@article{ranghettiTestingEstimationWater2016,
  title = {Testing Estimation of Water Surface in {{Italian}} Rice District from {{MODIS}} Satellite Data},
  author = {Ranghetti, Luigi and Busetto, Lorenzo and Crema, Alberto and Fasola, Mauro and Cardarelli, Elisa and Boschetti, Mirco},
  date = {2016-10},
  journaltitle = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {52},
  pages = {284--295},
  issn = {0303-2434},
  doi = {10.1016/j.jag.2016.06.018},
  url = {https://doi.org/10.1016/j.jag.2016.06.018},
  abstract = {[Highlights]

[::] Landsat 8 is a valid data set to recognise flooded rice parcels. [::] MODIS data can be used to estimate flooding fraction (FF) at 1 × 1 km resolution. [::] MODIS NDVI can be used to mask unreliable FF predictions. [::] Spatio-temporal dynamics of water management within Italian rice district are clearly depictable from FF maps.

[Abstract]

Recent changes in rice crop management within Northern Italy rice district led to a reduction of seeding in flooding condition, which may have an impact on reservoir water management and on the animal and plant communities that depend on the flooded paddies. Therefore, monitoring and quantifying the spatial and temporal variability of water presence in paddy fields is becoming important. In this study we present a method to estimate dynamics of presence of standing water (i.e. fraction of flooded area) in rice fields using MODIS data. First, we produced high resolution water presence maps from Landsat by thresholding the Normalised Difference Flood Index (NDFI) made: we made it by comparing five Landsat 8 images with field-obtained information about rice field status and water presence. Using these data we developed an empirical model to estimate the flooding fraction of each MODIS cell. Finally we validated the MODIS-based flooding maps with both Landsat and ground information. Results showed a good predictability of water surface from Landsat (OA = 92\%) and a robust usability of MODIS data to predict water fraction (R2 = 0.73, EF = 0.57, RMSE = 0.13 at 1 × 1 km resolution). Analysis showed that the predictive ability of the model decreases with the greening up of rice, so we used NDVI to automatically discriminate estimations for inaccurate cells in order to provide the water maps with a reliability flag. Results demonstrate that it is possible to monitor water dynamics in rice paddies using moderate resolution multispectral satellite data. The achievement is a proof of concept for the analysis of MODIS archives to investigate irrigation dynamics in the last 15 years to retrieve information for ecological and hydrological studies.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14095798,~to-add-doi-URL,agricultural-resources,cereals,italy,modis,remote-sensing,rice,water-resources}
}

{"_id":"MTfHT2NRpgqFaZLQk","bibbaseid":"ranghetti-busetto-crema-fasola-cardarelli-boschetti-testingestimationofwatersurfaceinitalianricedistrictfrommodissatellitedata","authorIDs":[],"author_short":["Ranghetti, L.","Busetto, L.","Crema, A.","Fasola, M.","Cardarelli, E.","Boschetti, M."],"bibdata":{"bibtype":"article","type":"article","title":"Testing Estimation of Water Surface in Italian Rice District from MODIS Satellite Data","author":[{"propositions":[],"lastnames":["Ranghetti"],"firstnames":["Luigi"],"suffixes":[]},{"propositions":[],"lastnames":["Busetto"],"firstnames":["Lorenzo"],"suffixes":[]},{"propositions":[],"lastnames":["Crema"],"firstnames":["Alberto"],"suffixes":[]},{"propositions":[],"lastnames":["Fasola"],"firstnames":["Mauro"],"suffixes":[]},{"propositions":[],"lastnames":["Cardarelli"],"firstnames":["Elisa"],"suffixes":[]},{"propositions":[],"lastnames":["Boschetti"],"firstnames":["Mirco"],"suffixes":[]}],"date":"2016-10","journaltitle":"International Journal of Applied Earth Observation and Geoinformation","volume":"52","pages":"284–295","issn":"0303-2434","doi":"10.1016/j.jag.2016.06.018","url":"https://doi.org/10.1016/j.jag.2016.06.018","abstract":"[Highlights] [::] Landsat 8 is a valid data set to recognise flooded rice parcels. [::] MODIS data can be used to estimate flooding fraction (FF) at 1 × 1 km resolution. [::] MODIS NDVI can be used to mask unreliable FF predictions. [::] Spatio-temporal dynamics of water management within Italian rice district are clearly depictable from FF maps. [Abstract] Recent changes in rice crop management within Northern Italy rice district led to a reduction of seeding in flooding condition, which may have an impact on reservoir water management and on the animal and plant communities that depend on the flooded paddies. Therefore, monitoring and quantifying the spatial and temporal variability of water presence in paddy fields is becoming important. In this study we present a method to estimate dynamics of presence of standing water (i.e. fraction of flooded area) in rice fields using MODIS data. First, we produced high resolution water presence maps from Landsat by thresholding the Normalised Difference Flood Index (NDFI) made: we made it by comparing five Landsat 8 images with field-obtained information about rice field status and water presence. Using these data we developed an empirical model to estimate the flooding fraction of each MODIS cell. Finally we validated the MODIS-based flooding maps with both Landsat and ground information. Results showed a good predictability of water surface from Landsat (OA = 92%) and a robust usability of MODIS data to predict water fraction (R2 = 0.73, EF = 0.57, RMSE = 0.13 at 1 × 1 km resolution). Analysis showed that the predictive ability of the model decreases with the greening up of rice, so we used NDVI to automatically discriminate estimations for inaccurate cells in order to provide the water maps with a reliability flag. Results demonstrate that it is possible to monitor water dynamics in rice paddies using moderate resolution multispectral satellite data. The achievement is a proof of concept for the analysis of MODIS archives to investigate irrigation dynamics in the last 15 years to retrieve information for ecological and hydrological studies.","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-14095798,~to-add-doi-URL,agricultural-resources,cereals,italy,modis,remote-sensing,rice,water-resources","bibtex":"@article{ranghettiTestingEstimationWater2016,\n title = {Testing Estimation of Water Surface in {{Italian}} Rice District from {{MODIS}} Satellite Data},\n author = {Ranghetti, Luigi and Busetto, Lorenzo and Crema, Alberto and Fasola, Mauro and Cardarelli, Elisa and Boschetti, Mirco},\n date = {2016-10},\n journaltitle = {International Journal of Applied Earth Observation and Geoinformation},\n volume = {52},\n pages = {284--295},\n issn = {0303-2434},\n doi = {10.1016/j.jag.2016.06.018},\n url = {https://doi.org/10.1016/j.jag.2016.06.018},\n abstract = {[Highlights]\n\n[::] Landsat 8 is a valid data set to recognise flooded rice parcels. [::] MODIS data can be used to estimate flooding fraction (FF) at 1 × 1 km resolution. [::] MODIS NDVI can be used to mask unreliable FF predictions. [::] Spatio-temporal dynamics of water management within Italian rice district are clearly depictable from FF maps.\n\n[Abstract]\n\nRecent changes in rice crop management within Northern Italy rice district led to a reduction of seeding in flooding condition, which may have an impact on reservoir water management and on the animal and plant communities that depend on the flooded paddies. Therefore, monitoring and quantifying the spatial and temporal variability of water presence in paddy fields is becoming important. In this study we present a method to estimate dynamics of presence of standing water (i.e. fraction of flooded area) in rice fields using MODIS data. First, we produced high resolution water presence maps from Landsat by thresholding the Normalised Difference Flood Index (NDFI) made: we made it by comparing five Landsat 8 images with field-obtained information about rice field status and water presence. Using these data we developed an empirical model to estimate the flooding fraction of each MODIS cell. Finally we validated the MODIS-based flooding maps with both Landsat and ground information. Results showed a good predictability of water surface from Landsat (OA = 92\\%) and a robust usability of MODIS data to predict water fraction (R2 = 0.73, EF = 0.57, RMSE = 0.13 at 1 × 1 km resolution). Analysis showed that the predictive ability of the model decreases with the greening up of rice, so we used NDVI to automatically discriminate estimations for inaccurate cells in order to provide the water maps with a reliability flag. Results demonstrate that it is possible to monitor water dynamics in rice paddies using moderate resolution multispectral satellite data. The achievement is a proof of concept for the analysis of MODIS archives to investigate irrigation dynamics in the last 15 years to retrieve information for ecological and hydrological studies.},\n keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14095798,~to-add-doi-URL,agricultural-resources,cereals,italy,modis,remote-sensing,rice,water-resources}\n}\n\n","author_short":["Ranghetti, L.","Busetto, L.","Crema, A.","Fasola, M.","Cardarelli, E.","Boschetti, M."],"key":"ranghettiTestingEstimationWater2016","id":"ranghettiTestingEstimationWater2016","bibbaseid":"ranghetti-busetto-crema-fasola-cardarelli-boschetti-testingestimationofwatersurfaceinitalianricedistrictfrommodissatellitedata","role":"author","urls":{"Paper":"https://doi.org/10.1016/j.jag.2016.06.018"},"keyword":["*imported-from-citeulike-INRMM","~INRMM-MiD:c-14095798","~to-add-doi-URL","agricultural-resources","cereals","italy","modis","remote-sensing","rice","water-resources"],"downloads":0},"bibtype":"article","biburl":"https://tmpfiles.org/dl/58794/INRMM.bib","creationDate":"2020-07-02T22:41:19.292Z","downloads":0,"keywords":["*imported-from-citeulike-inrmm","~inrmm-mid:c-14095798","~to-add-doi-url","agricultural-resources","cereals","italy","modis","remote-sensing","rice","water-resources"],"search_terms":["testing","estimation","water","surface","italian","rice","district","modis","satellite","data","ranghetti","busetto","crema","fasola","cardarelli","boschetti"],"title":"Testing Estimation of Water Surface in Italian Rice District from MODIS Satellite Data","year":null,"dataSources":["DXuKbcZTirdigFKPF"]}