Toward Generic Models for Green LAI Estimation in Maize and Soybean: Satellite Observations

Kira, Oz; Nguy-Robertson, Anthony L.; Arkebauer, Timothy J.; Linker, Raphael; Gitelson, Anatoly A.

doi:10.3390/rs9040318

Cited by 27 publications

(25 citation statements)

References 66 publications

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“…These results suggest a need for testing new vegetation indices adopting L8 bands. In agreement with our results, other studies comparing linear additive models showed a similar ability in estimating canopy cover and LAI adopting the S2 or L8 sensors (Korhonen et al, 2017).…”

Section: Satellite Sensors As Estimators Of Gppsupporting

confidence: 93%

“…The red edge corresponds to the steep increase in reflectance at the boundary between the red region, where chlorophyll is absorbed, and the leaf scattering at the NIR region. Red-edge bands were successfully employed to estimate chlorophyll content in maize (Zhang and Zhou, 2017) and LAI in crops (Kira et al, 2017). For these reasons they were integrated into numerous VIs, such as MTCI and PSRI, which were also applied in this study.…”

Section: Are Spectral Bands Better Gpp Estimators Than Vis?mentioning

confidence: 99%

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On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance

et al. 2018

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Abstract. We applied an empirical modelling approach for gross primary productivity (GPP) estimation from hyperspectral reflectance of Mediterranean grasslands undergoing different fertilization treatments. The objective of the study was to identify combinations of vegetation indices and bands that best represent GPP changes between the annual peak of growth and senescence dry out in Mediterranean grasslands. In situ hyperspectral reflectance of vegetation and CO2 gas exchange measurements were measured concurrently in unfertilized (C) and fertilized plots with added nitrogen (N), phosphorus (P) or the combination of N, P and potassium (NPK). Reflectance values were aggregated according to their similarity (r≥90 %) in 26 continuous wavelength intervals (Hyp). In addition, the same reflectance values were resampled by reproducing the spectral bands of both the Sentinel-2A Multispectral Instrument (S2) and Landsat 8 Operational Land Imager (L8) and simulating the signal that would be captured in ideal conditions by either Sentinel-2A or Landsat 8. An optimal procedure for selection of the best subset of predictor variables (LEAPS) was applied to identify the most effective set of vegetation indices or spectral bands for GPP estimation using Hyp, S2 or L8. LEAPS selected vegetation indices according to their explanatory power, showing their importance as indicators of the dynamic changes occurring in community vegetation properties such as canopy water content (NDWI) or chlorophyll and carotenoids ∕ chlorophyll ratio (MTCI, PSRI, GNDVI) and revealing their usefulness for grasslands GPP estimates. For Hyp and S2, bands performed as well as vegetation indices to estimate GPP. To identify spectral bands with a potential for improving GPP estimates based on vegetation indices, we applied a two-step procedure which clearly indicated the short-wave infrared region of the spectra as the most relevant for this purpose. A comparison between S2- and L8-based models showed similar explanatory powers for the two simulated satellite sensors when both vegetation indices and bands were included in the model. Altogether, our results describe the potential of sensors on board Sentinel-2 and Landsat 8 satellites for monitoring grassland phenology and improving GPP estimates in support of a sustainable agriculture management.

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Section: Satellite Sensors As Estimators Of Gppsupporting

confidence: 93%

Section: Are Spectral Bands Better Gpp Estimators Than Vis?mentioning

confidence: 99%

On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance

et al. 2018

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“…We propose that two processes are having an effect here: first, as the canopy becomes optically thicker, underestimation of LAI is expected [17], and secondly, the comparison of coarse resolution observations with point measurements introduces the effects of sub-pixel landscape heterogeneity [75,76]. In the literature [77,78], the use of empirical methods that have been trained with ground observations of the same area limits the generality of the methods for global applications. Additionally, no simultaneous inferences on FAPAR are presented in either of these two references.…”

Section: Discussionmentioning

confidence: 99%

Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

et al. 2017

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Abstract:The Fraction of Absorbed Photosynthetically-Active Radiation (FAPAR) is an important parameter in climate and carbon cycle studies. In this paper, we use the Earth Observation Land Data Assimilation System (EO-LDAS) framework to retrieve FAPAR from observations of directional surface reflectance measurements from the Multi-angle Imaging SpectroRadiometer(MISR) instrument. The procedure works by interpreting the reflectance data via the semi-discrete Radiative Transfer (RT) model, supported by a prior parameter distribution and a dynamic regularisation model and resulting in an inference of land surface parameters, such as effective Leaf Area Index (LAI), leaf chlorophyll concentration and fraction of senescent leaves, with full uncertainty quantification. The method is demonstrated over three agricultural FLUXNET sites, and the EO-LDAS results are compared with eight years of in situ measurements of FAPAR and LAI, resulting in a total of 24 site years. We additionally compare three other widely-used EO FAPAR products, namely the MEdium Resolution Imaging Spectrometer (MERIS) Full Resolution, the MISR High Resolution (HR) Joint Research Centre Two-stream Inversion Package (JRC-TIP) and MODIS MCD15 FAPAR products. The EO-LDAS MISR FAPAR retrievals show a high correlation with the ground measurements (r 2 > 0.8), as well as the lowest average RMSE (0.14), in line with the MODIS product. As the EO-LDAS solution is effectively interpolated, if only measurements that are coincident with MISR observations are considered, the correlation increases (r 2 > 0.85); the RMSE is lower by 4-5%; and the bias is 2% and 7%. The EO-LDAS MISR LAI estimates show a strong correlation with ground-based LAI (average r 2 = 0.76), but an underestimate of LAI for optically-thick canopies due to saturation (average RMSE = 2.23). These results suggest that the EO-LDAS approach is successful in retrieving both FAPAR and other land surface parameters. A large part of this success is based on the use of a dynamic regularisation model that counteracts the poor temporal sampling from the MISR instrument.

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“…The use of too many bands may also cause over‐fitting of the model to the trained data. Several studies have demonstrated that estimation accuracy was higher when using several defined spectral bands than when using the full spectral information available from a sensor (Meroni et al, 2004; Darvishzadeh et al, 2008; Verrelst et al, 2013, 2016; Kira et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…The data processing was performed similarly to previously published papers estimating green LAI in these crops (Kira et al, 2016, 2017). Training and validation processes included the testing of all possible combinations of spectral bands and a 10‐fold cross validation.…”

Section: Methodsmentioning

confidence: 99%

Gross Primary Production Estimation in Crops Using Solely Remotely Sensed Data

Kira

Nguy-Robertson

et al. 2019

Agronomy Journal

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Gross primary production (GPP) is a measure for crop productivity, indicating yield and expressing C exchange of agro-ecosystems. A multitude of satellite sensors at varying spatial and spectral resolution brings a possibility to use remotely sensed data for regional and global GPP estimation. More work is still needed to develop algorithms for GPP estimation applicable to multiple, if not all, vegetation types, phenological phases, and environmental conditions. This study employed neural networks (NN), multiple linear regressions (MLR), and vegetation indices (VI) to develop algorithms for GPP estimation based solely on remotely sensed data in two crops, maize (Zea mays L.) and soybean [Glycine max (L.) Merr.], with contrasting canopy architectures, leaf structures, and photosynthetic pathways. The focus of the study was to devise algorithms not requiring re-parameterization for different crop species. Data used in the models included in situ hyperspectral reflectance and satellite surface reflectance products. For the tested NN, MLR, and VI algorithms, the bands selected to obtain minimal errors in maize and soybean combined were mainly located in red edge and near infrared (NIR) spectral regions. For both in situ reflectance and satellite surface reflectance, a NN estimated GPP with normalized root mean square errors (NRMSE) below 14 and 18.7%, respectively, and VI using bands in red edge and NIR with NRMSE 15.6 and 20.4%, respectively. The results showed that the models based on the red edge and NIR bands may facilitate accurate assessments of crop GPP at multiple scales, from close range to satellite platforms. core Ideas• The models using remotely sensed data allow accurate estimation of gross primary production in two crops. • The optimal bands for gross primary production estimation in two crops were in near infrared and red edge regions. • Vegetation indices with red edge and near infrared reflectance were generic for maize and soybean. G ross primary production is the amount of biomass C that plants produce in a given period of time. As such, it is the sum of the net primary production (NPP), that is, the net amount of useful biomass C produced by plants, and plant respiration (Re). We have quantified the relationship of cumulative growing season GPP (g C m -2 season -1 ) and grain yield (g C m -2 season -1 ) at the Nebraska AmeriFlux sites (US-Ne1, US-Ne2, and US-Ne3). The coefficient of determination (R 2 ) for this relationship over the first 14 yr of the project is 0.95. This includes both maize and soybean grown under irrigated and rainfed conditions and encompasses a wide variation in environmental conditions. Grain yield is arguably the single most important consideration for agricultural producers and, due to its close correlation with GPP, measurements of GPP are of great interest for evaluating crop yield (e.g., Reeves et al., 2005). Traditionally, field studies use tower eddy covariance systems (e.g., Baldocchi, 2003) to obtain GPP in croplands. However, such approaches bring results over a l...

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Toward Generic Models for Green LAI Estimation in Maize and Soybean: Satellite Observations

Cited by 27 publications

References 66 publications

On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance

On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance

Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

Gross Primary Production Estimation in Crops Using Solely Remotely Sensed Data

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