scholarly journals A statistics-based temporal filter algorithm to map spatiotemporally continuous shortwave albedo from MODIS data

2013 ◽  
Vol 17 (6) ◽  
pp. 2121-2129 ◽  
Author(s):  
N. F. Liu ◽  
Q. Liu ◽  
L. Z. Wang ◽  
S. L. Liang ◽  
J. G. Wen ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Land Surface ◽  
Surface Albedo ◽  
Critical Role ◽  
Radiant Energy ◽  
High Temporal Resolution ◽  
Land Surface Parameter ◽  
Temporal Filter ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Land-surface albedo plays a critical role in the earth's radiant energy budget studies. Satellite remote sensing provides an effective approach to acquire regional and global albedo observations. Owing to cloud coverage, seasonal snow and sensor malfunctions, spatiotemporally continuous albedo datasets are often inaccessible. The Global LAnd Surface Satellite (GLASS) project aims at providing a suite of key land surface parameter datasets with high temporal resolution and high accuracy for a global change study. The GLASS preliminary albedo datasets are global daily land-surface albedo generated by an angular bin algorithm (Qu et al., 2013). Like other products, the GLASS preliminary albedo datasets are affected by large areas of missing data; beside, sharp fluctuations exist in the time series of the GLASS preliminary albedo due to data noise and algorithm uncertainties. Based on the Bayesian theory, a statistics-based temporal filter (STF) algorithm is proposed in this paper to fill data gaps, smooth albedo time series, and generate the GLASS final albedo product. The results of the STF algorithm are smooth and gapless albedo time series, with uncertainty estimations. The performance of the STF method was tested on one tile (H25V05) and three ground stations. Results show that the STF method has greatly improved the integrity and smoothness of the GLASS final albedo product. Seasonal trends in albedo are well depicted by the GLASS final albedo product. Compared with MODerate resolution Imaging Spectroradiometer (MODIS) product, the GLASS final albedo product has a higher temporal resolution and more competence in capturing the surface albedo variations. It is recommended that the quality flag should be always checked before using the GLASS final albedo product.

scholarly journals Estimation of Land Surface Albedo from MODIS and VIIRS Data: A Multi-Sensor Strategy Based on the Direct Estimation Algorithm and Statistical-Based Temporal Filter

2020 ◽  
Vol 12 (24) ◽  
pp. 4131
Author(s):  
Mengsi Wang ◽  
Xianlei Fan ◽  
Xijia Li ◽  
Qiang Liu ◽  
Ying Qu
Keyword(s):  
Temporal Resolution ◽  
Land Surface ◽  
Surface Albedo ◽  
Estimation Algorithm ◽  
Radiation Budget ◽  
Estimation Accuracy ◽  
High Temporal Resolution ◽  
Direct Estimation ◽  
Temporal Continuity ◽  
Temporal Filter

Land surface albedo is an important variable for Earth’s radiation and energy budget. Over the past decades, many surface albedo products have been derived from a variety of remote sensing data. However, the estimation accuracy, temporal resolution, and temporal continuity of these datasets still need to be improved. We developed a multi-sensor strategy (MSS) based on the direct-estimation algorithm (DEA) and Statistical-Based Temporal Filter (STF) to improve the quality of land surface albedo datasets. The moderate-resolution imaging spectroradiometer (MODIS) data onboard Terra and Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi-National Polar-orbiting Partnership (NPP) were used as multi-sensor data. The MCD43A3 product and in situ measurements from the Surface Radiation Budget Network (SURFRAD) and FLUXNET sites were employed for validation and comparison. The results showed that the proposed MSS method significantly improved the temporal continuity and estimation accuracy during the snow-covered period, which was more consistent with the measurements of SURFRAD (R = 0.9498, root mean square error (RMSE) = 0.0387, and bias = −0.0017) and FLUXNET (R = 0.9421, RMSE = 0.0330, and bias = 0.0002) sites. Moreover, this is a promising method to generate long-term, spatiotemporal continuous land surface albedo datasets with high temporal resolution.

scholarly journals Mapping spatially-temporally continuous shortwave albedo for global land surface from MODIS data

2012 ◽  
Vol 9 (7) ◽  
pp. 9043-9064 ◽  
Author(s):  
N. Liu ◽  
Q. Liu ◽  
L. Wang ◽  
S. Liang ◽  
J. Wen ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Satellite Remote Sensing ◽  
Surface Albedo ◽  
Critical Role ◽  
Radiant Energy ◽  
Cloud Coverage ◽  
Seasonal Snow ◽  
Data Gaps ◽  
Global Land

Abstract. Land-surface albedo plays a critical role in the Earth's radiant energy budget studies. Satellite remote sensing is an effective approach to acquire regional and global albedo observations. However, owing to cloud coverage, seasonal snow and sensor malfunctions, spatially-temporally continuous albedo datasets are often inaccessible. GLASS preliminary albedo datasets (GLASS02A2x, x = 1, 2, 3 and 4) are newly developed global daily land-surface albedo products. Like other products, GLASS02A2x albedo surfers from large areas of missing data. Beside this, sharp fluctuations exist in GLASS02A2x time series due to data noise and algorithm uncertainties. In this study, a statistics-based temporal filterer (STF) is proposed to fill the data gaps and smooth the fluctuations in GLASS02A2x albedo time series. The result of STF algorithm is the GLASS final albedo product (GLASS02A06). Results show that the STF method has greatly improved the integrity and smoothness of the GLASS final albedo product. Seasonal trends in albedo are well depicted by the GLASS final albedo product. Compared with MODIS product, the final GLASS albedo product is much more competent in capturing the surface albedo variations. Although the STF algorithm is designed for GLASS albedo product, it is able to incorporate other albedo products. The STF method may also be applied to other parameters, such as the LAI and soil moisture.

scholarly journals Development of Land Surface Albedo Algorithm for the GK-2A/AMI Instrument

2020 ◽  
Vol 12 (15) ◽  
pp. 2500
Author(s):  
Kyeong-Sang Lee ◽  
Sung-Rae Chung ◽  
Changsuk Lee ◽  
Minji Seo ◽  
Sungwon Choi ◽  
...  
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Monsoon Season ◽  
Atmospheric Correction ◽  
Optimization Method ◽  
Retrieval Algorithm ◽  
High Temporal Resolution ◽  
Temporal Consistency ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Mean Square Errors

The Korea Meteorological Administration successfully launched Korea’s next-generation meteorological satellite, Geo-KOMPSAT-2A (GK-2A), on 5 December 2018. It belongs to the new generation of GEO (Geostationary Elevation Orbit) satellite which offers capabilities to disseminate high spatial- (0.5–2 km) and high temporal-resolution (10 min) observations over a broad area, herein a geographic disk encompassing the Asia–Oceania region. The targeted objective is to enhance our understanding of climate change, owing to a bulk of coherent observations. For such, we developed an algorithm to map the land surface albedo (LSA), which is a major Essential Climate Variable (ECV). The retrieval algorithm devoted to GK-2A/Advanced Meteorological Imager (AMI) data considered Japan’s Himawari-8/Advanced Himawari Imager (AHI) data for prototyping, as this latter owns similar specifications to AMI. Our proposed algorithm is decomposed in three major steps: atmospheric correction, bidirectional reflectance distribution function (BRDF) modeling and angular integration, and narrow-to-broadband conversion. To perform BRDF modeling, the optimization method using normalized reflectance was applied, which improved the quality of BRDF modeling results, particularly when the number of observations was less than 15. A quality assessment was performed to compare our results to those of Moderate Resolution Imaging Spectroradiometer (MODIS) LSA products and ground measurement from Aerosol Robotic Network (AERONET) sites, Australian and New Zealand flux tower network (OzFlux) site and the Korea Flux Network (KoFlux) site from throughout 2017. Our results show dependable spatial and temporal consistency with MODIS broadband LSA data, and rapid changes in LSA due to snowfall and snow melting were well expressed in the temporal profile of our results. Our outcomes also show good agreement with the ground measurements from AERONET, OzFlux and KoFlux ground-based network with root mean square errors (RMSE) of 0.0223 and 0.0306, respectively, which is close to the accuracy of MODIS broadband LSA. Moreover, our results reveal still more reliable LSA products even when clouds are frequently present, such as during the summer monsoon season. It shows that our results are useful for continuous LSA monitoring.

scholarly journals Assessment of the EUMETSAT Multi Decadal Land Surface Albedo Data Record from Meteosat Observations

2021 ◽  
Vol 13 (10) ◽  
pp. 1992
Author(s):  
Alessio Lattanzio ◽  
Michael Grant ◽  
Marie Doutriaux-Boucher ◽  
Rob Roebeling ◽  
Jörg Schulz
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Time Range ◽  
Essential Variable ◽  
Spatial Coverage ◽  
Data Record ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask ◽  
Cloud Screening

Surface albedo, defined as the ratio of the surface-reflected irradiance to the incident irradiance, is one of the parameters driving the Earth energy budget and it is for this reason an essential variable in climate studies. Instruments on geostationary satellites provide suitable observations allowing long-term monitoring of surface albedo from space. In 2012, EUMETSAT published Release 1 of the Meteosat Surface Albedo (MSA) data record. The main limitation effecting the quality of this release was non-removed clouds by the incorporated cloud screening procedure that caused too high albedo values, in particular for regions with permanent cloud coverage. For the generation of Release 2, the MSA algorithm has been replaced with the Geostationary Surface Albedo (GSA) one, able to process imagery from any geostationary imager. The GSA algorithm exploits a new, improved, cloud mask allowing better cloud screening, and thus fixing the major limitation of Release 1. Furthermore, the data record has an extended temporal and spatial coverage compared to the previous release. Both Black-Sky Albedo (BSA) and White-Sky Albedo (WSA) are estimated, together with their associated uncertainties. A direct comparison between Release 1 and Release 2 clearly shows that the quality of the retrieval improved significantly with the new cloud mask. For Release 2 the decadal trend is less than 1% over stable desert sites. The validation against Moderate Resolution Imaging Spectroradiometer (MODIS) and the Southern African Regional Science Initiative (SAFARI) surface albedo shows a good agreement for bright desert sites and a slightly worse agreement for urban and rain forest locations. In conclusion, compared with MSA Release 1, GSA Release 2 provides the users with a significantly more longer time range, reliable and robust surface albedo data record.

scholarly journals Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

2015 ◽  
Vol 9 (5) ◽  
pp. 1879-1893 ◽  
Author(s):  
K. Atlaskina ◽  
F. Berninger ◽  
G. de Leeuw
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Snow Cover Fraction ◽  
Air Temperatures ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March–May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56 % of variation of albedo in March, 76 % in April and 92 % in May. Therefore the effects of other parameters were investigated only for areas with 100 % SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between −15 and −10 °C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100 % SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

scholarly journals Using the anomaly forcing Community Land Model (CLM 4.5) for crop yield projections

2020 ◽  
Author(s):  
Yaqiong Lu ◽  
Xianyu Yang
Keyword(s):  
Crop Yield ◽  
Temporal Resolution ◽  
Land Surface ◽  
Crop Yields ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Climate Scenarios ◽  
Climate Data ◽  
Land Surface Models ◽  
Surface Models

Abstract. Crop growth in land surface models normally requires high temporal resolution climate data (3-hourly or 6-hourly), but such high temporal resolution climate data are not provided by many climate model simulations due to expensive storage, which limits modeling choice if there is an interest in a particular climate simulation that only saved monthly outputs. The Community Land Surface Model (CLM) has proposed an alternative approach for utilizing monthly climate outputs as forcing data since version 4.5, and it is called the anomaly forcing CLM. However, such an approach has never been validated for crop yield projections. In our work, we created anomaly forcing datasets for three climate scenarios (1.5 °C warming, 2.0 °C warming, and RCP4.5) and validated crop yields against the standard CLM forcing with the same climate scenarios using 3-hourly data. We found that the anomaly forcing CLM could not produce crop yields identical to the standard CLM due to the different submonthly variations, and crop yields were underestimated by 5–8 % across the three scenarios (1.5 °C, 2.0 °C, and RCP4.5) for the global average, and 28–41 % of cropland showed significantly different yields. However, the anomaly forcing CLM effectively captured the relative changes between scenarios and over time, as well as regional crop yield variations. We recommend that such an approach be used for qualitative analysis of crop yields when only monthly outputs are available. Our approach can be adopted by other land surface models to expand their capabilities for utilizing monthly climate data.

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

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