Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup

Morisette, J. T.; Baret, Frédéric; Privette, Jeffrey L.; Myneni, Ranga B.; Nickeson, Jaime; Garrigues, Sébastien; Shabanov, Nikolay V.; Weiss, Marie; Fernandes, Richard; Leblanc, Sylvain G.; Kalácska, Margaret; Sánchez‐Azofeifa, Arturo; Chubey, M.; Rivard, Benoît; Stenberg, Pauline; Rautiainen, Miina; Voipio, Pekka; Manninen, Terhikki; Pilant, A. N.; Lewis, Timothy E.; Iiames, John; Colombo, R.; Meroni, Michele; Busetto, Lorenzo; Cohen, Warren B.; Turner, David P.; Warner, Eric; Petersen, G. W.; Seufert, G.; Cook, R.B.

doi:10.1109/tgrs.2006.872529

Cited by 367 publications

(272 citation statements)

References 60 publications

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“…This allows for the combination of current data with flux tower measurements. Field measurements were carried out on various spatial scales by applying the concept of elementary sampling units (ESUs; Morisette et al, 2006). Up to three ESUs were established for each crop type to investigate spatial heterogeneity on the scale of acreages.…”

Section: Measurement Design Of the Field Campaignsmentioning

confidence: 99%

“…PAI was ascertained in two different ways: (1) with the LAI-2200 Plant Canopy Analyzer (LAI-2200; LI-COR Biosciences, Inc.) and (2) based on digital hemispherical photos (DHPs). Both methods rely on the determination of the gap fraction (Ross, 1981), permit repetitive non-destructive measurements (Morisette et al, 2006), and were deployed complementarily. While PAI values obtained from the LAI-2200 method are available on the scale of SSPs and ESUs, PAI values derived from DHPs are only provided for entire ESUs.…”

Section: Plant Area Indexmentioning

confidence: 99%

See 1 more Smart Citation

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Truckenbrodt

Schmullius

2018

Earth Syst. Sci. Data

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Abstract. Ground reference data are a prerequisite for the calibration, update, and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations in the visible and infrared range. In situ data were collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato, and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance factors, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data were gathered at an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT 5, Landsat-7 and -8) with a cloud coverage ≤ 25 % are available for 10 and 15 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. Here, the experimental design of the field campaigns, and methods employed in the determination of all parameters, are described in detail. Insights into the database are provided and potential fields of application are discussed. The data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (https://doi.org/10.1594/PANGAEA.874251).

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Section: Measurement Design Of the Field Campaignsmentioning

confidence: 99%

Section: Plant Area Indexmentioning

confidence: 99%

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Truckenbrodt

Schmullius

2018

Earth Syst. Sci. Data

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“…The quality of the retrieved LAI was evaluated according to the framework "validation of global moderate-resolution LAI products" proposed by CEOS WGCV ("Committee Earth Observing Satellites' Working Group on Calibration and Validation") [34] Landsat Thematic Mapper/Enhanced Thematic Mapper (TM/ETM) data acquired quasi-synchronously with the LAI observation dates were used in conjunction with measured LAI to generate the 30 m TM LAI maps. Then, these TM LAI maps were aggregated to 500 m resolution for evaluating the accuracy of inversed LAI using MODIS data [35,36].…”

Section: Data Usedmentioning

confidence: 99%

Spatial and temporal variations of forest LAI in China during 2000–2010

Liu

Chen

et al. 2012

Chin. Sci. Bull.

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Forests are crucial terrestrial ecosystems. Their leaf area index (LAI) is a key parameter determining the exchange of matter and energy between the atmosphere and the ground surface. In this study, MOD 09A1 and MCD 43A1 data were input into an inversion model based on the 4-scale geometric optical model to retrieve 8-d 500 m LAI products in China during the period 2000 to 2010. The resulting LAI product was validated using LAI measured in 6 typical areas. The spatial and temporal variations of forest LAI and its relationships with temperature and precipitation were analyzed. The results show that the accuracy of the 500 m LAI product was above 70% in the 6 typical areas, indicating the reliability of this product.

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“…To ensure that the true LAI values and the spectral images were simultaneously available, the direct evaluation procedure proposed by Zeng, et al [24] was adopted here (Figure 1). This procedure was based on the PROSAIL model with a "bottom-up" LAI product-validation process [14], and the simulated images had to be similar to the satellite images in the vegetation structure and growth seasonal cycle.…”

Section: Sampling Strategy Evaluation Proceduresmentioning

confidence: 99%

“…Ground-based validation is critical for assessing the uncertainties and evaluating the accuracies of these satellite-derived products [10][11][12][13]. In the general "bottom-up" products validation framework, selection of the sampling strategy to define elementary sampling units (ESUs) for the entire site at the kilometer scale is an important step because the chosen sampling strategy results in different coefficients of the up-scaling function that ultimately affect the accuracy assessment of the LAI product [13][14][15]. The widely used random sampling or systematic sampling approaches in prior validation programs are simple and easy to carry out [16][17][18][19], but these approaches may not be appropriate over heterogeneous areas due to their low efficiency and the laborious and time-consuming nature of LAI field measurements [20].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities

Zeng

Liu

et al. 2015

Remote Sensing

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Abstract:A sampling strategy to define elementary sampling units (ESUs) for an entire site at the kilometer scale is an important step in the validation process for moderate-resolution leaf area index (LAI) products. Current LAI-sampling strategies are unable to consider the vegetation seasonal changes and are better suited for single-day LAI product validation, whereas the increasingly used wireless sensor network for LAI measurement (LAINet) requires an optimal sampling strategy across both spatial and temporal scales. In this study, we developed an efficient and robust LAI Sampling strategy based on Multi-temporal Prior knowledge (SMP) for long-term, fixed-position LAI observations. The SMP approach employed multi-temporal vegetation index (VI) maps and the vegetation classification map as a priori knowledge. The SMP approach minimized the multi-temporal bias of the VI frequency histogram between the ESUs and the entire site and maximized the nearest-neighbor index to ensure that ESUs were dispersed in the geographical space. The SMP approach was compared with four sampling OPEN ACCESS Remote Sens. 2015, 7 1301 strategies including random sampling, systematic sampling, sampling based on the land-cover map and a sampling strategy based on vegetation index prior knowledge using the PROSAIL model-based simulation analysis in the Heihe River basin. The results indicate that the ESUs selected using the SMP method spread more evenly in both the multi-temporal feature space and geographical space over the vegetation cycle. By considering the temporal changes in heterogeneity, the average root-mean-square error (RMSE) of the LAI reference maps can be reduced from 0.12 to 0.05, and the relative error can be reduced from 6.1% to 2.2%. The SMP technique was applied to assign the LAINet ESU locations at the Huailai Remote Sensing Experimental Station in Beijing, China, from 4 July to 28 August 2013, to validate three MODIS C5 LAI products. The results suggest that the average R 2 , RMSE, bias and relative uncertainty for the three MODIS LAI products were 0.60, 0.33, −0.11, and 12.2%, respectively. The MCD15A2 product performed best, exhibiting a RMSE of 0.20, a bias of −0.07 and a relative uncertainty of 7.4%. Future efforts are needed to obtain more long-term validation datasets using the SMP approach on different vegetation types for validating moderate-resolution LAI products in time series.

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Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup

Cited by 367 publications

References 60 publications

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Spatial and temporal variations of forest LAI in China during 2000–2010

An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities

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