Junlei Tan

Validation of MODIS-GPP product at 10 flux sites in northern China

Gross primary production (GPP) is an important variable in studies of the carbon cycle and climate change. The Moderate Resolution Imaging Spectroradiometer (MODIS)-GPP product (MOD17) provides global GPP data for terrestrial ecosystems; however, it is not well validated in China. In this study, an eddy covariance (EC) system observed GPP at 10 sites in northern China and was used to validate MOD17. The results indicated that MOD17 presents a strong bias in the study region due to the meteorological data, MODIS FPAR (fraction of absorbed photosynthetically active radiation) (MOD15), and the model parameters in the MODIS-GPP algorithm, Biome Parameters Look Up Table (BPLUT). Maximum light-use efficiency (ϵ0) had the strongest impact on the predicted GPP of the MODIS-GPP algorithm. After using the inputs observed in situ and improving parameters in the MODIS-GPP algorithm, the model could explain 85% of the EC-observed GPP of the sites, whereas the MODIS-GPP algorithm without in situ inputs and parameters only explained 26% of EC-observed GPP.

Evaluation of MODIS LST Products Using Longwave Radiation Ground Measurements in the Northern Arid Region of China

This study presents preliminary results of the validation of the Moderate Resolution Imaging Spectroradiometer (MODIS) daily LST products (MOD/MYD11A1, Version 5) using longwave radiation ground measurements obtained at 12 stations in the North Arid and Semi-Arid Area Cooperative Experimental Observation Integrated Research program. In this evaluation process, the broadband emissivity at each station was obtained from the ASTER Spectral Library or estimated from the MODIS narrowband emissivity Collection 5. A comparison of the validation results based on those two methods shows that no significant differences occur in the short-term validation, and a sensitivity analysis of the broadband emissivity demonstrates that it has a limited effect on the evaluation results. In general, the results at the 12 stations indicate that the LST products have a lower accuracy in China’s arid and semi-arid areas than in other areas, with a mean absolute error of 2–3 K. Compared with the temporal mismatch, the spatial mismatch has a stronger effect on the validation results in this study, and the stations with homogeneous land cover have more comparable MODIS LST accuracies. Comparisons between the stations indicate that the spatial mismatch can increase the influence of the temporal mismatch.

Validation of MODIS land surface temperature products using ground measurements in the Heihe River Basin, China

It is very necessary to validate MODIS land surface temperature (LST) for its application, especially in the arid and semi-arid regions. In this study, the Terra and Aqua MODIS 1km daily LST products (MOD/MYD11A1) are validated using ground based longwave radiation observation. The longwave radiation ground measurements during 2008 to 2009 were collected from four automatic weather stations in the Heihe River Basin. In this validation process, the land surface broadband emissivities of the validation stations were obtained from ASTER Spectral library. Then the ground-measured LSTs of validation stations were converted from surface longwave radiation based on Stefan-Boltzmanns law and thermal radioactive transfer theory. The validation results indicated that: except for DYKGT station, the mean bias was less then 1K and the mean absolute error (MAE) range was about 2-3K; MYD11A1 LSTs from Aqua have larger biases, MAEs, and RMSDs than that of MOD11A1 LSTs from Terra in most cases. The comparisons with ground measured LSTs show that the MAEs and RMSDs from daytime MOD/MYD11A1 comparisons are larger than that from nighttime MOD/MYD11A1 comparisons.

Seasonal variation of vegetation productivity over an alpine meadow in the Qinghai–Tibet Plateau in China: modeling the interactions of vegetation productivity, phenology, and the soil freeze–thaw process

Phenology controls the seasonal activities of vegetation on land surfaces and thus plays a fundamental role in regulating photosynthesis and other ecosystem processes. Therefore, accurately simulating phenology and soil processes is critical to ecosystem and climate modeling. In this study, we present an integrated ecosystem model of plant productivity, plant phenology, and the soil freeze–thaw process to (1) improve the quality of simulations of soil thermal regimes and (2) estimate the seasonal variability of plant phenology and its effects on plant productivity in high-altitude seasonal frozen regions. We tested different model configurations and parameterizations, including a refined soil stratification scheme that included unfrozen water in frozen soil, a remotely sensed diagnostic phenology scheme, and a modified prognostic phenology scheme, to describe the seasonal variation in vegetation. After refined soil layering resolution and the inclusion of unfrozen water in frozen soil, the results show that the model adequately reproduced the soil thermal regimes and their interactions observed at the site. The inclusion of unfrozen water in frozen soil was found to have a significant effect on soil moisture simulation during the spring but only a small effect on soil temperature simulation at this site. Moreover, the performance of improved phenology schemes was good. The phenology model accurately predicted the start and end of phenology, and its precise prediction of phenology variation allows an improved simulation of vegetation production.

Estimating the land-surface temperature of pixels covered by clouds in MODIS products

Abstract This study implements the “neighboring-pixel” (NP) theoretical method, which uses spatially and temporally NPs to reconstruct cloud-contaminated pixels in daily Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST) products. The 2012 MODIS LSTs of the Heihe River Basin (HRB) region in China are used as an example, and the ground-measured LSTs obtained at 17 sites from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project are used to validate the reconstruction results. The results show a bias of 0.25 K and RMSE of 4.122 K during the day and a bias of − 0.1263     K and RMSE of 2.901     K at night. The error analysis reveals an uncertainty in the estimation of the cloud-contaminated pixels that can be attributed to errors in the estimation of parameters and net solar radiation retrieval and inaccuracies inherent in the NP scheme. The analysis results reveal that the time-gap effect is the main cause of uncertainty in the nighttime reconstruction, whereas the large extreme cases for the daytime reconstruction are generally caused by strong convection systems that usually occur with heavy precipitation in the cloud-contaminated pixels. Despite the uncertainty, the proposed approach is promising for the improvement of MODIS LST application in practice.

New Scheme for Validating Remote-Sensing Land Surface Temperature Products with Station Observations

Continuous land-surface temperature (LST) observations from ground-based stations are an important reference dataset for validating remote-sensing LST products. However, a lack of evaluations of the representativeness of station observations limits the reliability of validation results. In this study, a new practical validation scheme is presented for validating remote-sensing LST products that includes a key step: assessing the spatial representativeness of ground-based LST measurements. Three indicators, namely, the dominant land-cover type (DLCT), relative bias (RB), and average structure scale (ASS), are established to quantify the representative levels of station observations based on the land-cover type (LCT) and LST reference maps with high spatial resolution. We validated MODIS LSTs using station observations from the Heihe River Basin (HRB) in China. The spatial representative evaluation steps show that the representativeness of observations greatly differs among stations and varies with different vegetation growth and other factors. Large differences in the validation results occur when using different representative level observations, which indicates a large potential for large error during the traditional T-based validation scheme. Comparisons show that the new validation scheme greatly improves the reliability of LST product validation through high-level representative observations.

Coupling of a biogeochemical model with a simultaneous heat and water model and its evaluation at an alpine meadow site

Alpine meadow covers most of the Qinghai-Tibet Plateau where frozen soil is widely distributed. In order to correctly simulate the carbon, water and energy flux of an alpine meadow site at Qinghai-Tibet Plateau, a widely used carbon cycle model Biome-BGC and a cold region land surface model SHAW were coupled. The outputs of the coupled model were validated with the observed carbon fluxes (Gross Primary Productivity, Net Ecosystem Exchange, Ecosystem Respiration), energy fluxes (Latent heat flux, Sensible heat flux), water flux (Evapotranspiration), soil moisture and soil temperature at A’rou site which is located on the east edge of Qinghai-Tibet Plateau. The results indicate that the coupled model can correctly predict the interactions between alpine meadow ecosystem and atmosphere.

A comparison of two photosynthesis parameterization schemes for an alpine meadow site on the Qinghai-Tibetan Plateau

Photosynthesis is a very important sub-process in the carbon cycle and is a crucial sub-modular function in carbon cycle models. In this study, two typical photosynthesis parameterization schemes were compared based on meteorological and eddy covariance (EC) observations at an alpine meadow site. The photosynthesis model parameters were estimated using the Markov Chain Monte Carlo (MCMC) method. The results indicated that the Farquhar-conductance coupled model better predicted the gross primary production (GPP) for the alpine meadow ecosystem at an hourly time scale than the light use efficiency (LUE) model even though the Farquhar-conductance coupled model has a lower computational efficiency than the LUE model. Compared to the Ball–Woodrow–Berry (BWB) stomatal conductance model, coupling the Farquhar model with the Leuning stomatal conductance model more accurately simulated GPP.

The reconstruction of MODIS land surface temperature products using NSSR

Land surface temperature (LST) is a key parameter in climatological and environmental studies [1]. The Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the NASA Terra and Aqua Earth Observing System satellites, can provide global temperature and narrowband emissivity data on a daily basis. However, when the surface is obscured by clouds, the variable cannot be measured directly by using satellite thermal infrared channels, which leads to many invalid value pixels in the MODIS LST products. Methods for calculating LST of the MODIS cloudy pixels are important, yet few studies have been done. The objective of this paper is to estimate the LST values of the cloudy-pixels using the neighboring-pixel approach (NP) and MODIS NSSR (net surface shortwave radiation) product. In this study, the Heihe River Basin was selected as a case study area. The estimation was validated using ground-measured data of Huazhaizi (HZZ) desert station which is covered by homogeneous desert steppe. The validation shows that the reconstruction values of MODIS LSTs can agree well with the ground-measured data, and the biggest absolute error is 2.6K.

GPP estimation over Heihe River Basin, China

Gross Primary Production (GPP) is the sum of carbon absorbed by plant canopy. It is a key measurement of carbon mass flux in carbon cycle studies. Remote sensing based light use efficiency model is a widely used method to estimate regional GPP. In this study, MODIS-PSN was used to estimate GPP in Heihe River Basin. In order to better the model accuracy, maximum light use efficiency (ε0) in MODIS-PSN is estimated using local observed carbon flux data and meteorological data. After adjustment of parameter ε0, MODIS-PSN can correctly estimate GPP for major vegetation type in the Heihe River Basin. Then, yearly GPP over Heihe River Basin was estimated. The results indicated that about 1.4*1013g carbon enter terrestrial ecosystem through vegetation photosynthesis in the Heihe River Basin one year. In contrast, there is just 5.73*1013g carbon enter terrestrial ecosystem according to the standard MODIS GPP product, which is greatly underestimated GPP in the Heihe River Bain.