Spatial scale conversion approach for moderate-resolution imaging spectroradiometer leaf area index product validation

2013 ◽  
Vol 7 (1) ◽  
pp. 073463 ◽  
Author(s):  
Yun-ping Chen ◽  
Wei Wei ◽  
Angelica E. Patterson ◽  
Ling Tong
Keyword(s):  
Leaf Area Index ◽  
Spatial Scale ◽  
Leaf Area ◽  
Area Index ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

scholarly journals Long-Term Variation of Global GEOV2 and MODIS Leaf Area Index (LAI) and Their Uncertainties: An Insight into the Product Stabilities

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hongliang Fang ◽  
Yao Wang ◽  
Yinghui Zhang ◽  
Sijia Li
Keyword(s):  
Leaf Area Index ◽  
Product Quality ◽  
Leaf Area ◽  
Vegetation Change ◽  
Temporal Variations ◽  
Area Index ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Global Vegetation

Leaf area index (LAI) is an essential climate variable that is crucial to understand the global vegetation change. Long-term satellite LAI products have been applied in many global vegetation change studies. However, these LAI products contain various uncertainties that are not been fully considered in current studies. The objective of this study is to explore the uncertainties in the global LAI products and the uncertainty variations. Two global LAI datasets—the European Geoland2 Version 2 (GEOV2) and Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2019)—were investigated. The qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) embedded in the product quality layers were analyzed to identify the temporal anomalies in the quality profile. The results show that the global GEOV2 (0.042/10a) and MODIS (0.034/10a) LAI values have steadly increased from 2003 to 2019. The global LAI uncertainty (0.016/10a) and relative uncertainty (0.3%/10a) from GEOV2 have also increased gradually, especially during the growing season from April to October. The uncertainty increase is larger for woody biomes than for herbaceous types. Contrastingly, the MODIS LAI product uncertainty remained stable over the study period. The uncertainty increase indicated by GEOV2 is partly attributed to the sensor shift in the product series. Further algorithm enhancement is necessary to improve the cross-sensor performance. This study highlights the importance of studying the LAI uncertainty and the uncertainty variation. Temporal variations in the LAI products and the product quality revealed herein have significant implications on global vegetation change studies.

scholarly journals An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

2021 ◽  
Vol 13 (4) ◽  
pp. 719
Author(s):  
Xiuxia Li ◽  
Shunlin Liang ◽  
Huaan Jin
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Continuous Data ◽  
Landsat Data ◽  
Area Index ◽  
Free Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Resolution Data

Leaf area index (LAI) and normalized difference vegetation index (NDVI) are key parameters for various applications. However, due to sensor tradeoff and cloud contaminations, these data are often temporally intermittent and spatially discontinuous. To address the discontinuities, this study proposed a method based on spectral matching of 30 m discontinuous values from Landsat data and 500 m temporally continuous values from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experiments have proven that the proposed method can effectively yield spatiotemporally continuous vegetation products at 30 m spatial resolution. The results for three different study areas with NDVI and LAI showed that the method performs well in restoring the time series, fills in the missing data, and reasonably predicts the images. Remarkably, the proposed method could address the issue when no cloud-free data pairs are available close to the prediction date, because of the temporal information “borrowed” from coarser resolution data. Hence, the proposed method can make better use of partially obscured images. The reconstructed spatiotemporally continuous data have great potential for monitoring vegetation, agriculture, and environmental dynamics.

scholarly journals Global White-Sky and Black-Sky FAPAR Retrieval Using the Energy Balance Residual Method: Algorithm and Validation

2019 ◽  
Vol 11 (9) ◽  
pp. 1004 ◽  
Author(s):  
Liu ◽  
Zhang ◽  
Xie ◽  
Liu ◽  
Song ◽  
...  
Keyword(s):  
Energy Balance ◽  
Area Index ◽  
Global Soil ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Comparison Of The Results ◽  
Spectral Mixture ◽  
Sky Conditions ◽  
Absorbed Photosynthetically Active Radiation

The fraction of absorbed photosynthetically active radiation by vegetation (FAPAR) is a key variable in describing the light absorption ability of the vegetation canopy. Most global FAPAR products, such as MCD15A2H and GEOV1, correspond to FAPAR under black-sky conditions at the satellite overpass time only. In this paper, we aim to produce both the global white-sky and black-sky FAPAR products based on the moderate resolution imaging spectroradiometer (MODIS) visible (VIS) albedo, leaf area index (LAI), and clumping index (CI) products. Firstly, a non-linear spectral mixture model (NSM) was designed to retrieve the soil visible (VIS) albedo. The global soil VIS albedo and its dynamics were successfully mapped at a resolution of 500 m using the MCD43A3 VIS albedo product and the MCD15A2H LAI product. Secondly, a method based on the energy balance residual (EBR) principle was presented to retrieve the white-sky and black-sky FAPAR using the MODIS broadband VIS albedo (white-sky and black-sky) product (MCD43A3), the LAI product (MCD15A2H) and CI products. Finally, the two EBR FAPAR products were compared with the MCD15A2H and Geoland2/BioPar version 1 (GEOV1) black-sky FAPAR products. A comparison of the results indicates that these FAPAR products show similar spatial and seasonal patterns. Direct validation using FAPAR observations from the Validation of Land European Remote sensing Instrument (VALERI) project demonstrates that the EBR black-sky FAPAR product was more accurate and had a lower bias (R2 = 0.917, RMSE = 0.088, and bias = −2.8 %) than MCD15A2H (R2 = 0.901, RMSE = 0.096, and bias = 7.6 % ) and GEOV1 (R2 = 0.868, RMSE = 0.105, and bias = 6.1%).

scholarly journals A Trend Analysis of Leaf Area Index and Land Surface Temperature and Their Relationship from Global to Local Scale

Land ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 388
Author(s):  
Azad Rasul ◽  
Sa’ad Ibrahim ◽  
Ajoke R. Onojeghuo ◽  
Heiko Balzter
Keyword(s):  
Surface Temperature ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Regional Scale ◽  
Climate Mitigation ◽  
Area Index ◽  
Detection Analysis ◽  
Moderate Resolution Imaging Spectroradiometer

Although the way in which vegetation phenology mediates the feedback of vegetation to climate systems is now well understood, the magnitude of these changes is still unknown. A thorough understanding of how the recent shift in phenology may impact on, for example, land surface temperature (LST) is important. To address this knowledge gap, it is important to quantify these impacts and identify patterns from the global to the regional scale. This study examines the trend and linear regression modeling of the leaf area index (LAI) and LST derived from the moderate resolution imaging spectroradiometer (MODIS) data, specifically to assess their spatial distribution and changing trends at the continental and regional scales. The change detection analysis of interannual variability in the global LAI and LST between two periods (2003–2010 and 2011–2018) demonstrates more positive LAI trends than negative, while for LST most changes were not significant. The relationships between LAI and LST were assessed across the continents to ascertain the response of vegetation to changes in LST. The regression between LAI and LST was negative in Australia (R2 = 0.487 ***), positive but minimal in Africa (R2 = 0.001), positive in North America (R2 = 0.641 ***), negative in Central America (R2 = 0.119), positive in South America (R2 = 0.253 *) and positive in Europe (R2 = 0.740 ***). Medium temperatures enhance photosynthesis and lengthen the growing season in Europe. We also found a significant greening trend in China (trendp = 0.16 ***) and India (trendp = 0.13 ***). The relationships between LAI and LST in these most prominent greening countries of the world are R2 = 0.06 and R2 = 0.25 for China and India, respectively. Our deductions here are twofold—(1) In China, an insignificant association appeared between greening trend and temperature. (2) In India, the significant greening trend may be a factor in lowering temperatures. Therefore, temperature may stabilize if the greening trend continues. We attribute the trends in both countries to the different land use management and climate mitigation policies adopted by these countries.

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