular bioirrigation and tidal flushing of the sediment profile. Our findings highlight a pressing need to determine how the relative importance of multiple abiotic and biotic factors act in concert to alter major biogeochemical pathways across a variety of contexts and habitats.

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Laboratory and image spectroscopy for evaluating the biophysical state of meadow vegetation in the Krkonoše National Park

Miscellanea Geographica ◽

10.2478/mgrsd-2014-0014 ◽

2014 ◽

Vol 18 (2) ◽

pp. 15-22 ◽

Cited By ~ 2

Author(s):

Jan Jelének ◽

Lucie Kupková ◽

Bogdan Zagajewski ◽

Stanislav Březina ◽

Adrian Ochytra ◽

...

Keyword(s):

Invasive Species ◽

National Park ◽

Vegetation Indices ◽

In Situ Measurements ◽

Hyperspectral Data ◽

Coefficient Of Determination ◽

Area Index ◽

Area Of Interest ◽

Maximal Sensitivity

Abstract The paper deals with the evaluation of mountain meadow vegetation condition using in-situ measurements of the fraction of Accumulated Photosynthetically Active Radiation (fAPAR) and Leaf Area Index (LAI). The study analyses the relationship between these parameters and spectral properties of meadow vegetation and selected invasive species with the goal of finding out vegetation indices for the detection of fAPAR and LAI. The developed vegetation indices were applied on hyperspectral data from an APEX (Airborne Prism Experiment) sensor in the area of interest in the Krkonoše National Park. The results of index development on the level of the field data were quite good. The maximal sensitivity expressed by the coefficient of determination for LAI was R2 = 0.56 and R2 = 0.79 for fAPAR. However, the sensitivity of all the indices developed at the image level was quite low. The output values of in-situ measurements confirmed the condition of invasive species as better than that of the valuable original meadow vegetation, which is a serious problem for national park management.

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Vegetation Optical Depth and Soil Moisture Retrieved from L-Band Radiometry over the Growth Cycle of a Winter Wheat

Remote Sensing ◽

10.3390/rs10101637 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1637 ◽

Cited By ~ 9

Author(s):

Thomas Meyer ◽

Lutz Weihermüller ◽

Harry Vereecken ◽

François Jonard

Keyword(s):

Soil Moisture ◽

Winter Wheat ◽

Optical Depth ◽

Incidence Angle ◽

Soil Surface ◽

Growing Season ◽

Area Index ◽

Vegetation Height ◽

L Band

L-band radiometer measurements were performed at the Selhausen remote sensing field laboratory (Germany) over the entire growing season of a winter wheat stand. L-band microwave observations were collected over two different footprints within a homogenous winter wheat stand in order to disentangle the emissions originating from the soil and from the vegetation. Based on brightness temperature (TB) measurements performed over an area consisting of a soil surface covered by a reflector (i.e., to block the radiation from the soil surface), vegetation optical depth (τ) information was retrieved using the tau-omega (τ-ω) radiative transfer model. The retrieved τ appeared to be clearly polarization dependent, with lower values for horizontal (H) and higher values for vertical (V) polarization. Additionally, a strong dependency of τ on incidence angle for the V polarization was observed. Furthermore, τ indicated a bell-shaped temporal evolution, with lowest values during the tillering and senescence stages, and highest values during flowering of the wheat plants. The latter corresponded to the highest amounts of vegetation water content (VWC) and largest leaf area index (LAI). To show that the time, polarization, and angle dependence is also highly dependent on the observed vegetation species, white mustard was grown during a short experiment, and radiometer measurements were performed using the same experimental setup. These results showed that the mustard canopy is more isotropic compared to the wheat vegetation (i.e., the τ parameter is less dependent on incidence angle and polarization). In a next step, the relationship between τ and in situ measured vegetation properties (VWC, LAI, total of aboveground vegetation biomass, and vegetation height) was investigated, showing a strong correlation between τ over the entire growing season and the VWC as well as between τ and LAI. Finally, the soil moisture was retrieved from TB observations over a second plot without a reflector on the ground. The retrievals were significantly improved compared to in situ measurements by using the time, polarization, and angle dependent τ as a priori information. This improvement can be explained by the better representation of the vegetation layer effect on the measured TB.

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How valuable are citizen science data for a space-borne crop growth monitoring? – The reliability of self-appraisals

10.5194/egusphere-egu2020-13332 ◽

2020 ◽

Author(s):

Sina C. Truckenbrodt ◽

Friederike Klan ◽

Erik Borg ◽

Klaus-Dieter Missling ◽

Christiane C. Schmullius

Keyword(s):

Management Practices ◽

Test Site ◽

Uncertainty Assessment ◽

Earth Observation ◽

Growth Monitoring ◽

State Variables ◽

Science Data ◽

Area Index ◽

In Situ Data

Space-borne Earth Observation (EO) data depicting vegetation covered land surfaces contain insufficient information for an unambiguous interpretation of the spectral signal in terms of variables that characterize the vegetation state (e.g., leaf area index, leaf chlorophyll content and proportion of senescent material). For the retrieval of vegetation properties from EO data, an optimal estimate of the state variables needs to be found. The uncertainty of such an estimate can be reduced by combining EO data and in situ data. Information provided by citizens represents a valuable and mostly inexpensive source for in situ data. Since the quality of such data can be diverse, the consideration of uncertainties is of great importance.In this contribution, we present a concept for the elicitation of local knowledge from citizens with respect to the application of management practices (e.g., sowing and harvesting date, irrigation) and the instantaneous state of crops. The concept includes the acquisition of in situ data as well as an uncertainty assessment (precision and/or accuracy). The latter involves a profiling in which inherent uncertainties are quantified for individual citizens. This concept was tested for agricultural fields of the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site in Northeast Germany. Within the frame of several field seminars, students were requested to assess management practices and the instantaneous state of crops. Furthermore, they assessed their own ability to create valid data. They filled in pseudonymized questionnaires from which we created corresponding datasets. At the same day, field data were collected with appropriate equipment and can be used as reference against which student estimates can be compared. The level of compliance between estimated and measured data was determined on an individual basis.The results of this analysis will be presented. Conclusions will be drawn regarding the ability of the students to evaluate their own skills. In addition, we will demonstrate an approach for a digital ascertainment of in situ data. In the future, this approach will be used to collect in situ data for the setup of refined prior information within the frame of the Earth Observation Land Data Assimilation System (EO-LDAS).

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Global assessment of Vegetation Index and Phenology Lab (VIP) and Global Inventory Modeling and Mapping Studies (GIMMS) version 3 products

Biogeosciences ◽

10.5194/bg-13-625-2016 ◽

2016 ◽

Vol 13 (3) ◽

pp. 625-639 ◽

Cited By ~ 17

Author(s):

M. Marshall ◽

E. Okuto ◽

Y. Kang ◽

E. Opiyo ◽

M. Ahmed

Keyword(s):

Global Change ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Poor Performance ◽

Area Index ◽

In Situ Data ◽

Northern Latitudes ◽

Wide Range ◽

Global Inventory

Abstract. Earth observation-based long-term global vegetation index products are used by scientists from a wide range of disciplines concerned with global change. Inter-comparison studies are commonly performed to keep the user community informed on the consistency and accuracy of such records as they evolve. In this study, we compared two new records: (1) Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index version 3 (NDVI3g) and (2) Vegetation Index and Phenology Lab (VIP) version 3 NDVI (NDVI3v) and enhanced vegetation index 2 (EVI3v). We evaluated the two records via three experiments that addressed the primary use of such records in global change research: (1) leaf area index (LAI), (2) vegetation climatology, and (3) trend analysis of the magnitude and timing of vegetation productivity. Unlike previous global studies, a unique Landsat 30 m spatial resolution and in situ LAI database for major crop types on five continents was used to evaluate the performance of not only NDVI3g and NDVI3v but also EVI3v. The performance of NDVI3v and EVI3v was worse than NDVI3g using the in situ data, which was attributed to the fusion of GIMMS and MODIS data in the VIP record. EVI3v has the potential to contribute biophysical information beyond NDVI3g and NDVI3v to global change studies, but we caution its use due to the poor performance of EVI3v in this study. Overall, the records were most consistent at northern latitudes during the primary growing season and southern latitudes and the tropics throughout much of the year, while the records were less consistent at northern latitudes during green-up and senescence, and in the great deserts of the world throughout much of the year. These patterns led to general agreement (disagreement) between trends in the magnitude (timing) of NDVI over the study period. Bias in inter-calibration of the VIP record at northernmost latitudes was suspected to contribute most to these discrepancies.

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