Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model

Si, Youbin; Schlerf, Martin; Zurita-Milla, Raúl; Skidmore, Andrew K.; Wang, Zhihui

doi:10.1016/j.rse.2012.02.011

Cited by 110 publications

(83 citation statements)

References 48 publications

(79 reference statements)

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“…Recent research has highlighted the importance of exploiting a priori knowledge to constrain solutions of the ill-posed inversion problem in RTMs [27,43]. In this work, we used the TRY database and the available literature to extract prior knowledge and improve our results.…”

Section: Creation Of Leaf Plant Traits' Distributionsmentioning

confidence: 99%

Global Estimation of Biophysical Variables from Google Earth Engine Platform

et al. 2018

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This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R 2 = 0.87, RMSE = 0.54 m 2 /m 2 and ME = 0.03 m 2 /m 2 in the case of LAI, and R 2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R 2 = 0.42 and R 2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications.

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Section: Creation Of Leaf Plant Traits' Distributionsmentioning

confidence: 99%

Global Estimation of Biophysical Variables from Google Earth Engine Platform

et al. 2018

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“…In addition, when grazing is practiced in an area, the height of the grassland is the first variable affected. However, and similar to the variable CGC, other abiotic factors should be discarded, such as climate variables, which may affect the characteristics of the grassland [50,51].…”

Section: Discussionmentioning

confidence: 99%

Mapping Land Cover and Estimating the Grassland Structure in a Priority Area of the Chihuahuan Desert

Rodríguez-Maturino

Martínez-Guerrero

Chairez-Hernández

et al. 2017

Land

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Abstract:A field characterization of the grassland vegetation structure, represented by the coverage of grass canopy (CGC) and the grass height, was carried out during three years (2009)(2010)(2011) in a priority area for the conservation of grasslands of North America. Landsat Thematic Mapper (TM5) images were selected and the information of reflectance was obtained based on the geographical location of each field-sampling site. Linear models, constructed with field and satellite data, with high coefficients of determination for CGC (R 2 = 0.81, R 2 = 0.81 and R 2 = 0.72) and grass height (R 2 = 0.82, R 2 = 0.79 and R 2 = 0.73) were obtained. The maps showed a good level of CGC (>25%) and grass height (>25 cm), except for the year 2009, which presented the lowest values of grass height in the area. According to the Kappa Index, a moderate concordance among the three CGC maps was presented (0.49-0.59). Conversely, weak and moderate concordances were found among the grass height maps (0.36-0.59). It was observed that areas with a high CGC do not necessarily correspond to areas with greater grass height values. Based on the data analyzed in this study, the grassland areas are highly dynamic, structurally heterogeneous and the spatial distribution of the variables does not show a definite pattern. From the information generated, it is possible to determine those areas that are the most important for monitoring to then establish effective strategies for the conservation of these grasslands and the protection of threatened migratory bird species.

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“…However, the analysis of a second dataset of 1500 m resolution showed that this effect was relatively small in our case. Absolute geolocation accuracy of the data sets also must be considered; while MERIS images were reprojected and co-registered, inaccuracies cannot be excluded [25,51]. Differing sensor spectral resolution characteristics may also be a factor in the observed discrepancies.…”

Section: Meris and Modis Time Series And Trendsmentioning

confidence: 99%

“…The MODIS and MERIS sensors have similar spatial and temporal resolutions, but MERIS has spectral characteristics that are better suited for interpreting vegetation parameters [25]. Atmospheric and soil influences are reduced for specific MERIS indices such as MTCI [20].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Medium Spatial Resolution ENVISAT-MERIS and Terra-MODIS Time Series for Vegetation Decline Analysis: A Case Study in Central Asia

et al. 2014

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Accurate monitoring of land surface dynamics using remote sensing is essential for the synoptic assessment of environmental change. We assessed a Medium Resolution Imaging Spectrometer (MERIS) full resolution dataset for vegetation monitoring as an alternative to the more commonly used Moderate-Resolution Imaging Spectroradiometer (MODIS) data. Time series of vegetation indices calculated from 300 m resolution MERIS and 250 m resolution MODIS datasets were analyzed to monitor vegetation productivity trends in the irrigated lowlands in Northern Uzbekistan for the period 2003-2011. Mann-Kendall trend analysis was conducted using the time series of Normalized Differenced Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and MERIS-based Terrestrial Chlorophyll Index (MTCI) to detect trends and examine the capabilities of each sensor and index. The methodology consisted of (1) preprocessing of the original imagery; (2) processing and statistical analysis of the corresponding time series datasets; and (3) comparison of the resulting trends. Results confirmed the occurrence of widespread vegetation productivity decline, ranging from 5.5% (MERIS-MTCI) to 21% (MODIS-NDVI) of the total irrigated cropland in the study area. All indices identified OPEN ACCESSRemote Sens. 2014, 6 5239 the same spatial patterns of decreasing vegetation. Average vegetation index values of NDVI and SAVI were slightly higher when measured by MERIS than by MODIS. These differences merit further investigation to allow a fusion of these datasets for consistent monitoring of cropland productivity decline at scales suitable for guiding operational land management practices.

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Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model

Cited by 110 publications

References 48 publications

Global Estimation of Biophysical Variables from Google Earth Engine Platform

Global Estimation of Biophysical Variables from Google Earth Engine Platform

Mapping Land Cover and Estimating the Grassland Structure in a Priority Area of the Chihuahuan Desert

Comparison of Medium Spatial Resolution ENVISAT-MERIS and Terra-MODIS Time Series for Vegetation Decline Analysis: A Case Study in Central Asia

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