ArticleRemote sensing2020
Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions.
Article in Remote sensing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- BioRT-HBV 1.0: A Biogeochemical Reactive Transport Model at the Watershed Scale.Journal of advances in modeling earth systems · 2024Article
- Spatial calibration and uncertainty reduction of the SWAT model using multiple remotely sensed data.Heliyon · 2024Article
- The Application of SWAT Model and Remotely Sensed Products to Characterize the Dynamic of Streamflow and Snow in a Mountainous Watershed in the High Atlas.Sensors (Basel, Switzerland) · 2023Article
- Vulnerable Waters are Essential to Watershed Resilience.Ecosystems (New York, N.Y.) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km
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Registered trials
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