Mapping global seasonal forest background reflectivity with Multi‐angle Imaging Spectroradiometer data

Jiao, Tong; Liu, Ronggao; Liu, Yang; Písek, Jan; Chen, Jing M.

doi:10.1002/2013jg002493

Cited by 30 publications

(23 citation statements)

References 83 publications

(95 reference statements)

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“…The forest understory LAI was derived from the MISR forest background reflectivity maps by Jiao et al (2014). This MISR seasonal forest background reflectivity dataset provides monthly reflectivities in the red and NIR bands over global forest areas at 0.01 • (approximately 1 km) resolution on a geographic grid, which represents the reflectance of all materials below the forest canopy, such as understory vegetation, rocks, soils, leaf litter, lichens, mosses, snow or their mixture.…”

Section: Modis and Misr Background Reflectivity Datamentioning

confidence: 99%

“…The reflectivity was derived from the MISR surface bidirectional reflectance factor (BRF) in the nadir and 45.6 • forward directions (An and Bf cameras) for each day from 2000 to 2010 based on the four-scale model. Then, monthly forest background reflectivity was produced by averaging 11-year valid results for each month to replace a large number of invalid retrievals (Jiao et al, 2014).…”

Section: Modis and Misr Background Reflectivity Datamentioning

confidence: 99%

“…The LAI derived from the MISR forest background reflectivity (Jiao et al, 2014) should approximate the forest understory LAI. Here, by assuming that the forest understory has a similar composition to a mixture of shrubland, grassland and moss, its LAI was retrieved from the SR of the forest background based on the LAI algorithms for shrub and grass, which is applicable for grass and other non-forest biomes except shrub.…”

Section: Estimation Of the Forest Understory Laimentioning

confidence: 99%

“…As there are still missing values in monthly SR B maps, and uncertainties exist in MISR forest background reflectivity dataset, especially for dense canopy (Jiao et al, 2014), the monthly SR B was scaled to a 10 km resolution to fill the missing values and reduce these uncertainties and then used to adjust the MODIS observed SR (SR obs ) based on Eq. (6).…”

Section: Estimation Of the Forest Overstory Laimentioning

confidence: 99%

“…Background reflectivities in the red and near-infrared (NIR) bands have been retrieved over global forest areas at a resolution of 1.1 km using MISR observations from 2000 to 2010 (Jiao et al, 2014). A large amount of invalid retrievals exists in these background reflectivity maps for each day because of frequently missing data and invalid retrievals in MISR land surface products, which is due to cloud and aerosol contamination, topographically complex terrain, and other suboptimal illumination conditions.…”

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Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data

2017

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Abstract. Forest overstory and understory layers differ in carbon and water cycle regimes and phenology, as well as ecosystem functions. Separate retrievals of leaf area index (LAI) for these two layers would help to improve modeling forest biogeochemical cycles, evaluating forest ecosystem functions and also remote sensing of forest canopies by inversion of canopy reflectance models. In this paper, overstory and understory LAI values were estimated separately for global needleleaf and deciduous broadleaf forests by fusing MISR and MODIS observations. Monthly forest understory LAI was retrieved from the forest understory reflectivity estimated using MISR data. After correcting for the background contribution using monthly mean forest understory reflectivities, the forest overstory LAI was estimated from MODIS observations. The results demonstrate that the largest extent of forest understory vegetation is present in the boreal forest zones at northern latitudes. Significant seasonal variations occur for understory vegetation in these zones with LAI values up to 2-3 from June to August. The mean proportion of understory LAI to total LAI is greater than 30 %. Higher understory LAI values are found in needleleaf forests (with a mean value of 1.06 for evergreen needleleaf forests and 1.04 for deciduous needleleaf forests) than in deciduous broadleaf forests (0.96) due to the more clumped foliage and easier penetration of light to the forest floor in needleleaf forests. Spatially and seasonally variable forest understory reflectivity helps to account for the effects of the forest background on LAI retrieval while compared with constant forest background. The retrieved forest overstory and understory LAI values were compared with an existing dataset for larch forests in eastern Siberia (40-75 • N, 45-180 • E). The retrieved overstory and understory LAI is close to that of the existing dataset, with an absolute error of 0.02 (0.06), relative error of 1.3 % (14.3 %) and RMSE of 0.93 (0.29) for overstory (understory). The comparisons between our results and field measurements in eight forest sites show that the R 2 values are 0.52 and 0.62, and the RMSEs are 1.36 and 0.62 for overstory and understory LAI, respectively.

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Section: Modis and Misr Background Reflectivity Datamentioning

confidence: 99%

Section: Modis and Misr Background Reflectivity Datamentioning

confidence: 99%

Section: Estimation Of the Forest Understory Laimentioning

confidence: 99%

Section: Estimation Of the Forest Overstory Laimentioning

confidence: 99%

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confidence: 99%

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Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data

2017

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Retrieval of seasonal dynamics of forest understory reflectance from semiarid to boreal forests using MODIS BRDF data

et al. 2016

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Spatial and temporal patterns of forest background (understory) reflectance are crucial for retrieving biophysical parameters of forest canopies (overstory) and subsequently for ecosystem modeling. In this communication, we retrieved seasonal courses of understory normalized difference vegetation index (NDVI) from multiangular Moderate Resolution Imaging Spectroradiometer bidirectional reflectance distribution function (MODIS BRDF)/albedo data. We compared satellite‐based seasonal courses of understory NDVI to understory NDVI values measured in different types of forests distributed along a wide latitudinal gradient (65.12°N–31.35°N). Our results indicated that the retrieval method performs well particularly over open forests of different types. We also demonstrated the limitations of the method for closed canopies, where the understory signal retrieval is much attenuated.

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Next Step in Vegetation Remote Sensing: Synergetic Retrievals of Canopy Structural and Leaf Biochemical Parameters

Chen

Wang

et al. 2022

New Thinking in GIScience

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Mapping global seasonal forest background reflectivity with Multi‐angle Imaging Spectroradiometer data

Cited by 30 publications

References 83 publications

Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data

Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data

Retrieval of seasonal dynamics of forest understory reflectance from semiarid to boreal forests using MODIS BRDF data

Next Step in Vegetation Remote Sensing: Synergetic Retrievals of Canopy Structural and Leaf Biochemical Parameters

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