A Global Land Cover Climatology Using MODIS Data

Broxton, P. D.; Zeng, Xubin; Sulla‐Menashe, Damien; Troch, P. A.

doi:10.1175/jamc-d-13-0270.1

Cited by 289 publications

(212 citation statements)

References 35 publications

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“…1) were cumulative starting on the earliest DOY were data were available (DOY 105) for all years (2010)(2011)(2012)(2013)(2014)(2015). This watershed is 32,375 km 2 of which 91% is classified as agricultural use, mostly maize and soy production (Broxton et al 2014). We used the zoo R package to linearly interpolate across missing Q data (Moatar and Meybeck 2005).…”

Section: Data Setsmentioning

confidence: 99%

Weather whiplash in agricultural regions drives deterioration of water quality

et al. 2017

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Excess nitrogen (N) impairs inland water quality and creates hypoxia in coastal ecosystems. Agriculture is the primary source of N; agricultural management and hydrology together control aquatic ecosystem N loading. Future N loading will be determined by how agriculture and hydrology intersect with climate change, yet the interactions between changing climate and water quality remain poorly understood. Here, we show that changing precipitation patterns, resulting from climate change, interact with agricultural land use to deteriorate water quality. We focus on the 2012-2013 Midwestern U.S. drought as a ''natural experiment''. The transition from drought conditions in 2012 to a wet spring in 2013 was abrupt; the media dubbed this ''weather whiplash''. We use recent (2010)(2011)(2012)(2013)(2014)(2015) and historical data to connect weather whiplash (drought-to-flood transitions) to increases in riverine N loads and concentrations. The drought likely created highly N-enriched soils; this excess N mobilized during heavy spring rains (2013), resulting in a 34% increase (10.5 vs. 7.8 mg N L -1 ) in the flow-weighted mean annual Biogeochemistry (2017) 133:7-15 DOI 10.1007 nitrate concentration compared to recent years. Furthermore, we show that climate change will likely intensify weather whiplash. Increased weather whiplash will, in part, increase the frequency of riverine N exceeding E.P.A. drinking water standards. Thus, our observations suggest increased climatic variation will amplify negative trends in water quality in a region already grappling with severe impairments.

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Section: Data Setsmentioning

confidence: 99%

Weather whiplash in agricultural regions drives deterioration of water quality

et al. 2017

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“…Detection of placid open water with SAR data is relatively straight forward because a transmitted radar pulse specularly reflects away from a side-looking SAR antenna giving the water very low values of backscatter [20]. The training site for open water was in the region of very dark radar pixels in the southern part of the lake and classified by the 0.5 km MODIS-based Global Land Cover Climatology [21] as open water. The training site for dry vegetation was in the region outside of the lake boundaries and classified by the 0.5 km MODIS-based Global Land Cover Climatology [21] as grassland.…”

Section: Lake Area Estimation Using Esa Sentinel-1a C-band Radar Datamentioning

confidence: 99%

“…The training site for open water was in the region of very dark radar pixels in the southern part of the lake and classified by the 0.5 km MODIS-based Global Land Cover Climatology [21] as open water. The training site for dry vegetation was in the region outside of the lake boundaries and classified by the 0.5 km MODIS-based Global Land Cover Climatology [21] as grassland. The training site for flooded vegetation was at the discharge of the Komadougou Yobe (also known as the Yobe) River.…”

Section: Lake Area Estimation Using Esa Sentinel-1a C-band Radar Datamentioning

confidence: 99%

Lake Chad Total Surface Water Area as Derived from Land Surface Temperature and Radar Remote Sensing Data

et al. 2018

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Lake Chad, located in the middle of the African Sahel belt, underwent dramatic decreases in the 1970s and 1980s leaving less than ten percent of its 1960s surface water extent as open water. In this paper, we present an extended record (dry seasons 1988-2016) of the total surface water area of the lake (including both open water and flooded vegetation) derived using Land Surface Temperature (LST) data (dry seasons [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015][2016] from the NASA Terra MODIS sensor and EUMETSAT Meteosat-based LST measurements (dry seasons 1988-2001) from an earlier study. We also examine the total surface water area for Lake Chad using radar data (dry seasons 2015-2016) from the ESA Sentinel-1a mission. For the limited number of radar data sets available to us (18 data sets), we find on average a close match between the estimates from these data and the corresponding estimates from LST, though we find spatial differences in the estimates using the two types of data. We use these spatial differences to adjust the record (dry seasons 2000-2016) from MODIS LST. Then we use the adjusted record to remove the bias of the existing LST record (dry seasons 1988-2001) derived from Meteosat measurements and combine the two records. From this composite, extended record, we plot the total surface water area of the lake for the dry seasons of 1988-1989 through 2016-2017. We find for the dry seasons of 1988-1989 to 2016-2017 that the maximum total surface water area of the lake was approximately 16,800 sq. km (February and May, 2000), the minimum total surface water area of the lake was approximately 6400 sq. km (November, 1990), and the average was approximately 12,700 sq. km. Further, we find the total surface water area of the lake to be highly variable during this period, with an average rate of increase of approximately 143 km 2 per year.

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“…Given that the surface R n is closely related to the land cover type, MCD12Q1 [37] land cover type data were used in this study, as shown in Figure 2 for reference. Data from all those networks were checked site by site, and some extremely abnormal values were removed.…”

Section: Land Cover Type Datamentioning

confidence: 99%

“…Given that the surface Rn is closely related to the land cover type, MCD12Q1 [37] land cover type data were used in this study, as shown in Figure 2 for reference. …”

Section: Land Cover Type Datamentioning

confidence: 99%

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

et al. 2016

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Abstract:The Clouds and the Earth's Radiant Energy System (CERES) generates one of the few global satellite radiation products. The CERES ARM Validation Experiment (CAVE) has been providing long-term in situ observations for the validation of the CERES products. However, the number of these sites is low and their distribution is globally sparse, and particularly the surface net radiation product has not been rigorously validated yet. Therefore, additional validation efforts are highly required to determine the accuracy of the CERES radiation products. In this study, global land surface measurements were comprehensively collected for use in the validation of the CERES net radiation (R n ) product on a daily (340 sites) and a monthly (260 sites) basis, respectively. The validation results demonstrated that the CERES R n product was, overall, highly accurate. The daily validations had a Mean Bias Error (MBE) of 3.43 W¨m´2, Root Mean Square Error (RMSE) of 33.56 W¨m´2, and R 2 of 0.79, and the monthly validations had an MBE of 3.40 W¨m´2, RMSE of 25.57 W¨m´2, and R 2 of 0.84. The accuracy was slightly lower for the high latitudes. Following the validation, the monthly CERES R n product, from March 2000 to July 2014, was used for a further analysis. The global spatiotemporal variation of the R n , which occurred during the measurement period, was analyzed. In addition, two hot spot regions, the southern Great Plains and south-central Africa, were then selected for use in determining the driving factors or attribution of the R n variation. We determined that R n over the southern Great Plains decreased by´0.33 W¨m´2 per year, which was mainly driven by changes in surface green vegetation and precipitation. In south-central Africa, R n decreased at a rate of´0.63 W¨m´2 per year, the major driving factor of which was surface green vegetation.

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A Global Land Cover Climatology Using MODIS Data

Cited by 289 publications

References 35 publications

Weather whiplash in agricultural regions drives deterioration of water quality

Weather whiplash in agricultural regions drives deterioration of water quality

Lake Chad Total Surface Water Area as Derived from Land Surface Temperature and Radar Remote Sensing Data

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

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