Evaluation of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30 m GlobeLand30 Datasets: A Case Study in Anhui Province, China

Liang, Dong; Zuo, Yan; Huang, Liusheng; Zhao, Jing; Teng, Ling; Yang, Fan

doi:10.3390/ijgi4042519

Cited by 92 publications

(46 citation statements)

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“…For this objective, we make use of the IGBP (International GeosphereBiosphere Programme) classification (Loveland et al, 1999). We used the official MODIS land cover product (MCD12Q1) for the year 2008 at 5 min resolution (Liang et al, 2015). For each POLDER pixel (≈ 6.2 × 6.2 km 2 ) we analyse the land cover type for the 5 × 5 MODIS cells centred on the POLDER pixel.…”

Section: Data Selectionmentioning

confidence: 99%

A BRDF–BPDF database for the analysis of Earth target reflectances

Bréon

Maignan

2017

Earth Syst. Sci. Data

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Abstract. Land surface reflectance is not isotropic. It varies with the observation geometry that is defined by the sun, view zenith angles, and the relative azimuth. In addition, the reflectance is linearly polarized. The reflectance anisotropy is quantified by the bidirectional reflectance distribution function (BRDF), while its polarization properties are defined by the bidirectional polarization distribution function (BPDF). The POLDER radiometer that flew onboard the PARASOL microsatellite remains the only space instrument that measured numerous samples of the BRDF and BPDF of Earth targets.Here, we describe a database of representative BRDFs and BPDFs derived from the POLDER measurements. From the huge number of data acquired by the spaceborne instrument over a period of 7 years, we selected a set of targets with high-quality observations. The selection aimed for a large number of observations, free of significant cloud or aerosol contamination, acquired in diverse observation geometries with a focus on the backscatter direction that shows the specific hot spot signature. The targets are sorted according to the 16-class International Geosphere-Biosphere Programme (IGBP) land cover classification system, and the target selection aims at a spatial representativeness within the class. The database thus provides a set of high-quality BRDF and BPDF samples that can be used to assess the typical variability of natural surface reflectances or to evaluate models. It is available freely from the PANGAEA website (doi:10.1594/PANGAEA.864090).In addition to the database, we provide a visualization and analysis tool based on the Interactive Data Language (IDL). It allows an interactive analysis of the measurements and a comparison against various BRDF and BPDF analytical models. The present paper describes the input data, the selection principles, the database format, and the analysis tool.

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Section: Data Selectionmentioning

confidence: 99%

A BRDF–BPDF database for the analysis of Earth target reflectances

Bréon

Maignan

2017

Earth Syst. Sci. Data

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“…In order to use this product easily in the community, four main classification schemes were provided, including IGBP (International Geosphere-Biosphere Programme), UMD (University of Maryland), LAI/fPAR (Leaf Area Index/fraction of Photosynthetically Active Radiation), and NPP (Net Primary Productivity) [30,31]. For our study, we use the primary land cover scheme, which is provided by the IGPB land cover classification.…”

Section: Modis Land Covermentioning

confidence: 99%

Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data

2017

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Abstract:Recently, several methods have been introduced and applied to estimate daily air surface temperature (T a ) using MODIS land surface temperature data (MODIS LST). Among these methods, the most common used method is statistical modeling, and the most applied algorithms are linear/multiple linear regression models (LM). There are only a handful of studies using machine learning algorithm models such as random forest (RF) or cubist regression (CB). In particular, there is no study comparing different combinations of four MODIS LST datasets with or without auxiliary data using different algorithms such as multiple linear regression, random forest, and cubist regression for daily T a-max , T a-min , and T a-mean estimation. Our study examines the mentioned combinations of four MODIS-LST datasets and shows that different combinations and differently applied algorithms produce various T a estimation accuracies. Additional analysis of daily data from three climate stations in the mountain area of North West of Vietnam for the period of five years (2009 to 2013) with four MODIS LST datasets (AQUA daytime, AQUA nighttime, TERRA daytime, and TERRA nighttime) and two additional auxiliary datasets (elevation and Julian day) shows that CB and LM should be applied if MODIS LST data is used solely. If MODIS LST is used together with auxiliary data, especially in mountainous areas, CB or RF is highly recommended. This study proved that the very high accuracy of T a estimation (R 2 > 0.93/0.80/0.89 and RMSE~1.5/2.0/1.6 • C of T a-max , T a-min , and T a-mean , respectively) could be achieved with a simple combination of four LST data, elevation, and Julian day data using a suitable algorithm.

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“…The product provides data characterizing five global land cover classification systems at annual time step and a spatial resolution of 500 m × 500 m for the period of 2001-present[36]. In this study, we chose the International Geosphere Biosphere Program (IGBP) classification scheme, which includes 11 natural vegetation classes, three developed and mosaicked land classes, and three non-vegetated land classes.…”

Section: Methodsmentioning

confidence: 99%

A Novel Large-Scale Temperature Dominated Model for Predicting the End of the Growing Season

Zheng

Shi

et al. 2016

PLoS ONE

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Vegetation phenology regulates many ecosystem processes and is an indicator of the biological responses to climate change. It is important to model the timing of leaf senescence accurately, since the canopy duration and carbon assimilation are strongly determined by the timings of leaf senescence. However, the existing phenology models are unlikely to accurately predict the end of the growing season (EGS) on large scales, resulting in the misrepresentation of the seasonality and interannual variability of biosphere–atmosphere feedbacks and interactions in coupled global climate models. In this paper, we presented a novel large-scale temperature dominated model integrated with the physiological adaptation of plants to the local temperature to assess the spatial pattern and interannual variability of the EGS. Our model was validated in all temperate vegetation types over the Northern Hemisphere. The results indicated that our model showed better performance in representing the spatial and interannual variability of leaf senescence, compared with the original phenology model in the Integrated Biosphere Simulator (IBIS). Our model explained approximately 63% of the EGS variations, whereas the original model explained much lower variations (coefficient of determination R2 = 0.01–0.18). In addition, the differences between the EGS reproduced by our model and the MODIS EGS at 71.3% of the pixels were within 10 days. For the original model, it is only 26.1%. We also found that the temperature threshold (TcritTm) of grassland was lower than that of woody species in the same latitudinal zone.

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Evaluation of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30 m GlobeLand30 Datasets: A Case Study in Anhui Province, China

Cited by 92 publications

References 50 publications

A BRDF–BPDF database for the analysis of Earth target reflectances

A BRDF–BPDF database for the analysis of Earth target reflectances

Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data

A Novel Large-Scale Temperature Dominated Model for Predicting the End of the Growing Season

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