Special Issue "Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 September 2015).

Special Issue Editors

Prof. Guomo Zhou
E-Mail
Guest Editor
Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, Zhejiang A&F University Hangzhou, Zhejiang, China
Interests: forestry; biomass/carbon modeling; carbon management; land use/cover change
Prof. Conghe Song
E-Mail Website
Guest Editor
Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3220, USA
Interests: remote sensing of the environment with multiple sensors for natural resource monitoring; land surface biophysical parameter extraction; land cover/land use change (LCLUC) detection; modeling of the ecological consequence of the terrestrial ecosystem as a result of LCLUC in the context of climate change, and understanding the socioeconomic driving factors of LCLUC
Special Issues and Collections in MDPI journals
Prof. Dr. Guangxing Wang
E-Mail Website
Guest Editor
Southern Illinois University, Department of Geography and Environmental Resources
Tel. 618-453-6017
Interests: remote sensing, GIS, spatial statistics, natural and environmental resources, sampling design; environmental quality assessment; forest and city vegetation carbon sequestration, desertification trend monitoring, quality assessment and spatial uncertainty analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Human-induced land use/cover changes, especially deforestation and forest degradation, have been regarded as an important factor influencing global climate and environmental changes, and are important factors resulting in high uncertainty of biomass/carbon budget estimation. It is critical to develop high-quality land use/cover datasets and forest biomass/carbon dynamics at different scales for accurate understanding of carbon cycling and the relevant global changes. Remote sensing techniques and relevant models are important tools for examining land use/cover change, estimating carbon dynamics, and exploring the human-environment interactions.

This Special Issue on “Carbon Cycle, Global Change, and Multi-sensor Remote Sensing” calls for papers that present original research work on the following broad topics:

1) Application of remote sensing techniques to carbon cycle and global change
2) Application of new sensors/algorithms to biomass/carbon dynamics estimation
3) Integration of multi-sensor data for land use/cover classification
4) Integration of multi-temporal data for land use/cover change detection

Prof. Dengsheng Lu
Prof. Guomo Zhou
Prof. Conghe Song
Prof. Guangxing Wang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (11 papers)

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Research

Open AccessArticle
Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data
Remote Sens. 2016, 8(1), 21; https://doi.org/10.3390/rs8010021 - 30 Dec 2015
Cited by 8
Abstract
Agroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past [...] Read more.
Agroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past has been done to study AGB of agroforestry systems using airborne lidar data. Focusing on an agroforestry system in the Brazilian Amazon, this study first predicted plot-level AGB using fixed-effects regression models that assumed the regression coefficients to be constants. The model prediction errors were then analyzed from the perspectives of tree DBH (diameter at breast height)—height relationships and plot-level wood density, which suggested the need for stratifying agroforestry fields to improve plot-level AGB modeling. We separated teak plantations from other agroforestry types and predicted AGB using mixed-effects models that can incorporate the variation of AGB-height relationship across agroforestry types. We found that, at the plot scale, mixed-effects models led to better model prediction performance (based on leave-one-out cross-validation) than the fixed-effects models, with the coefficient of determination (R2) increasing from 0.38 to 0.64. At the landscape level, the difference between AGB densities from the two types of models was ~10% on average and up to ~30% at the pixel level. This study suggested the importance of stratification based on tree AGB allometry and the utility of mixed-effects models in modeling and mapping AGB of agroforestry systems. Full article
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
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Open AccessArticle
Urban Land-Cover Change and Its Impact on the Ecosystem Carbon Storage in a Dryland City
Remote Sens. 2016, 8(1), 6; https://doi.org/10.3390/rs8010006 - 24 Dec 2015
Cited by 27
Abstract
Lack of research into the complexity in urban land conversion, and paucity of observational data of soil organic carbon (SOC) beneath impervious surface area (ISA) limit our understanding of the urbanization effects on carbon (C) pools in dryland cities. Employing Landsat TM images [...] Read more.
Lack of research into the complexity in urban land conversion, and paucity of observational data of soil organic carbon (SOC) beneath impervious surface area (ISA) limit our understanding of the urbanization effects on carbon (C) pools in dryland cities. Employing Landsat TM images acquired in 1990 and 2010, a hybrid classification method consisting of Linear Spectral Mixture Analysis and decision tree classification was applied to retrieve the land cover (water, ISA, greenspace, cropland, and remnant desert) of the largest dryland city in China—Urumqi. Based on vegetation carbon (VEGC) and SOC density data determined through field observations and literature reviews, we developed Urumqi’s C pool maps in 1990 and 2010, and assessed the urbanization impacts on ecosystem C. Our results showed that ISA tripled from 1990 to 2010 displacing remnant desert and cropland. The urban landscape, especially the greenspaces, became obviously fragmented. In 2010, more than 95% of the urban ecosystem C was SOC, 48% of which under the ISA. The city lost 19% of C stock from 1990 to 2010. About 82% of the ecosystem C loss was caused by the conversion of remnant desert and cropland into ISA, mainly in the northern city. Full article
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
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Open AccessArticle
Ability of the Photochemical Reflectance Index to Track Light Use Efficiency for a Sub-Tropical Planted Coniferous Forest
Remote Sens. 2015, 7(12), 16938-16962; https://doi.org/10.3390/rs71215860 - 15 Dec 2015
Cited by 12
Abstract
Light use efficiency (LUE) models are widely used to estimate gross primary productivity (GPP), a dominant component of the terrestrial carbon cycle. Their outputs are very sensitive to LUE. Proper determination of this parameter is a prerequisite for LUE models to simulate GPP [...] Read more.
Light use efficiency (LUE) models are widely used to estimate gross primary productivity (GPP), a dominant component of the terrestrial carbon cycle. Their outputs are very sensitive to LUE. Proper determination of this parameter is a prerequisite for LUE models to simulate GPP at regional and global scales. This study was devoted to investigating the ability of the photochemical reflectance index (PRI) to track LUE variations for a sub-tropical planted coniferous forest in southern China using tower-based PRI and GPP measurements over the period from day 101 to 275 in 2013. Both half-hourly PRI and LUE exhibited detectable diurnal and seasonal variations, and decreased with increases of vapor pressure deficit (VPD), air temperature (Ta), and photosynthetically active radiation (PAR). Generally, PRI is able to capture diurnal and seasonal changes in LUE. However, correlations of PRI with LUE varied dramatically throughout the growing season. The correlation was the strongest (R2 = 0.6427, p < 0.001) in July and the poorest in May. Over the entire growing season, PRI relates better to LUE under clear or partially cloudy skies (clearness index, CI > 0.3) with moderate to high VPD (>20 hPa) and high temperatures (>31 C). Overall, we found that PRI is most sensitive to variations in LUE under stressed conditions, and the sensitivity decreases as the growing conditions become favorable when atmosphere water vapor, temperature and soil moisture are near the optimum conditions. Full article
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
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Open AccessArticle
Increasing the Accuracy of Mapping Urban Forest Carbon Density by Combining Spatial Modeling and Spectral Unmixing Analysis
Remote Sens. 2015, 7(11), 15114-15139; https://doi.org/10.3390/rs71115114 - 11 Nov 2015
Cited by 11
Abstract
Accurately mapping urban vegetation carbon density is challenging because of complex landscapes and mixed pixels. In this study, a novel methodology was proposed that combines a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) [...] Read more.
Accurately mapping urban vegetation carbon density is challenging because of complex landscapes and mixed pixels. In this study, a novel methodology was proposed that combines a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), to map the forest carbon density of Shenzhen City of China, using Landsat 8 imagery and sample plot data collected in 2014. The independent variables that contributed to statistically significantly improving the fit of a model to data and reducing the sum of squared errors were first selected from a total of 284 spectral variables derived from the image bands. The vegetation fraction from LSUA was then added as an independent variable. The results obtained using cross-validation showed that: (1) Compared to the methods without the vegetation information, adding the vegetation fraction increased the accuracy of mapping carbon density by 1%–9.3%; (2) As the observed values increased, the LSR and kNN residuals showed overestimates and underestimates for the smaller and larger observations, respectively, while LMSR improved the systematical over and underestimations; (3) LSR resulted in illogically negative and unreasonably large estimates, while KNN produced the greatest values of root mean square error (RMSE). The results indicate that combining the spatial modeling method LMSR and the spectral unmixing analysis LUSA, coupled with Landsat imagery, is most promising for increasing the accuracy of urban forest carbon density maps. In addition, this method has considerable potential for accurate, rapid and nondestructive prediction of urban and peri-urban forest carbon stocks with an acceptable level of error and low cost. Full article
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
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Open AccessArticle
Estimation of CO2 Sequestration by the Forests in Japan by Discriminating Precise Tree Age Category using Remote Sensing Techniques
Remote Sens. 2015, 7(11), 15082-15113; https://doi.org/10.3390/rs71115082 - 11 Nov 2015
Cited by 12
Abstract
This study estimates CO2 sequestration by forests in Japan using Land Remote Sensing Satellite (Landsat) Operational Land Imager (OLI) and the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) remote sensing data for the in-depth retrieval of forest [...] Read more.
This study estimates CO2 sequestration by forests in Japan using Land Remote Sensing Satellite (Landsat) Operational Land Imager (OLI) and the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) remote sensing data for the in-depth retrieval of forest growth stages (tree age). Landsat imagery was used to develop a detailed forest cover map, while the PALSAR data were used to estimate the volume information. The volume was converted to tree age information for each of the three forest types in Japan. An estimation of CO2 sequestration values for each forest type and for each tree age from the forest inventory data was made. The forest cover map results in four classes, and the overall accuracy yields approximately 74%. For the volume estimation, Root Mean Square Error (RMSE) was computed with the ground reference information resulting in 105.58 m3/ha. The final result showed that total CO2 sequestration in Japan based on tree age forest subclasses yields 85.0 Mt∙CO2 (coniferous), 4.76 Mt∙CO2 (evergreen broadleaf) and 21.61 Mt∙CO2 (deciduous broadleaf), which in total is 111.27 Mt∙CO2. Using remote sensing techniques to quantitatively estimate CO2 sequestration in Japanese forests has been shown both to have advantages and to offer further possibilities. Full article
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
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Open AccessArticle
Spatio-Temporal Changes in Vegetation Activity and Its Driving Factors during the Growing Season in China from 1982 to 2011
Remote Sens. 2015, 7(10), 13729-13752; https://doi.org/10.3390/rs71013729 - 21 Oct 2015
Cited by 24
Abstract
Using National Oceanographic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Climatic Research Unit (CRU) climate datasets, we analyzed interannual trends in the growing-season Normalized Difference Vegetation Index (NDVI) in China from 1982 to 2011, as well as the effects of climatic [...] Read more.
Using National Oceanographic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Climatic Research Unit (CRU) climate datasets, we analyzed interannual trends in the growing-season Normalized Difference Vegetation Index (NDVI) in China from 1982 to 2011, as well as the effects of climatic variables and human activities on vegetation variation. Growing-season (period between the onset and end of plant growth) NDVI significantly increased (p < 0.01) on a national scale and showed positive trends in 52.76% of the study area. A multiple regression model was used to investigate the response of vegetation to climatic factors during recent and previous time intervals. The interactions between growing-season NDVI and climatic variables were more complex than expected, and a lag existed between climatic factors and their effects on NDVI. The regression residuals were used to show that over 6% of the study area experienced significantly human-induced vegetation variations (p < 0.05). These regions were mostly located in densely populated, reclaimed agriculture, afforestation, and conservation areas. Similar conclusions were drawn based on land-use change over the study period. Full article
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
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Open AccessArticle
Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa
Remote Sens. 2015, 7(9), 12076-12102; https://doi.org/10.3390/rs70912076 - 18 Sep 2015
Cited by 12
Abstract
Accurate quantification of land use/cover change (LULCC) is important for efficient environmental management, especially in regions that are extremely affected by climate variability and continuous population growth such as West Africa. In this context, accurate LULC classification and statistically sound change area estimates [...] Read more.
Accurate quantification of land use/cover change (LULCC) is important for efficient environmental management, especially in regions that are extremely affected by climate variability and continuous population growth such as West Africa. In this context, accurate LULC classification and statistically sound change area estimates are essential for a better understanding of LULCC processes. This study aimed at comparing mono-temporal and multi-temporal LULC classifications as well as their combination with ancillary data and to determine LULCC across the heterogeneous landscape of southwest Burkina Faso using accurate classification results. Landsat data (1999, 2006 and 2011) and ancillary data served as input features for the random forest classifier algorithm. Five LULC classes were identified: woodland, mixed vegetation, bare surface, water and agricultural area. A reference database was established using different sources including high-resolution images, aerial photo and field data. LULCC and LULC classification accuracies, area and area uncertainty were computed based on the method of adjusted error matrices. The results revealed that multi-temporal classification significantly outperformed those solely based on mono-temporal data in the study area. However, combining mono-temporal imagery and ancillary data for LULC classification had the same accuracy level as multi-temporal classification which is an indication that this combination is an efficient alternative to multi-temporal classification in the study region, where cloud free images are rare. The LULCC map obtained had an overall accuracy of 92%. Natural vegetation loss was estimated to be 17.9% ± 2.5% between 1999 and 2011. The study area experienced an increase in agricultural area and bare surface at the expense of woodland and mixed vegetation, which attests to the ongoing deforestation. These results can serve as means of regional and global land cover products validation, as they provide a new validated data set with uncertainty estimates in heterogeneous ecosystems prone to classification errors. Full article
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
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Open AccessArticle
Satellite-Observed Energy Budget Change of Deforestation in Northeastern China and its Climate Implications
Remote Sens. 2015, 7(9), 11586-11601; https://doi.org/10.3390/rs70911586 - 11 Sep 2015
Cited by 4
Abstract
Large-scale deforestation may affect the surface energy budget and consequently climate by changing the physical properties of the land surface, namely biophysical effects. This study presents the potential energy budget change caused by deforestation in Northeastern China and its climate implications, which was [...] Read more.
Large-scale deforestation may affect the surface energy budget and consequently climate by changing the physical properties of the land surface, namely biophysical effects. This study presents the potential energy budget change caused by deforestation in Northeastern China and its climate implications, which was evaluated by quantifying the differences in MODIS-observed surface physical properties between cropland and forest. We used the MODIS land products for the period of 2001–2010 in 112 cells of 0.75° × 0.75° each, within which only best quality satellite pixels over the pure forest and cropland pixels are selected for comparison. It is estimated that cropland has a winter (summer) mean albedo of 0.38 (0.16), which is 0.15 (0.02) higher than that of forest. Due to the higher albedo, cropland absorbs 16.84 W∙m2 (3.08 W∙m2) less shortwave radiation than forest. Compared to forest, cropland also absorbs 8.79 W∙m2 more longwave radiation in winter and 8.12 W∙m2 less longwave radiation in summer. In total, the surface net radiation of cropland is 7.53 W∙m2 (11.2 W∙m2) less than that of forest in winter (summer). Along with these radiation changes, the latent heat flux through evapotranspiration over cropland is less than that over forest, especially in summer (−19.12 W∙m2). Average sensible heat flux increases in summer (7.92 W∙m2) and decreases in winter (−8.17 W∙m2), suggesting that conversion of forest to cropland may lead to warming in summer and cooling in winter in Northeastern China. However, the annual net climate effect is not notable because of the opposite sign of the energy budget change in summer and winter. Full article
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
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Open AccessArticle
Sensitivity of Multi-Source SAR Backscatter to Changes in Forest Aboveground Biomass
Remote Sens. 2015, 7(8), 9587-9609; https://doi.org/10.3390/rs70809587 - 28 Jul 2015
Cited by 9
Abstract
Accurate estimates of forest aboveground biomass (AGB) after anthropogenic disturbance could reduce uncertainties in the carbon budget of terrestrial ecosystems and provide critical information to policy makers. Yet, the loss of carbon due to forest disturbance and the gain from post-disturbance recovery have [...] Read more.
Accurate estimates of forest aboveground biomass (AGB) after anthropogenic disturbance could reduce uncertainties in the carbon budget of terrestrial ecosystems and provide critical information to policy makers. Yet, the loss of carbon due to forest disturbance and the gain from post-disturbance recovery have not been sufficiently assessed. In this study, a sensitivity analysis was first conducted to investigate: (1) the influence of incidence angle and soil moisture on Synthetic Aperture Radar (SAR) backscatter; (2) the feasibility of cross-image normalization between multi-temporal and multi-sensor SAR data; and (3) the possibility of applying normalized backscatter data to detect forest biomass changes. An empirical model was used to reduce incidence angle effects, followed by cross-image normalization procedure to lessen soil moisture effect. Changes in forest biomass at medium spatial resolution (100 m) were mapped using both spaceborne and airborne SAR data. Results indicate that (1) the effect of incidence angle on SAR backscatter could be reduced to less than 1 dB by the correction model for airborne SAR data; (2) over 50% of the changes in SAR backscatter due to soil moisture could be eliminated by the cross-image normalization procedure; and (3) forest biomass changes greater than 100 Mg·ha1 or above 50% of 150 Mg·ha1 are detectable using cross-normalized SAR data. Full article
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
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Open AccessArticle
Assessing MODIS GPP in Non-Forested Biomes in Water Limited Areas Using EC Tower Data
Remote Sens. 2015, 7(3), 3274-3292; https://doi.org/10.3390/rs70303274 - 20 Mar 2015
Cited by 2
Abstract
Although shrublands, savannas and grasslands account for 37% of the world’s terrestrial area, not many studies have analysed the role of these ecosystems in the global carbon cycle at a regional scale. The MODIS Gross Primary Production (GPP) product is used here to [...] Read more.
Although shrublands, savannas and grasslands account for 37% of the world’s terrestrial area, not many studies have analysed the role of these ecosystems in the global carbon cycle at a regional scale. The MODIS Gross Primary Production (GPP) product is used here to help bridge this gap. In this study, the agreement between the MODIS GPP product (GPPm) and the GPP Eddy Covariance tower data (GPPec) was tested for six different sites in temperate and dry climatic regions (three grasslands, two shrublands and one evergreen forest). Results of this study show that for the non-forest sites in water-limited areas, GPPm is well correlated with GPPec at annual scales (r2 = 0.77, n = 12; SEE = 149.26 g C∙m−2∙year−1), although it tends to overestimate GPP and it is less accurate in the sites with permanent water restrictions. The use of biome-specific models based on precipitation measurements at a finer spatial resolution than the Data Assimilation Office (DAO) values can increase the accuracy of these estimations. The seasonal dynamics and the beginning and end of the growing season were well captured by GPPm for the sites where (i) the productivity was low throughout the year or (ii) the changes in the flux trend were abrupt, usually due to the restrictions in water availability. The agreement between GPPec and GPPm in non-forested sites was lower on a weekly basis than at an annual scale (0.44 ≤ r2 ≤ 0.49), but these results were improved by including meteorological data at a finer spatial scale, and soil water content and temperature measurements in the model developed to predict GPPec (0.52 ≤ r2 ≤ 0.65). Full article
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
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Open AccessArticle
Performance of Linear and Nonlinear Two-Leaf Light Use Efficiency Models at Different Temporal Scales
Remote Sens. 2015, 7(3), 2238-2278; https://doi.org/10.3390/rs70302238 - 25 Feb 2015
Cited by 10
Abstract
The reliable simulation of gross primary productivity (GPP) at various spatial and temporal scales is of significance to quantifying the net exchange of carbon between terrestrial ecosystems and the atmosphere. This study aimed to verify the ability of a nonlinear two-leaf model (TL-LUEn), [...] Read more.
The reliable simulation of gross primary productivity (GPP) at various spatial and temporal scales is of significance to quantifying the net exchange of carbon between terrestrial ecosystems and the atmosphere. This study aimed to verify the ability of a nonlinear two-leaf model (TL-LUEn), a linear two-leaf model (TL-LUE), and a big-leaf light use efficiency model (MOD17) to simulate GPP at half-hourly, daily and 8-day scales using GPP derived from 58 eddy-covariance flux sites in Asia, Europe and North America as benchmarks. Model evaluation showed that the overall performance of TL-LUEn was slightly but not significantly better than TL-LUE at half-hourly and daily scale, while the overall performance of both TL-LUEn and TL-LUE were significantly better (p < 0.0001) than MOD17 at the two temporal scales. The improvement of TL-LUEn over TL-LUE was relatively small in comparison with the improvement of TL-LUE over MOD17. However, the differences between TL-LUEn and MOD17, and TL-LUE and MOD17 became less distinct at the 8-day scale. As for different vegetation types, TL-LUEn and TL-LUE performed better than MOD17 for all vegetation types except crops at the half-hourly scale. At the daily and 8-day scales, both TL-LUEn and TL-LUE outperformed MOD17 for forests. However, TL-LUEn had a mixed performance for the three non-forest types while TL-LUE outperformed MOD17 slightly for all these non-forest types at daily and 8-day scales. The better performance of TL-LUEn and TL-LUE for forests was mainly achieved by the correction of the underestimation/overestimation of GPP simulated by MOD17 under low/high solar radiation and sky clearness conditions. TL-LUEn is more applicable at individual sites at the half-hourly scale while TL-LUE could be regionally used at half-hourly, daily and 8-day scales. MOD17 is also an applicable option regionally at the 8-day scale. Full article
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
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