Moderate Resolution Imaging Spectroradiometer Products Research Articles

Estimating the Columnar Concentrations of Black Carbon Aerosols in China Using MODIS Products.

Black carbon (BC), the strongest light-absorbing particle, is believed to play substantial roles in regional air quality and global climate change. In this study, taking advantage of the high quality of moderate resolution imaging spectroradiometer products, we developed a new algorithm to estimate the BC columnar concentrations over China by simulating the BC and non-BC aerosol mixing states in detail. The results show that our new algorithm produces a reliable estimation of BC aerosols, in which BC columnar concentrations and their related parameters (aerosol absorption and BC surface concentration) show reasonable agreements and low biases compared with ground-based measurements. The uncertainties of BC retrievals are mainly associated with the surface and aerosol assumptions used in the algorithm, ranging from -14 to 44% at higher aerosol optical depth (AOD > 0.5). The proposed algorithm can improve the capability of space-borne aerosol remote sensing by successfully distinguishing BC from other aerosols. The acquired BC columnar concentrations enable the spatial pattern of serious BC aerosol pollution over East China to be characterized, showing that it exhibits higher levels in winter. These nationwide results are beneficial for estimating BC emissions, proposing mitigation strategies for air pollution, and potentially reducing the uncertainties of climate change studies.

A multinomial logistic model-based land use and land cover classification for the South Asian Association for Regional Cooperation nations using Moderate Resolution Imaging Spectroradiometer product

The land-use and land-cover (LULC) mapping is crucial for the management and planning of a nation. The land use and land cover are the two separate terminologies often used interchangeably. We mapped LULC for the nations of South Asian Association for Regional Cooperation represented by Afghanistan, Bangladesh, Bhutan, India, Nepal, the Maldives, Pakistan and Sri Lanka covering an area of ca. 5,100,000 km2. Mapping LULC is a cumbersome process for a larger area; nevertheless, in recent decades the satellite-based Earth observation has provided a great impetus and various approaches have been adopted to map LULC. In this study, we used the multinomial logit model (Mnlogit) to classify Moderate Resolution Imaging Spectroradiometer (MODIS)-derived time-series data for mapping LULC. The MODIS-archived images for the years 2004–2007 and 2016 were used to map LULC for corresponding years. For the same years, maps were available from IGBP, FAO and NR Census, India, for comparison; hence, these years were selected. The classification of images for each of the years was found to be more consistent and accurate in comparison with the existing LULC maps of IGBP and FAO, and hence a robust approach. The method can be adopted to map global resources. We achieved overall accuracy ranging from 76 to 82% with kappa statistic (K-hat) ranging from 0.64 to 0.73, which is better than previous works. The study demonstrates an effective way to utilize remote sensing data for mapping LULC for a larger area with reasonable accuracy.

Improving on MODIS MCD64A1 Burned Area Estimates in Grassland Systems: A Case Study in Kansas Flint Hills Tall Grass Prairie

Uncertainty in satellite-derived burned area estimates are especially high in grassland systems, which are some of the most frequently burned ecosystems in the world. In this study, we compare differences in predicted burned area estimates for a region with the highest fire activity in North America, the Flint Hills of Kansas, USA, using the moderate resolution imaging spectroradiometer (MODIS) MCD64A1 burned area product and a customization of the MODIS MCD64A1 product using a major ground-truthing effort by the Kansas Department of Health and Environment (KDHE-MODIS customization). Local-scale ground-truthing and the KDHE-MODIS product suggests MODIS burned area estimates under predicted fire occurrence by 28% over a 19-year period in the Flint Hills ecoregion. Between 2001 and 2019, MODIS product indicated <1 million acres burned on average, which was far below the KDHE-MODIS customization (mean = 2.6 million acres). MODIS also showed that <1% of the Flint Hills burned 5 times from 2001–2019 (2001, 2002, 2007, 2012 and 2013), whereas KDHE-MODIS customization showed this never happened in any single year. KDHE-MODIS also captured some areas of the Flint Hills that burned every year (19 times out of 19 years), which is well-known with field inventory data, whereas the maximum fire occurrence in MODIS was 14 times in 19 years. Finally, MODIS never captured >8% burned area for any given year in the Flint Hills, even in years when fire activity was highest (2008, 2009, 2011, 2014). Based on these results, coupling MODIS burned area computations with local scale ground-truth efforts has the potential to significantly improve fire occurrence estimates and reduce uncertainty in other grassland and savanna regions.

Spatiotemporal Distribution of Major Aerosol Types over China Based on MODIS Products between 2008 and 2017

Knowledge of aerosol-type distribution is critical to the evaluation of aerosol–climate effects. However, research on aerosol-type distribution covering all is limited. This study characterized the spatiotemporal distribution of major aerosol types over China by using MODerate resolution Imaging Spectroradiometer (MODIS) products from 2008 to 2017. Two aerosol-type classification methods were combined to achieve this goal. One was for relatively high aerosol load (AOD ≥ 0.2) using aerosol optical depth (AOD) and aerosol relative optical depth (AROD) and the other was for low aerosol load (AOD < 0.2) using land use and population density information, which assumed that aerosols are closely related to local emissions. Results showed that the dominant aerosol-type distribution has a distinct spatial and temporal pattern. In western China, background aerosols (mainly dust/desert dust and continent aerosol) dominate with a combined occurrence ratio over 70% and they have slight variations on seasonal scale. While in eastern China, the dominant aerosols show strong seasonal variations. Spatially, mixed aerosols dominate most parts of eastern China in spring due to the influence of long-range transported dust from Taklamakan and Gobi desert and urban/industry aerosols take place in summer due to strong photochemical reactions. Temporally, mixed and urban/industry aerosols co-dominate eastern China.

Drought Risk Assessment in Cultivated Areas of Central Asia Using MODIS Time-Series Data

Drought is one of the most damaging environmental hazards and a naturally occurring phenomenon in Central Asia that is accompanied by crucial consequences for the agriculture sector. This research aimed at understanding the nature and extent of drought over the cropland regions of Central Asia with the help of spatiotemporal information from the region. We assessed drought occurrence using the vegetation health index (VHI). An algorithm was developed to reduce the noise of heterogeneous land surfaces by adjusting the vegetation index and brightness temperature. The vegetation condition index (VCI) and temperature condition index (TCI) were calculated using Moderate Resolution Imaging Spectroradiometer (MODIS) products for the growing season (April–September) from 2000 to 2015. The intense drought years were identified and a drought map (drought probability occurrence) was generated. The findings of this research indicated regional heterogeneity in the cropland areas having experienced droughts, observed through spatiotemporal variations. Some of the rain-fed and irrigated croplands of Kazakhstan demonstrated a higher vulnerability to annual drought occurrences and climate change impacts, while other cropland regions were found to be more resistant to such changes. The development of policy tools is required to support informed decision-making and planning processes to adapt to the occurrence of droughts. This could be achieved by the timely assessment, monitoring, and evaluation of the spatiotemporal distribution trends and variabilities of drought occurrences in this region. The results from this study focus on the spatiotemporal variations in drought to reveal the bigger picture in order to better understand the regional capacity for sustainable land management and agricultural activities within a changing environment.

Retrieval of Soil Moisture by Integrating Sentinel-1A and MODIS Data over Agricultural Fields

Soil moisture is an essential variable in the land surface ecosystem, which plays an important role in agricultural drought monitoring, crop status monitoring, and crop yield prediction. High-resolution radar data can be combined with optical remote-sensing data to provide a new approach to estimate high-resolution soil moisture over vegetated areas. In this paper, the Sentinel-1A data and the Moderate Resolution Imaging Spectroradiometer (MODIS) data are combined to retrieve soil moisture over agricultural fields. The advanced integral equation model (AIEM) is utilized to calculate the scattering contribution of the bare soil surface. The water cloud model (WCM) is applied to model the backscattering coefficient of vegetated areas, which use two vegetation parameters to parameterize the scattering and attenuation properties of vegetation. Four different vegetation parameters extracted from MODIS products are combined to predict the scattering contribution of vegetation, including the leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), normalized difference vegetation index (NDVI), and the enhanced vegetation index (EVI). The effective roughness parameters are chosen to parameterize the AIEM. The Sentinel-1A and MODIS data in 2017 are used to calibrate the coupled model, and the datasets in 2018 are used for soil moisture estimation. The calibration results indicate that the Sentinel-1A backscattering coefficient can be accurately predicted by the coupled model with the Pearson correlation coefficient (R) ranging from 0.58 to 0.81 and a root mean square error (RMSE) ranging from 0.996 to 1.401 dB. The modeled results show that the retrieved soil moisture can capture the seasonal dynamics of soil moisture with R ranging from 0.74 to 0.81. With the different vegetation parameter combinations used for parameterizing the scattering contribution of the canopy, the importance of suitable vegetation parameters for describing the scattering and attenuation properties of vegetation is confirmed. The LAI is recommended to characterize the scattering properties. There is no obvious clue for selecting vegetation descriptors to characterize the attenuation properties of vegetation. These promising results confirm the feasibility and validity of the coupled model for soil moisture retrieval from the Sentinel-1A and MODIS data.

Assessment of Fire Fuel Load Dynamics in Shrubland Ecosystems in the Western United States Using MODIS Products

Assessing fire behavior in shrubland/grassland ecosystems of the western United States has proven especially problematic, in part due to the complex nature of the vegetation and its relationships with prior fire history events. Our goals in this study were (1) to determine if we can effectively leverage the high temporal resolution capabilities of current remote sensing systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve upon shrub and grassland mapping and (2) to determine if these improvements alter and improve fire behavior model results in these grass- and shrub-dominated systems. The study focused on the shrublands and grasslands of the Owyhee Basin, which is located primarily in southern Idaho. Shrubland and grassland fuel load dynamics were characterized using Normalized Difference Vegetation Index (NDVI) and Net Primary Production (NPP) datasets (both derived from MODIS). NDVI shrub and grassland values were converted to biomass, and custom fire behavior fuel models were then developed to evaluate the impacts of surface fuel changes on fire behaviors. Results from the study include the following: (1) high intra- and interannual spectral variability characterized these shrubland/grassland ecosystems, and this spectral variability was highly correlated with climate variables, most notably precipitation; (2) fire activity had a higher likelihood of occurring in areas where the NDVI (and biomass) differential between spring and summer values was especially high; (3) the annual fuel loads estimated from MODIS NPP showed that live herbaceous fuel loads were closely correlated with annual precipitation; (4) estimated fuel load accumulation was higher on shrublands than grasslands with the same vegetation productivity; (5) the total fuel load on shrublands was impacted by shrubland age, and live woody fuel load was over 66% of the total fuel load; and (6) comparisons of simulated fire behavior and spread between dynamic and static fuel loads, the latter estimates being obtained from the operational and nationwide LANDFIRE program, showed clear differences in fire indices and fire burn areas between the dynamic fuel loads and the static fuel loads. Current standard fuel models appear to have bias in underestimating the fire spread and total burnable area.

Evaluation of Two Global Land Surface Albedo Datasets Distributed by the Copernicus Climate Change Service and the EUMETSAT LSA-SAF

The present paper is devoted to the quality assessment of two global land surface albedo products developed by Meteo France in the frame of the Copernicus Climate Change Service (C3S) and the LSA-SAF (Satellite Application Facility on Land Surface Analysis), herein called, respectively, VGT (VeGeTation) (the C3Sv1 dataset, derived from VGT sensors onboard Satellites for the Observation of the Earth, also called SPOT) and ETAL (European polar system Ten-day surface ALbedo, derived from Advanced Very High Resolution Radiometers (AVHRR) onboard METeorological OPerational (METOP) satellites). The evaluation study inter-compared these products with measurements at 33 ground stations and two independent operational products, MTAL-R/NRT (Meteosat second generation Ten-day ALbedo Reprocessed/Near Real-Time) and MODIS (MODerate resolution Imaging Spectroradiometer), over two distinct four-year periods. In accordance with the prescription from the Land Product Validation group of the joint Committee on Earth Observation Satellites (LPV/CEOS), the evaluation was addressed per land cover; furthermore, two albedo regimes were considered throughout the evaluation to distinguish between high (over 0.15) and low (below 0.15) surface albedo behaviors. First, we show that both VGT and ETAL products agree well with the measurements and the other satellite products at the ground stations. Second, when inter-compared with MODIS, the results are noteworthy for ETAL as opposed to VGT, with 11 out of 13 land cover types passing the Global Climate Observing System (GCOS) requirements for more than 80% of the sites for albedo values less than 0.15 (compared with none for VGT) and 10 out of 14 land cover types passing the GCOS requirements for more than 50% of the sites for albedo values greater than 0.15 (compared with 5 for VGT). Finally, a pixel-by-pixel analysis reveals that VGT overestimates the surface albedo as compared with MODIS by about 0.02 in absolute value for albedo values less than 0.15 and by about 22% in relative value for albedo values greater than 0.15. The root-mean-square-deviation (RMSD) in absolute value is about 0.015 for albedo values less than 0.15 and 51.5% in relative value for albedo values greater than 0.15. In contrast, the bias for ETAL when compared with MODIS remains very small. Over the four-year period, ETAL overestimates the surface albedo as compared with MODIS by 0.001 in absolute value for the regime of surface albedo less than 0.15 and by about 5.8% in relative value for albedo values greater than 0.15. The RMSD in absolute value is about 0.014 for albedo values less than 0.15 and 19.4% in relative value for albedo values greater than 0.15. Assuming that the MODIS product is a good reference, a relative bias of around 6% can be judged satisfactory for ETAL surface albedo. The lower performance of the VGT (C3Sv1) product is currently the subject of investigation. Work is ongoing to upgrade it further towards the final C3S product.

Optimal MODIS data processing for accurate multi-year paddy rice area mapping in China

ABSTRACT Researchers, policy makers, and farmers currently rely on remote sensing technology to monitor crops. Although data processing methods can be different among different remote sensing methods, little work has been done on studying these differences. In order for potential users to have confidence in remote sensing products, an analysis of mapping accuracies and their associated uncertainties with different data processing methods is required. This study used the MOD09A1 and MYD09A1 products of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite, from which the Enhanced Vegetation Index (EVI) and the two-band EVI (EVI2) images were obtained. The objective of this study was to analyze the accuracy of different data processing combinations for multi-year rice area mapping. Sixteen combinations of EVI and EVI2 with two cloudy pixel removal methods (QA/BLUE) and four pixel replacement methods (MO/MY/MOY/MYO) were investigated over the Jiangsu Province of southeast China from 2006 to 2016. Different accuracy results were obtained with different data processing combinations for multi-year rice field mapping. Based on a comparison of the relative performance of different MODIS products and processing method combinations, EVI2_BLUE_MYO was proposed to be the optimal processing method, and was applied to forecasting the rice-planted area of 2017. Study results from 2006 to 2017 were validated against reference data and showed accuracies of rice area extraction of greater than 95%. The mean absolute error of transplanting, heading, and maturity dates were 11.55, 8.10, and 7.78 days, respectively. In 2017, two sample regions (A and B) were selected from places where rice fractional cover was greater than 75%. Rice area extraction accuracies of 85.0% (A) and 92.3% (B) were obtained. These results demonstrated the complementarity of MOD09A1 and MYD09A1 datasets in enhancing pixel spatial coverage and improving rice area mapping when atmospheric influences are significant. The optimal data processing combination indentified in this study is promising for accurate multi-year and large-area paddy rice information extraction and forecasting.

Evaluation of Six High-Spatial Resolution Clear-Sky Surface Upward Longwave Radiation Estimation Methods with MODIS

Surface upward longwave radiation (SULR) is a critical component in the calculation of the Earth’s surface radiation budget. Multiple clear-sky SULR estimation methods have been developed for high-spatial resolution satellite observations. Here, we comprehensively evaluated six SULR estimation methods, including the temperature-emissivity physical methods with the input of the MYD11/MYD21 (TE-MYD11/TE-MYD21), the hybrid methods with top-of-atmosphere (TOA) linear/nonlinear/artificial neural network regressions (TOA-LIN/TOA-NLIN/TOA-ANN), and the hybrid method with bottom-of-atmosphere (BOA) linear regression (BOA-LIN). The recently released MYD21 product and the BOA-LIN—a newly developed method that considers the spatial heterogeneity of the atmosphere—is used initially to estimate SULR. In addition, the four hybrid methods were compared with simulated datasets. All the six methods were evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Surface Radiation Budget Network (SURFRAD) in situ measurements. Simulation analysis shows that the BOA-LIN is the best one among four hybrid methods with accurate atmospheric profiles as input. Comparison of all the six methods shows that the TE-MYD21 performed the best, with a root mean square error (RMSE) and mean bias error (MBE) of 14.0 and −0.2 W/m2, respectively. The RMSE of BOA-LIN, TOA-NLIN, TOA-LIN, TOA-ANN, and TE-MYD11 are equal to 15.2, 16.1, 17.2, 21.2, and 18.5 W/m2, respectively. TE-MYD21 has a much better accuracy than the TE-MYD11 over barren surfaces (NDVI < 0.3) and a similar accuracy over non-barren surfaces (NDVI ≥ 0.3). BOA-LIN is more stable over varying water vapor conditions, compared to other hybrid methods. We conclude that this study provides a valuable reference for choosing the suitable estimation method in the SULR product generation.

Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany

This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (<600 g/m2) and significant p-value (<0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 (<0.68) and high RMSE (>600 g/m2). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).

Evaluating the potential for application of MODIS satellite data for urban air quality monitoring in Vietnam

Using satellite remote sensing data in combining with other technologies (i.e. sensor, ground-based system) for surface air quality monitoring has been considered as one of promising approaches for urban air quality management in future smart cities. This study assessed the accuracy of latest collection (Collection 6.1 - C6.1) of MODerate resolution Imaging Spectroradiometer (MODIS) satellite aerosol optical depth (AOD) retrieval by Dark Target (DT) algorithm at 3 and 10 km spatial resolutions in urban areas in Vietnam. Collocated Aqua and Terra MODIS C6.1 AOD at 550 nm were inter-compared against AOD from two AERONET stations in Vietnam, estimated throughout the satellite passes (± 1 h). Two investigated AERONET stations are Nghia Do (inland urban) and Bac Lieu (coastal urban) with the studying periods of 2010 - 2016 and 2010 - 2017, respectively. Our findings showed that the MODIS DT 3km were performed worse than the MODIS DT 10 km product in urban areas. Furthermore, all two MODIS AOD DT products showed better performance at inland urban site than coastal urban site. In general, both MODIS products were able to follow the monthly-mean AERONET AODs. The peak of aerosol loading at the investigated urban sites could be properly attributed to the combination of the monsoon effect and biomass burning activities. The mean aerosol loading of rainy season appeared to be lowest in the year, which could be attributed to the fact that the frequent rain washes out the dust in the atmosphere.

Recent declines in global water vapor from MODIS products: Artifact or real trend?

Atmospheric water vapor plays a key role in the global water and energy cycles. Accurate estimation of water vapor and consistent representation of its spatial-temporal variation are critical to climate analysis and model validation. This study used ground observational data from global radiosonde and sunphotometer networks to evaluate MODIS (MODerate-resolution Imaging Spectroradiometer) precipitable water vapor (PWV) products for 2000–2017. The products included the thermal-infrared (TIR) (Collection 6, C006) and its updated version (Collection 061, C061), and near-infrared (NIR) products (C061). Our results demonstrated that compared to its earlier version subject to sensor crosstalk problem, the C061_TIR data showed improved accuracy in terms of bias, standard deviation, mean absolute error, root mean square error, and coefficient of determination, regression slope and intercept. Among the PWV products, C061_NIR data achieved the best overall performance in accuracy evaluation. The C061_NIR revealed the PWV had a multi-year average of 2.50 ± 0.08 cm for the globe, 2.03 ± 0.06 cm for continents, and 2.70 ± 0.09 cm for oceans in 2000–2017. The PWV values yielded an increasing rate of 0.015 cm/year for the globe, 0.010 cm/year for continents, and 0.017 cm/year for oceans. Nearly 98.95% of the globe showed an increasing trend, 80.74% of statistical significance, mainly distributed within and around the tropical zones. The findings should be valuable for understanding of global water and energy cycles.

Fractional evergreen forest cover mapping by MODIS time-series FEVC-CV methods at sub-pixel scales

Evergreen forest plays a particular role in biodiversity, carbon sequestration, and soil and water conservation, and the spatial and temporal evolutions of evergreen forest complexly interact with climate change. However, it is difficult for the existing remote sensed data to meet both demands of large scale and high accuracy. To solve this problem, we developed a time-series FEVC-CV method by combining the fractional evergreen forest cover model (FEVC) with the coefficient of variation (CV) at sub-pixel scales. This method using the normalized difference vegetation index (NDVI) dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS) product to meet the needs of large-scale mapping. Considering the severe mixed-pixel problem and spatial heterogeneity of the study area, the improved dimidiate pixel model was used to extract the fractional evergreen forest cover at sub-pixel scales by introducing a new variable the intra-annual NDVI minimum value (NDVIann-min) and dividing grid units. Meanwhile, the CV of the time-series NDVI highlighted the time series fluctuation stability of evergreen forest compared with other vegetation types, such as deciduous forest and continuous crop. Therefore, the intra-annual time-series CV (CVai) and the CV of the continuous crop key phenology (CVkp) period were used to eliminate the interferences from other vegetation types, which were extremely admixed with low-coverage evergreen forest. We then verified the accuracy of the algorithm using 2-m resolution Gaofen-1 images. The results revealed that the overall accuracy of our algorithm exceeded 90%, with a root mean square error (RMSE) of cover fraction of around 10%. In addition, the mean relative error (MRE) indicated that the extraction accuracy of evergreen forest in non-urban areas was superior to the accuracy in urban areas. The results show that the algorithm achieved fairly high accuracy in detecting evergreen forest, including evergreen trees in urban areas, at a large scale.

Comparative evaluation of VIIRS daily snow cover product with MODIS for snow detection in China based on ground observations

Accurate spatiotemporal information of snow cover not only is important for investigating the mechanisms of climate change but also greatly contributes to hydrological modelling in mountainous regions. The Suomi-National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (referred to as VNP) daily snow cover product is recently released and expected to take place of Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products in near future. As an important addition to the widely used MODIS products, there is also an urgent need for a reliable accuracy evaluation and comparison of VNP for future large-scale daily snow cover mapping. This study for the first time evaluates the accuracy of VNP daily snow cover data in China using daily snow depth observations from 330 stations. The accuracy of VNP data is generally good with the averaged CK (Cohen's Kappa) and FS (F-Score) as high as 0.72 and 0.75, respectively, but considerably decreases to 0.50 and 0.52 for the Tibetan Plateau. VNP shows slightly better accuracy than MODIS TERRA for stations outside the Tibetan Plateau owing to its higher spatial resolution, but its accuracy is lower than TERRA for those within the Tibetan Plateau possibly due to its longer time interval between ground observation and satellite overpass time. By contrast, VNP shows much better accuracy than MODIS AQUA in China including both outside and within the Tibetan Plateau. This study provides important implications for optimal use of VNP and MODIS daily snow cover products in China, which may further contribute to more accurate snow variation information for climate analysis and cryospheric hydrological modelling.

Human Activities Enhance Radiation Forcing through Surface Albedo Associated with Vegetation in Beijing

The impact of human activities on vegetation has been the focus of much research, but the impact on radiation energy through surface albedo associated with vegetation greenness and length of the growth season is still not well documented. Based on the land cover data for the years 2000 and 2015, this study first divided the land cover change in Beijing from 2000 to 2015 into five types according to the impact of human activities and vegetation resilience, namely, old urban areas (OU), urban expansion areas (UE), cropland (CP), mixed pixel areas (MP, which means the land covers other than urban expansion which had changed from 2000 to 2015), and the residual vegetation cover areas (pure pixels (PP), dominated by natural and seminatural vegetation, such as grassland, forest, and wetland). Then, we calculated the direct radiative forcing from the albedo change from 2000 to 2015 and analyzed the effect of vegetation on the albedo under different land cover types based on multi-resource Moderate Resolution Imaging Spectroradiometer (MODIS) products of vegetation, albedo, and solar radiation. The results showed that the most typical changes in land cover were from urban expansion. By comparing the PP with the four human-affected land cover types (OU, UE, MP, and CP), we confirmed that the radiative forcing increment between 2001–2003 and 2013–2015 in PP (0.01 W/m2) was much smaller than that in the four human-affected land cover types (the mean increment was 0.92 W/m2). This study highlights that human activities affected vegetation growth. This, in turn, brought changes in the albedo, thereby enhancing radiative forcing in Beijing during 2000–2015.

An observation-driven optimization method for continuous estimation of soil moisture over large heterogeneous areas

ABSTRACTIn most remote sensing-based soil moisture (SM) retrieval methods, in-situ SM measurements are commonly used for validation purposes. Few studies have investigated whether such measurements can be used for calibration. In this paper, an observation-driven optimization method was proposed to estimate SM from remote sensing observations. Specifically, the optimization method was developed within the surface temperature-vegetation index (TVX) framework for the definition of objective function and constraints. In-situ SM measurements were used to optimize the theoretical boundaries of the TVX feature space. We demonstrated the applicability of the new method with Moderate Resolution Imaging Spectroradiometer (MODIS) products over the Southern Great Plains (SGP) of the United States of America. Results indicate that the accuracy produced using only one site for calibration has reached a level comparable with those produced by traditional methods. Moreover, the method has not only bypassed the complex parameterization of aerodynamic and surface resistance but also achieved continuous monitoring of SM. That is just the capacity that the traditional TVX method does not possess. Therefore, although our optimization method requires the ancillary of in-situ observations, its simplicity proves that it is a useful tool for a quick and continuous monitoring of SM over large heterogeneous areas.

Variation of gross primary production, evapotranspiration and water use efficiency for global croplands

An improved understanding of the productivity and water use efficiency (WUE) of global croplands is essential for coping with the growing demand for food and water. At present, the spatiotemporal distribution and trends of WUE and the associated gross primary production (GPP) and evapotranspiration (ET) for croplands remain poorly quantified at the global scale, especially considering the differences between rainfed and irrigated conditions. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the newly developed Global Food Security-support Analysis Data product of Crop Dominance (GFSAD1KCD) were utilized to quantify and analyze spatiotemporal variation and annual trends in GPP, ET and WUE for global croplands from 2000 to 2014. The analysis was complemented with total water storage data from the Gravity Recovery and Climate Experiment. The results showed that the croplands with the highest GPP had the highest ET. The mean WUE of global croplands was 1.73 g C kg−1 H2O, ranging from a lower mean value of 1.62 g C kg−1 H2O for irrigated croplands to a higher mean value of 1.83 g C kg−1 H2O for rainfed croplands. Irrigated croplands generally had lower WUE than rainfed croplands; this difference decreased during 2000–2014 owing to a significantly increasing trend in WUE for irrigated cropland and a significantly deceasing trend in WUE for rainfed croplands. However, the increase in WUE observed for irrigated croplands did not necessarily overlap with the decrease in groundwater depletion, as indicated by the corresponding and significantly decreasing trend of total water storage. The GFSAD1KCD data proved useful for investigating primary production and WUE for global croplands, especially in comparisons between irrigated and rainfed conditions. Future studies should focus on reducing the uncertainty associated with the distribution of irrigated croplands in order to better quantify and understand WUE on a global scale.

Feasibility of Using MODIS Products to Simulate Sun-Induced Chlorophyll Fluorescence (SIF) in Boreal Forests

Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R2 = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R2 = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature.

Species- and elevation-dependent productivity changes in East Asian temperate forests

The velocity and impact of climate change on forest appear to be site, environment, and tree species-specific. The primary objective of this research is to assess the changes in productivity of five major temperate tree species (Pinus densiflora, PD; Larix kaempferi, LK; Pinus koraiensis, PK; Quercus variabilis, QV; and Quercus mongolica, QM) in South Korea using terrestrial inventory and satellite remote sensing data. The area covered by each tree species was further categorized into either lowland forest (LLF) or high mountain forest (HMF) and investigated. We used the repeated Korean national forest inventory (NFI) data to calculate a stand-level annual increment (SAI). We then compared the SAI, a ground-based productivity measure, to MODerate resolution Imaging Spectroradiometer (MODIS) net primary productivity as a measure of productivity based on satellite imagery. In addition, the growth index of each increment core, which eliminated the effect of tree age on radial growth, was derived as an indicator of the variation in primary productivity by tree species over the past four decades. Based on our result from NFI plots and increment core data sets, the productivity of PD, QV, and QM in LLF was relatively higher than those in HMF, while LK and PK in HMF were more productive than lowland ones. Our analysis of the increment core data revealed a contrasting pattern of long-term productivity changes between coniferous and oak tree species. While the productivity of oak tree species tended to increase after the 1990s, the productivity in coniferous forests tended to decrease. These differences across forest types and their altitudinal classes are also noticeable from the MODIS product. The results of our study can be used to develop climate-smart forest management strategies to ensure that the forests continue to be resilient and continue to provide a wide range of ecosystem services in the Eastern Asian region.