Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA, using multi-satellite data fusion

Yang, Yun; Anderson, Martha C.; Gao, Feng; Hain, Christopher; Semmens, K. A.; Kustas, William P.; Noormets, Asko; Wynne, Randolph H.; Thomas, Valerie A.; Sun, Ge

doi:10.5194/hess-21-1017-2017

Cited by 89 publications

(49 citation statements)

References 79 publications

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“…Improved performance using STARFM was also noted by Cammalleri et al [2013] over rainfed corn and soybean fields in central Iowa. However, Yang et al [2017a] obtained similar or marginally poorer performance with STARFM over a managed pine plantation in the North Carolina coastal plains. In the latter case, the flux measurement systems were in forest stands in wet soils that did not experience stress during the study period.…”

Section: Value Added By Fusion Of Landsat Et With Modis Time Seriesmentioning

confidence: 89%

“…The method involves running STARFM for the partly cloudy/striped prediction date using Landsat and MODIS retrieved ET from surrounding clear dates. The cloud/stripeimpacted areas in the Landsat retrieval are then filled as a weighted function of the STARFM estimated Landsat-like ET -see details in Yang et al [2017a]. Scenes with missing data over more than approximately 30% of the targeted modeling domain are excluded from the fusion process.…”

Section: Landsat Scene Gap-fillingmentioning

confidence: 99%

“…The DMS sharpening procedure described in Section 2.3.1 improves LST resolution to 30 m (Figures 4c and 4d), where connections between ET and land use and water management patterns are more clearly made (Figures 4e and 4f). For Landsat 7 ET images post 2003, stripes due to the SLC failure are gap-filled (Figure 4g) using the method developed by Yang et al [2017a]. In this case, DOY 203 was used to fill DOY 211 and the edges of the stripes show consistency between the reconstructed and the original areas, with no obviously patchy artifacts in the gap-filled ET images.…”

Section: Example Of Et Fusion Processmentioning

confidence: 99%

“…The final ET product, characterized by both fine spatial resolution (30 m) and temporal frequency (daily), is obtained by fusing MODIS and Landsat derived ET maps using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) [Gao et al, 2006]. This ET fusion approach has been successfully applied over rain-fed and irrigated cotton, corn and soybean fields [Cammalleri et al, 2014;Yang et al, 2017b], irrigated vineyards [Semmens et al, 2015], as well as forested landscapes [Yang et al, 2017a].…”

Section: Introductionmentioning

confidence: 99%

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Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach

Sun

Anderson

Gao

et al. 2017

Water Resources Research

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The health of the Chesapeake Bay ecosystem has been declining for several decades due to high levels of nutrients and sediments largely tied to agricultural production systems. Therefore, monitoring of agricultural water use and hydrologic connections between crop lands and Bay tributaries has received increasing attention. Remote sensing retrievals of actual evapotranspiration (ET) can provide valuable information in support of these hydrologic modeling efforts, spatially and temporally describing consumptive water use by crops and natural vegetation and quantifying response to expansion of irrigated area occurring with Bay watershed. In this study, a multisensor satellite data fusion methodology, combined with a multiscale ET retrieval algorithm, was applied over the Choptank River watershed located within the Lower Chesapeake Bay region on the Eastern Shore of Maryland, USA to produce daily 30 m resolution ET maps. ET estimates directly retrieved on Landsat satellite overpass dates have high accuracy with relative error (RE) of 9%, as evaluated using flux tower measurements. The fused daily ET time series have reasonable errors of 18% at the daily time step ‐ an improvement from 27% errors using standard Landsat‐only interpolation techniques. Annual water consumption by different land cover types was assessed, showing reasonable distributions of water use with cover class. Seasonal patterns in modeled crop transpiration and soil evaporation for dominant crop types were analyzed, and agree well with crop phenology at field scale. Additionally, effects of irrigation occurring during a period of rainfall shortage were captured by the fusion program. These results suggest that the ET fusion system will have utility for water management at field and regional scales over the Eastern Shore. Further efforts are underway to integrate these detailed water use data sets into watershed‐scale hydrologic models to improve assessments of water quality and inform best management practices to reduce nutrient and sediment loads to the Chesapeake Bay.

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Section: Value Added By Fusion Of Landsat Et With Modis Time Seriesmentioning

confidence: 89%

Section: Landsat Scene Gap-fillingmentioning

confidence: 99%

Section: Example Of Et Fusion Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach

Sun

Anderson

Gao

et al. 2017

Water Resources Research

Self Cite

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“…In brief, STARFM compares MODIS and Landsat image pairs on days when both are available, computes spatial weighting statistics, then applies these weights to downscale MODIS images between clear-sky Landsat overpasses. STARFM was originally developed to fuse surface reflectance imagery, but has also been applied successfully to ET datasets developed over a variety of agricultural and forested landscapes in the United States [20,[41][42][43][44][45] and in Spain [46]. …”

Section: Data Fusionmentioning

confidence: 99%

Field-Scale Assessment of Land and Water Use Change over the California Delta Using Remote Sensing

et al. 2018

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Abstract:The ability to accurately monitor and anticipate changes in consumptive water use associated with changing land use and land management is critical to developing sustainable water management strategies in water-limited climatic regions. In this paper, we present an application of a remote sensing data fusion technique for developing high spatiotemporal resolution maps of evapotranspiration (ET) at scales that can be associated with changes in land use. The fusion approach combines ET map timeseries developed using an multi-scale energy balance algorithm applied to thermal data from Earth observation platforms with high spatial but low temporal resolution (e.g., Landsat) and with moderate resolution but frequent temporal coverage (e.g., MODIS (Moderate Resolution Imaging Spectroradiometer)). The approach is applied over the Sacramento-San Joaquin Delta region in California-an area critical to both agricultural production and drinking water supply within the state that has recently experienced stresses on water resources due to a multi-year (2012-2017) extreme drought. ET "datacubes" with 30-m resolution and daily timesteps were constructed for the 2015-2016 water years and related to detailed maps of land use developed at the same spatial scale. The ET retrievals are evaluated at flux sites over multiple land covers to establish a metric of accuracy in the annual water use estimates, yielding root-mean-square errors of 1.0, 0.8, and 0.3 mm day −1 at daily, monthly, and yearly timesteps, respectively, for all sites combined. Annual ET averaged over the Delta changed only 3 mm year −1 between water years, from 822 to 819 mm year −1 , translating to an area-integrated total change in consumptive water use of seven thousand acre-feet (TAF). Changes were largest in areas with recorded land-use change between water years-most significantly, fallowing of crop land presumably in response to reductions in water availability and allocations due to the drought. Moreover, the time evolution in water use associated with wetland restoration-an effort aimed at reducing subsidence and carbon emissions within the inner Delta-is assessed using a sample wetland chronosequence. Region-specific matrices of consumptive water use associated with land use changes may be an effective tool for policymakers and farmers to understand how land use conversion could impact consumptive use and demand.

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Using big data for evaluating development outcomes: A systematic map

Rathinam

Khatua²,

Siddiqui³

et al. 2021

Campbell Systematic Reviews

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Background Policy makers need access to reliable data to monitor and evaluate the progress of development outcomes and targets such as sustainable development outcomes (SDGs). However, significant data and evidence gaps remain. Lack of resources, limited capacity within governments and logistical difficulties in collecting data are some of the reasons for the data gaps. Big data—that is digitally generated, passively produced and automatically collected—offers a great potential for answering some of the data needs. Satellite and sensors, mobile phone call detail records, online transactions and search data, and social media are some of the examples of big data. Integrating big data with the traditional household surveys and administrative data can complement data availability, quality, granularity, accuracy and frequency, and help measure development outcomes temporally and spatially in a number of new ways.The study maps different sources of big data onto development outcomes (based on SDGs) to identify current evidence base, use and the gaps. The map provides a visual overview of existing and ongoing studies. This study also discusses the risks, biases and ethical challenges in using big data for measuring and evaluating development outcomes. The study is a valuable resource for evaluators, researchers, funders, policymakers and practitioners in their effort to contributing to evidence informed policy making and in achieving the SDGs. Objectives Identify and appraise rigorous impact evaluations (IEs), systematic reviews and the studies that have innovatively used big data to measure any development outcomes with special reference to difficult contexts Search Methods A number of general and specialised data bases and reporsitories of organisations were searched using keywords related to big data by an information specialist. Selection Criteria The studies were selected on basis of whether they used big data sources to measure or evaluate development outcomes. Data Collection and Analysis Data collection was conducted using a data extraction tool and all extracted data was entered into excel and then analysed using Stata. The data analysis involved looking at trends and descriptive statistics only. Main Results The search yielded over 17,000 records, which we then screened down to 437 studies which became the foundation of our systematic map. We found that overall, there is a sizable and rapidly growing number of measurement studies using big data but a much smaller number of IEs. We also see that the bulk of the big data sources are machine‐generated (mostly satellites) represented in the light blue. We find that satellite data was used in over 70% of the measurement studies and in over 80% of the IEs. Authors' Conclusions This map gives us a sense that there is a lot of work being done to develop appropriate measures using big data which could subsequently be used in IEs. Information on costs, ethics, transparency is lacking in the studies and more work is needed in this area to understand the efficaci...

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Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA, using multi-satellite data fusion

Cited by 89 publications

References 79 publications

Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach

Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach

Field-Scale Assessment of Land and Water Use Change over the California Delta Using Remote Sensing

Using big data for evaluating development outcomes: A systematic map

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