Downscaling of MODIS leaf area index using landsat vegetation index

Ovakoglou, Georgios; Alexandridis, Thomas; Clevers, J.G.P.W.; Gitas, Ioannis Z.

doi:10.1080/10106049.2020.1750062

Cited by 14 publications

(9 citation statements)

References 32 publications

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“…Moreover, as shown from a number of previous studies, EVI is more robust in the differentiation of the phenology stages due to its ability to explore the structural properties (branches, stems etc.) of the tree apart from just the leaves or green pigment [79][80][81]. Furthermore, the EVI metrics at the M/H stage of all the farms, showed relatively less seasonal variations within farms across the times series.…”

Section: Discussionmentioning

confidence: 88%

Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana

et al. 2021

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In 2020, mango (Mangifera indica) exports contributed over 40 million tons, worth around US$20 billion, to the global economy. Only 10% of this contribution was made from African countries including Ghana, largely due to lower investment in the sector and general paucity of research into the mango value chain, especially production, quality and volume. Considering the global economic importance of mango coupled with the gap in the use of the remote sensing technology in the sector, this study tested the hypothesis that phenological stages of mango can be retrieved from Sentinel-2 (S2) derived time series vegetation indices (VIs) data. The study was conducted on four mango farms in the Yilo Krobo Municipal Area of Ghana. Seasonal (temporal) growth curves using four VIs (NDVI, GNDVI, EVI and SAVI) for the period from 2017 to 2020 were derived for each of the selected orchards and then aligned with five known phenology stages: Flowering/Fruitset (F/FS), Fruit Development (FRD), Maturity/Harvesting (M/H), Flushing (FLU) and Dormancy (D). The significance of the variation “within” and “between” farms obtained from the VI metrics of the S2 data were tested using single-factor and two-factor analysis of variance (ANOVA). Furthermore, to identify which specific variable pairs (phenology stages) were significantly different, a Tukey honest significant difference (HSD) post-hoc test was conducted, following the results of the ANOVA. Whilst it was possible to differentiate the phenological stages using all the four VIs, EVI was found to be the best related with p < 0.05 for most of the studied farms. A distinct annual trend was identified with a peak in June/July and troughs in December/January. The derivation of remote sensing based ‘time series’ growth profiles for commercial mango orchards supports the ‘benchmarking’ of annual and seasonal orchard performance and therefore offers a near ‘real time’ technology for identifying significant variations resulting from pest and disease incursions and the potential impacts of seasonal weather variations.

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Section: Discussionmentioning

confidence: 88%

Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana

et al. 2021

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“…The fine-resolution LAI based on the model-downscaling method was compared with the reference LAI (field LAI measurements and the LAI maps obtained from field LAI) to evaluate the accuracy of this method. Moreover, Ovakoglou's downscaling method [27] was compared to evaluate this model-downscaling method.…”

Section: Fine-resolution Lai Estimationmentioning

confidence: 99%

“…The results and validation of 30 m LAIs are analyzed in this section. Meanwhile, the comparison between the fine-resolution LAI estimated by the model-downscaling method and Ovakoglou's downscaling method [27] is shown in this section. The result evaluated by the ground LAI is shown in Figure 8a; RMSE = 0.821, and bias= 0.299.…”

Section: Accuracy Of the Estimated Fine-resolution Laimentioning

confidence: 99%

“…These results show that the accuracy of the LAI maps with 30 m spatial resolutions based on the model-downscaling method is high and can meet the application requirements for forests. At the Camerons site, the LAI map based on the model-downscaling method proposed in this paper is shown in Figure 12a; the LAI map based on Ovakoglou's downscaling method [27] is shown in Figure 12b; the LAI map based on the model F(P) is shown in Figure 12c. The reference LAI map obtained from the VALERI project is shown in Figure 12d.…”

Section: Forestmentioning

confidence: 99%

“…For example, Gao et al developed a regression tree between homogeneous and high-quality MODIS LAI data and pixel-aggregated Landsat surface reflectance for fine-resolution LAI estimation [25,26]. Moreover, Landsat LAI values were estimated using MODIS regression equations and Landsat Enhanced Vegetation Index (EVI) values [27]. In an LAI downscaling study, these methods ignored the scaling effect of the remote-sensing model.…”

Section: Introductionmentioning

confidence: 99%

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A Model-Downscaling Method for Fine-Resolution LAI Estimation

et al. 2020

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The leaf area index (LAI) is a critical parameter for characterizing the structure and function of vegetation in ecosystems. Currently, operational LAI products always have coarse spatial resolution, and fine-resolution LAI maps are urgently needed for ecological environment assessment and the precise monitoring of cropland growth. LAI downscaling methods are efficient at improving the spatial resolution of LAI products but often ignore the scaling effect of the model. In this study, a novel model-downscaling method is proposed for fine-resolution LAI estimation. It uses scaling equations of model parameters (SEMPs) to describe the scaling relations of models at different spatial resolutions and construct a downscaled model from a coarse-resolution model. Landsat Normalized Difference Vegetation Index (NDVI) at 30 m and Global LAnd Surface Satellite (GLASS) LAI at 1 km spatial resolutions are used because they are readily available. The downscaled model is evaluated by a fine-resolution model directly constructed with fine-resolution data. The fine-resolution LAI values estimated by this model-downscaling method are evaluated with field LAI measurements. The validation results show that the proposed method can generate highly accurate LAIs, with an RMSE of 0.821 at the Pshenichne cropland site in Ukraine and an RMSE of 0.515 at the Camerons forest site in Australia when compared with field LAI measurements. The results are also better than those of Ovakoglou’s downscaling method. These results demonstrate that the model-downscaling method for fine-resolution LAI estimation is viable and referable for related studies.

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Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim Using MODIS Satellite Data by AHP Method and Geospatial Techniques

Das,

Mondal

2024

Environmental Science and Engineering

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Downscaling of MODIS leaf area index using landsat vegetation index

Cited by 14 publications

References 32 publications

Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana

Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana

A Model-Downscaling Method for Fine-Resolution LAI Estimation

Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim Using MODIS Satellite Data by AHP Method and Geospatial Techniques

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