scholarly journals Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation

2021 ◽  
Vol 14 (1) ◽  
pp. 57
Author(s):  
Siyuan Chen ◽  
Lichun Sui ◽  
Liangyun Liu ◽  
Xinjie Liu
Keyword(s):  
Diffuse Radiation ◽  
Terrestrial Ecosystems ◽  
Accurate Estimation ◽  
Data Sets ◽  
Area Index ◽  
Diffuse Fraction ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Meteorological Observations ◽  
Radiation Conditions

Accurate estimation of gross primary productivity (GPP) is necessary to better understand the interaction of global terrestrial ecosystems with climate change and human activities. Light use efficiency (LUE)-based GPP models are widely used for retrieving several GPP products with various temporal and spatial resolutions. However, most LUE-based models assume a clear-sky condition, and the influence of diffuse radiation on GPP estimations has not been well considered. In this paper, a diffuse and direct (DDA) absorbed photosynthetically active radiation (APAR)-based method is proposed for better estimation of half-hourly GPP, which partitions APAR under diffuse and direct radiation conditions. Firstly, energy balance residual (EBR) FAPAR, moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) (MCD15A2H) and clumping index (CI) products, as well as solar radiation records supplied by FLUXNET2015 were used to calculate diffuse and direct APAR at a half-hourly scale. Then, an eddy covariance-LUE (EC-LUE) model and meteorological observations from FLUXNET2015 data sets were used for obtaining corresponding LUE values. A co-variation relationship between LUE and diffuse fraction was observed, and the LUE was higher under more diffuse radiation conditions. Finally, the DDA-based method was tested using the half-hourly FLUXNET GPP and compared with half-hourly GPP calculated using total APAR (GPP_TA). The results indicated that the half-hourly GPP estimated using the DDA-based method (GPP_DDA) was more accurate, giving higher R2 values, lower RMSE and RMSE* values (R2 varied from 0.565 to 0.682, RMSE ranged from 3.219 to 12.405 and RMSE* were within the range of 2.785 to 8.395) than the GPP_TA (R2 varied from 0.558 to 0.653, RMSE ranged from 3.407 to 13.081 and RMSE* were within the range of 3.321 to 9.625) across FLUXNET sites within different vegetation types. This study explored the effects of partitioning the diffuse and direct APAR on half-hourly GPP estimations, which demonstrates a higher agreement with FLUXNET GPP than total APAR-based GPP.

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 Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

2015 ◽  
Vol 8 (10) ◽  
pp. 4025-4041 ◽  
Author(s):  
H.-J. Kang ◽  
J.-M. Yoo ◽  
M.-J. Jeong ◽  
Y.-I. Won
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Data Sets ◽  
High Latitudes ◽  
Ice Surface ◽  
Moderate Resolution ◽  
Microwave Sounding ◽  
Polar Oceans ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ice Surface Temperature

Abstract. Uncertainties in the satellite-derived surface skin temperature (SST) data in the polar oceans during two periods (16–24 April and 15–23 September) 2003–2014 were investigated and the three data sets were intercompared as follows: MODerate Resolution Imaging Spectroradiometer Ice Surface Temperature (MODIS IST), the SST of the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU), and AIRS only. The AIRS only algorithm was developed in preparation for the degradation of the AMSU-A. MODIS IST was systematically warmer up to 1.65 K at the sea ice boundary and colder down to −2.04 K in the polar sea ice regions of both the Arctic and Antarctic than that of the AIRS/AMSU. This difference in the results could have been caused by the surface classification method. The spatial correlation coefficient of the AIRS only to the AIRS/AMSU (0.992–0.999) method was greater than that of the MODIS IST to the AIRS/AMSU (0.968–0.994). The SST of the AIRS only compared to that of the AIRS/AMSU had a bias of 0.168 K with a RMSE of 0.590 K over the Northern Hemisphere high latitudes and a bias of −0.109 K with a RMSE of 0.852 K over the Southern Hemisphere high latitudes. There was a systematic disagreement between the AIRS retrievals at the boundary of the sea ice, because the AIRS only algorithm utilized a less accurate GCM forecast over the seasonally varying frozen oceans than the microwave data. The three data sets (MODIS, AIRS/AMSU and AIRS only) showed significant warming rates (2.3 ± 1.7 ~ 2.8 ± 1.9 K decade−1) in the northern high regions (70–80° N) as expected from the ice-albedo feedback. The systematic temperature disagreement associated with surface type classification had an impact on the resulting temperature trends.

scholarly journals Evaluation of the Simard et al. 2011 Global Canopy Height Map in Boreal Forests

2020 ◽  
Vol 12 (7) ◽  
pp. 1114
Author(s):  
Wei Yang ◽  
Akihiko Kondoh
Keyword(s):  
Boreal Forests ◽  
Forest Canopy ◽  
Spatial Separation ◽  
Canopy Height ◽  
Tree Cover ◽  
Data Sets ◽  
Data Set ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Light detection and ranging (LiDAR) provides a state-of-the-art technique for measuring forest canopy height. Nevertheless, it may miss some forests due to its spatial separation of individual spots. A number of efforts have been made to overcome the limitation of global LiDAR datasets to generate wall-to-wall canopy height products, among which a global satellite product produced by Simard et al. (2011) (henceforth, the Simard-map) has been the most widely applied. However, the accuracy of the Simard-map is uncertain in boreal forests, which play important roles in the terrestrial carbon cycle and are encountering more extensive climate changes than the global average. In this letter, we evaluated the Simard-map in boreal forests through a literature review of field canopy height. Our comparison shows that the Simard-map yielded a significant correlation with the field canopy height (R2 = 0.68 and p < 0.001). However, remarkable biases were observed with the root mean square error (RMSE), regression slope, and intercept of 6.88 m, 0.448, and 10.429, respectively. Interestingly, we found that the evaluation results showed an identical trend with a validation of moderate-resolution imaging spectroradiometer (MODIS) tree-cover product (MOD44B) in boreal forests, which was used as a crucial input data set for generating the Simard-map. That is, both the Simard-map and MOD44B yielded an overestimation (underestimation) in the lower (upper) tails of the scatterplots between the field and satellite data sets. This indicates that the MOD44B product is the likely source of error for the estimation biases of the Simard-map. Finally, a field calibration was performed to improve the Simard-map in boreal forests by compensating for the estimation biases and discarding non-forest areas, which provided a more reliable canopy height product for future applications.

scholarly journals Variations in Winter Surface Temperature of the Purog Kangri Ice Field, Qinghai–Tibetan Plateau, 2001–2018, Using MODIS Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1133
Author(s):  
Yufan Qie ◽  
Ninglian Wang ◽  
Yuwei Wu ◽  
An’an Chen
Keyword(s):  
Tibetan Plateau ◽  
Surface Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Warming Trend ◽  
Data Set ◽  
Moderate Resolution ◽  
Ice Field ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Meteorological Observations

In the context of global warming, the land surface temperature (LST) from remote sensing data is one of the most useful indicators to directly quantify the degree of climate warming in high-altitude mountainous areas where meteorological observations are sparse. Using the daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MOD11A1 V6) after eliminating pixels that might be contaminated by clouds, this paper analyzes temporal and spatial variations in the mean LST on the Purog Kangri ice field, Qinghai–Tibetan Plateau, in winter from 2001 to 2018. There was a large increasing trend in LST (0.116 ± 0.05 °C·a−1) on the Purog Kangri ice field during December, while there was no evident LST rising trend in January and February. In December, both the significantly decreased albedo (−0.002 a−1, based on the MOD10A1 V6 albedo product) on the ice field surface and the significantly increased number of clear days (0.322 d·a−1) may be the main reason for the significant warming trend in the ice field. In addition, although the two highest LST of December were observed in 2017 and 2018, a longer data set is needed to determine whether this is an anomaly or a hint of a warmer phase of the Purog Kangri ice field in December.

scholarly journals Snow Albedo Seasonality and Trend from MODIS Sensor and Ground Data at Johnsons Glacier, Livingston Island, Maritime Antarctica

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3569
Author(s):  
Calleja ◽  
Corbea-Pérez ◽  
Fernández ◽  
Recondo ◽  
Peón ◽  
...  
Keyword(s):  
Moving Average ◽  
Data Sets ◽  
Snow Albedo ◽  
Livingston Island ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Sky Conditions ◽  
Cloud Mask

The aim of this work is to investigate whether snow albedo seasonality and trend under all sky conditions at Johnsons Glacier (Livingston Island, Antarctica) can be tracked using the Moderate Resolution Imaging Spectroradiometer (MODIS) snow albedo daily product MOD10A1. The time span is from December 2006 to February 2015. As the MOD10A1 snow albedo product has never been used in Antarctica before, we also assess the performance for the MOD10A1 cloud mask. The motivation for this work is the need for a description of snow albedo under all sky conditions (including overcast days) using satellite data with mid-spatial resolution. In-situ albedo was filtered with a 5-day windowed moving average, while the MOD10A1 data were filtered using a maximum filter. Both in-situ and MOD10A1 data follow an exponential decay during the melting season, with a maximum decay of 0.049/0.094 day−1 (in-situ/MOD10A1) for the 2006–2007 season and a minimum of 0.016/0.016 day−1 for the 2009–2010 season. The duration of the decay varies from 85 days (2007–2008) to 167 days (2013–2014). Regarding the albedo trend, both data sets exhibit a slight increase of albedo, which may be explained by an increase of snowfall along with a decrease of snowmelt in the study area. Annual albedo increases of 0.2% and 0.7% are obtained for in-situ and MOD10A1 data, respectively, which amount to respective increases of 2% and 6% in the period 2006–2015. We conclude that MOD10A1 can be used to characterize snow albedo seasonality and trend on Livingston Island when filtered with a maximum filter.

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 The importance of temporal collocation for the evaluation of aerosol models with observations

2016 ◽  
Vol 16 (2) ◽  
pp. 1065-1079 ◽  
Author(s):  
N. A. J. Schutgens ◽  
D. G. Partridge ◽  
P. Stier
Keyword(s):  
Remote Sensing Data ◽  
Single Scattering ◽  
Data Sets ◽  
Model Errors ◽  
Model Data ◽  
Sampling Errors ◽  
Temporal Sampling ◽  
Moderate Resolution ◽  
Surface Measurements ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. It is often implicitly assumed that over suitably long periods the mean of observations and models should be comparable, even if they have different temporal sampling. We assess the errors incurred due to ignoring temporal sampling and show that they are of similar magnitude as (but smaller than) actual model errors (20–60 %).Using temporal sampling from remote-sensing data sets, the satellite imager MODIS (MODerate resolution Imaging Spectroradiometer) and the ground-based sun photometer network AERONET (AErosol Robotic NETwork), and three different global aerosol models, we compare annual and monthly averages of full model data to sampled model data. Our results show that sampling errors as large as 100 % in AOT (aerosol optical thickness), 0.4 in AE (Ångström Exponent) and 0.05 in SSA (single scattering albedo) are possible. Even in daily averages, sampling errors can be significant. Moreover these sampling errors are often correlated over long distances giving rise to artificial contrasts between pristine and polluted events and regions. Additionally, we provide evidence that suggests that models will underestimate these errors. To prevent sampling errors, model data should be temporally collocated to the observations before any analysis is made.We also discuss how this work has consequences for in situ measurements (e.g. aircraft campaigns or surface measurements) in model evaluation.Although this study is framed in the context of model evaluation, it has a clear and direct relevance to climatologies derived from observational data sets.

scholarly journals Ozone Monitoring Instrument (OMI) Aura nitrogen dioxide standard product version 4.0 with improved surface and cloud treatments

2021 ◽  
Vol 14 (1) ◽  
pp. 455-479
Author(s):  
Lok N. Lamsal ◽  
Nickolay A. Krotkov ◽  
Alexander Vasilkov ◽  
Sergey Marchenko ◽  
Wenhan Qin ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
Standard Product ◽  
Independent Observations ◽  
Moderate Resolution ◽  
Ozone Monitoring ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. We present a new and improved version (V4.0) of the NASA standard nitrogen dioxide (NO2) product from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This version incorporates the most salient improvements for OMI NO2 products suggested by expert users and enhances the NO2 data quality in several ways through improvements to the air mass factors (AMFs) used in the retrieval algorithm. The algorithm is based on the geometry-dependent surface Lambertian equivalent reflectivity (GLER) operational product that is available on an OMI pixel basis. GLER is calculated using the vector linearized discrete ordinate radiative transfer (VLIDORT) model, which uses as input high-resolution bidirectional reflectance distribution function (BRDF) information from NASA's Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) instruments over land and the wind-dependent Cox–Munk wave-facet slope distribution over water, the latter with a contribution from the water-leaving radiance. The GLER combined with consistently retrieved oxygen dimer (O2–O2) absorption-based effective cloud fraction (ECF) and optical centroid pressure (OCP) provide improved information to the new NO2 AMF calculations. The new AMFs increase the retrieved tropospheric NO2 by up to 50 % in highly polluted areas; these differences arise from both cloud and surface BRDF effects as well as biases between the new MODIS-based and previously used OMI-based climatological surface reflectance data sets. We quantitatively evaluate the new NO2 product using independent observations from ground-based and airborne instruments. The new V4.0 data and relevant explanatory documentation are publicly available from the NASA Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets/OMNO2_V003/summary/, last access: 8 November 2020), and we encourage their use over previous versions of OMI NO2 products.

Intercomparison of Surface broadband Albedo products from MODIS, CGLS over Northeast Asia

2020 ◽  
Author(s):  
Noh-Hun Seong ◽  
Sungwon Choi ◽  
Donghyun Jin ◽  
Daeseong Jung ◽  
Kyung-soo Han
Keyword(s):  
Northeast Asia ◽  
Radiation Budget ◽  
Data Sets ◽  
Land Type ◽  
Comparison Results ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Types

&lt;p&gt;Surface broadband albedo&amp;#160;is one of the climate variables that understand Earth&amp;#8217;s radiation budget. Currently, the polar-orbit satellite-derived surface broadband albedo products are retrieved by several organizations. As there are many kinds, it is necessary to identify the characteristics of each products. In this study, we were to compare representative products for long-term that the albedo products based on polar-obit satellite such as moderate resolution imaging spectroradiometer (MODIS) and the Copernicus Global Land Service (CGLS). We studied the Northeast Asia region where the land type remains unchanged from 2000 to 2018. The overall trend of the two products was similar. However, differences occurred depending on the land types and season. The relatively high value of MODIS albedo was calculated in winter because it was sensitive to the snow. In other seasons, the CGLS albedo was higher than the MODIS albedo. The MODIS albedo was calculated higher than CGLS albedo for all land types except forest. The comparison results showed that caution should be given before operational use of the albedo data sets in these regions.&lt;/p&gt;

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