Articles | Volume 28, issue 23
https://doi.org/10.5194/hess-28-5295-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-5295-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Dec 2024
Research article |
| 06 Dec 2024
| 06 Dec 2024
Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models
Stroud Water Research Center, 970 Spencer Road, Avondale, PA 19311, USA
David Jones
National Park Service National Capital Region Network, 4598 MacArthur Blvd. NW, Washington, DC 20007, USA
Diana Oviedo-Vargas
Stroud Water Research Center, 970 Spencer Road, Avondale, PA 19311, USA
John Paul Schmit
National Park Service National Capital Region Network, 4598 MacArthur Blvd. NW, Washington, DC 20007, USA
Darren L. Ficklin
Department of Geography, Indiana University Bloomington, 701 E. Kirkwood Avenue, Bloomington, IN 47405, USA
Xuesong Zhang
Hydrology and Remote Sensing Laboratory, United States Department of Agriculture Agricultural Research Service, Bldg. 007, Rm. 104, BARC-West, Beltsville, MD 20705-2350, USA
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Cited
16 citations as recorded by crossref.
- Evaluating Spatial Representativeness Across Multiple Scales for a Comprehensive Ground Validation Network Using Landsat Land Surface Temperature Data and Random Forest X. He et al. https://doi.org/10.1109/TGRS.2025.3570685
- Assessing the impact of data fusion and data quality on spatiotemporal characteristics of exposure models and probability maps - a case study for hazardous mineral fibres D. Zelman-Fahm et al. https://doi.org/10.1016/j.hazadv.2026.101065
- Sử dụng dữ liệu ảnh Landsat và thuật toán học máy xác định biến động lớp phủ/sử dụng đất tại huyện Đắk Glong và Krông Nô - tỉnh Đắk Nông, giai đoạn 2010 - 2023 C. Thị Hoài & N. Thị Thanh Hương https://doi.org/10.55250/Jo.vnuf.15.1.2026.070-079
- Denoising of Binary Built-Up Maps Using Multi-Temporal Image Processing Thresholding S. Becker & N. Wayant https://doi.org/10.3390/land15020271
- Surface runoff estimation in Brazil using the NRCS-CN method and Google Earth Engine cloud platform D. Kaik de Lima Araujo et al. https://doi.org/10.1080/02626667.2026.2691924
- Sensor Informed Predictive Model for Total Organic Carbon and Nutrients on the Upper Yampa River K. Demaree et al. https://doi.org/10.1021/acsestwater.5c00759
- Tillage legacy, grasses and soil texture shape soil organic carbon recovery in semi-arid woodland S. Colunga et al. https://doi.org/10.1038/s41598-025-18504-x
- High fecal indicator bacteria in temperate headwater streams at baseflow: implications for management and public health D. Myers et al. https://doi.org/10.1016/j.wroa.2025.100347
- Estimating Fractional Land Cover Using Sentinel-2 and Multi-Source Data with Traditional Machine Learning and Deep Learning Approaches S. Sierra et al. https://doi.org/10.3390/rs17193364
- An efficient posterior probability-based image fusion change detection model for the estimation of seasonal agricultural changes using microwave and optical datasets N. Vyas et al. https://doi.org/10.1016/j.compag.2025.110683
- Performance comparison of machine learning algorithms for land cover classification C. Atta Amponsah et al. https://doi.org/10.1080/10095020.2026.2667719
- Freshwater salinization syndrome is degrading streamwater quality in the National Capital Region national parks, USA D. Myers et al. https://doi.org/10.1086/739398
- Responses of surface runoff and soil water-erosion to changes in seasonal land cover and rainfall intensity; the case of Shilansha watershed, Rift Valley Basin of Ethiopia A. Gedle et al. https://doi.org/10.1016/j.ejrh.2025.102289
- Comparative evaluation of machine learning models integrated with optimization algorithms for daily transpiration estimation in citrus orchards of dry-hot valley region in China M. Wu et al. https://doi.org/10.1016/j.compag.2026.111773
- Twenty-Four Years of Land Cover Land Use Change in Gasabo, Rwanda, and Projection for 2032 N. Fred et al. https://doi.org/10.3390/land15040655
- Increasing the Thematic Resolution for Trees and Built Area in a Global Land Cover Dataset Using Class Probabilities D. Myers et al. https://doi.org/10.3390/rs17152570
16 citations as recorded by crossref.
- Evaluating Spatial Representativeness Across Multiple Scales for a Comprehensive Ground Validation Network Using Landsat Land Surface Temperature Data and Random Forest X. He et al. https://doi.org/10.1109/TGRS.2025.3570685
- Assessing the impact of data fusion and data quality on spatiotemporal characteristics of exposure models and probability maps - a case study for hazardous mineral fibres D. Zelman-Fahm et al. https://doi.org/10.1016/j.hazadv.2026.101065
- Sử dụng dữ liệu ảnh Landsat và thuật toán học máy xác định biến động lớp phủ/sử dụng đất tại huyện Đắk Glong và Krông Nô - tỉnh Đắk Nông, giai đoạn 2010 - 2023 C. Thị Hoài & N. Thị Thanh Hương https://doi.org/10.55250/Jo.vnuf.15.1.2026.070-079
- Denoising of Binary Built-Up Maps Using Multi-Temporal Image Processing Thresholding S. Becker & N. Wayant https://doi.org/10.3390/land15020271
- Surface runoff estimation in Brazil using the NRCS-CN method and Google Earth Engine cloud platform D. Kaik de Lima Araujo et al. https://doi.org/10.1080/02626667.2026.2691924
- Sensor Informed Predictive Model for Total Organic Carbon and Nutrients on the Upper Yampa River K. Demaree et al. https://doi.org/10.1021/acsestwater.5c00759
- Tillage legacy, grasses and soil texture shape soil organic carbon recovery in semi-arid woodland S. Colunga et al. https://doi.org/10.1038/s41598-025-18504-x
- High fecal indicator bacteria in temperate headwater streams at baseflow: implications for management and public health D. Myers et al. https://doi.org/10.1016/j.wroa.2025.100347
- Estimating Fractional Land Cover Using Sentinel-2 and Multi-Source Data with Traditional Machine Learning and Deep Learning Approaches S. Sierra et al. https://doi.org/10.3390/rs17193364
- An efficient posterior probability-based image fusion change detection model for the estimation of seasonal agricultural changes using microwave and optical datasets N. Vyas et al. https://doi.org/10.1016/j.compag.2025.110683
- Performance comparison of machine learning algorithms for land cover classification C. Atta Amponsah et al. https://doi.org/10.1080/10095020.2026.2667719
- Freshwater salinization syndrome is degrading streamwater quality in the National Capital Region national parks, USA D. Myers et al. https://doi.org/10.1086/739398
- Responses of surface runoff and soil water-erosion to changes in seasonal land cover and rainfall intensity; the case of Shilansha watershed, Rift Valley Basin of Ethiopia A. Gedle et al. https://doi.org/10.1016/j.ejrh.2025.102289
- Comparative evaluation of machine learning models integrated with optimization algorithms for daily transpiration estimation in citrus orchards of dry-hot valley region in China M. Wu et al. https://doi.org/10.1016/j.compag.2026.111773
- Twenty-Four Years of Land Cover Land Use Change in Gasabo, Rwanda, and Projection for 2032 N. Fred et al. https://doi.org/10.3390/land15040655
- Increasing the Thematic Resolution for Trees and Built Area in a Global Land Cover Dataset Using Class Probabilities D. Myers et al. https://doi.org/10.3390/rs17152570
Saved (final revised paper)
Latest update: 14 Jul 2026
Short summary
We studied how streamflow and water quality models respond to land cover data collected by satellites during the growing season versus the non-growing season. The land cover data showed more trees during the growing season and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resource management.
We studied how streamflow and water quality models respond to land cover data collected by...
