Extracting soilwater holding capacity parameters of a distributed agro-hydrological model from high resolution optical satellite observations series

Remote Sensing

Volumn 8, Issue 2, 2016, Pages

  Ferrant, Sylvain ^a   Bustillo, Vincent ^a,b   Burel, Enguerrand ^a   Salmon Monviola, Jordy ^c,d   Claverie, Martin ^e,f   Jarosz, Nathalie ^a,b   Yin, Tiangang ^a   Rivalland, Vincent ^a   Dedieu, Gérard ^a   Demarez, Valerie ^a   Ceschia, Eric ^a   Probst, Anne ^b   Al Bitar, Ahmad ^a   Kerr, Yann ^a   Probst, Jean Luc ^b   Durand, Patrick ^c,d   Gascoin, Simon ^a  

^a CESBIO   (France)

^b UNIVERSITÉ DE TOULOUSE   (France)

^c UMR SAS   (France)

^d AGROCAMPUS OUEST   (France)

^e UNIVERSITY OF MARYLAND   (United States)

^f NASA GODDARD SPACE FLIGHT CENTER   (United States)

Author keywords

Agro hydrological model; Formosat 2 time series; Leaf area index; Nitrate contamination; Nitrogen excess; Sentinel 2 type time series; Soil water holding capacity; Spatial calibration

Indexed keywords

CALIBRATION; CATCHMENTS; CLIMATE MODELS; CROPS; CULTIVATION; ENVIRONMENTAL IMPACT; HYDROLOGY; NITROGEN; REMOTE SENSING; RUNOFF; SATELLITE IMAGERY; SATELLITES; SOIL MOISTURE; SOILS; SURFACE WATERS; TIME SERIES;

FORMOSAT-2; HYDROLOGICAL MODELING; LEAF AREA INDEX; NITRATE CONTAMINATION; NITROGEN EXCESS; SOIL WATER HOLDINGS; SPATIAL CALIBRATION;

PIXELS;

EID: 84962497356     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8020154     Document Type: Article

References (59)

1. Bongiovanni, R.; Lowenberg-Deboer, J. Precision Agriculture and Sustainability. Precis. Agric. 2004, 5, 359-387.
2. Robert, P.C. Precision Agriculture: A challenge for crop nutrition management. Plant Soil 2002, 247, 143-149.
3. Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.J.; Strachan, I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 2004, 90, 337-352.
4. Houlès, V.; Guérif, M.; Mary, B. Elaboration of a Nitrogen nutrition indicator for winter wheat based on Leaf Area Index and chlorophyll content for making nitrogen recommendations. Eur. J. Agron. 2007, 27, 1-11.
5. Launay, M.; Guerif, M. Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agric. Ecosyst. Environ. 2005, 111, 321-339.
6. Jégo, G.; Pattey, E.; Liu, J. Using Leaf Area Index, Retrieved from Optical Imagery, in the STICS Crop Model for Predicting Yield and Biomass of Field Crops. Field Crops Res. 2012, 131, 63-74.
7. Morel, J.; Bégué, A.; Todoroff, P.; Martiné, J.-F.; Lebourgeois, V.; Petit, M. Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation. Eur. J. Agron. 2014, 61, 60-68.
8. Varella, H.; Guérif, M.; Buis, S.; Beaudoin, N. Soil Properties estimation by inversion of a crop model and observations on crops improves the prediction of agro-environmental variables. Eur. J. Agron. 2010, 33, 139-147.
9. Hank, T.B.; Bach, H.; Mauser, W. Using a remote sensing-supported hydro-agroecological model for field-scale simulation of heterogeneous crop growth and yield: Application for wheat in central Europe. Remote Sens. 2015, 7, 3934-3965.
10. Ferrant, S.; Gascoin, S.; Veloso, A.; Salmon-Monviola, J.; Claverie, M.; Rivalland, V.; Dedieu, G.; Demarez, V.; Ceschia, E.; Probst, J.L.; et al. Agro-hydrology and multi temporal high resolution remote sensing: Toward an explicit spatial processes calibration. Hydrol. Earth Syst. Sci. 2014, 18, 5219-5237.
11. Ulaby, F.T.; Batlivala, P.P.; Dobson, M.C. Microwave backscatter dependence on surface roughness, soil moisture, and soil texture: Part I-bare soil. IEEE Trans. Geosci. Electron. 1978, 16, 286-295.
12. Niang, M.A.; Nolin, M.C.; Jégo, G.; Perron, I. Digital Mapping of soil texture using RADARSAT-2 polarimetric synthetic aperture radar data. Soil Sci. Soc. Am. J. 2014, 78, 673-684.
13. Kruse, F.A. Use of airborne imaging spectrometer data to map minerals associated with hydrothermally altered rocks in the Northern Grapevine Mountains, Nevada, and California. Remote Sens. Environ. 1988, 24, 31-51.
14. Ben-Dor, E.; Taylor, R.G.; Hill, J.; Demattê, J.A.M.; Whiting, M.L.; Chabrillat, S.; Sommer, S. Imaging spectrometry for soil applications. Adv. Agron. 2008, 97, 321-392.
15. Zribi, M.; Hégarat-Mascle, S.; Ottlé, C.; Kammoun, B.; Guerin, C. Surface Soil moisture estimation from the synergistic use of the (multi-Incidence and Multi-Resolution) active microwave ERS wind scatterometer and SAR Data. Remote Sens. Environ. 2003, 86, 30-41.
16. Zribi, M.; Dechambre, M. A new empirical model to retrieve soil moisture and roughness from C-Band radar data. Remote Sens. Environ. 2003, 84, 42-52.
17. Sreelash, K.; Sekhar, M.; Ruiz, L.; Buis, S.; Bandyopadhyay, S. Improved modeling of groundwater recharge in agricultural watersheds using a combination of crop model and remote sensing. J. Indian Inst. Sci. 2013, 93, 189-207.
18. Refsgaard, J.; Thorsen, M.; Jensen, J.; Kleeschulte, S.; Hansen, S. Large scale modelling of groundwater contamination from nitrate leaching. J. Hydrol. 1999, 211, 117-140.
19. Arnold, J.G.; Allen, P.M.; Bernhardt, G. A comprehensive surface-groundwater flow model. J. Hydrol. 1993, 142, 47-69.
20. Whitehead, P.; Wilson, E.; Butterfield, D. A semi-distributed integrated nitrogen model for multiple source assessment in catchment. Part 1. model structure and process equations. Sci. Total Environ. 1998, 210/211, 547-558.
21. Beaujouan, V.; Durand, P.; Ruiz, L.; Aurousseau, P.; Cotteret, G. A hydrological model dedicated to topography-based simulation of nitrogen transfer and transformation: Rationale and application to the geomorphology-denitrification relationship. Hydrol. Process. 2002, 16, 493-507.
22. Ledoux, E.; Gomez, E.; Monget, J.; Viavattene, C.; Viennot, P.; Ducharne, A.; Benoit, M.; Mignolet, C.; Schott, C.; Mary, B. Agriculture and groundwater nitrate contamination in the Seine Basin. The STICS-MODCOU modelling chain. Sci. Total Environ. 2007, 375, 33-47.
23. Viaud, V.; Durand, P.; Merot, P.; Sauboua, E.; Saadi, Z. Modeling the impact of the spatial structure of a hedge network on the hydrology of a small catchment in a temperate climat. Agric. Water Manag. 2005, 74, 135-163.
24. Ferrant, S.; Caballero, Y.; Perrin, J.; Gascoin, S.; Dewandel, B.; Aulong, S.; Dazin, F.; Ahmed, S.; Maréchal, J.C. Projected impacts of climate change on farmers' extraction of groundwater from crystalline aquifers in South India. Sci. Rep. 2014, 4.
25. Perrin, J.; Ferrant, S.; Massuel, S.; Dewandel, B.; Marechal, J.C.; Aulong, S.; Ahmed, S. Assessing water availability in a semi-arid watershed of Southern India using a semi-distributed model. J. Hydrol. 2012, 460-461, 143-155.
26. Ferrant, S.; Oehler, F.; Durand, P.; Ruiz, L.; Salmon-Monviola, J.; Justes, E.; Dugast, P.; Probst, A.; Probst, J.L.; Sanchez-Perez, J.M. Understanding nitrogen transfer dynamics in a small agricultural catchment:Comparison of a distributed (TNT2) and a semi distributed (SWAT) modelling approaches. J. Hydrol. 2011, 406, 1-15.
27. Cheema, M.J.M.; Immerzeel,W.W.; Bastiaanssen,W. Spatial Quantification of groundwater abstraction in the Irrigated Indus Basin. Groundwater 2014, 52, 25-36.
28. Immerzeel, W.W.; Gaur, A.; Zwart, S.J. Integrating Remote sensing and a process-based hydrological model to evaluate water use and productivity in South Indian Catchment. Agric. Water Manag. 2008, 95, 11-24.
29. Martin, E.; Gascoin, S.; Grusson, Y.; Murgue, C.; Bardeau, M.; Anctil, F.; Ferrant, S.; Lardy, R.; LeMoigne, P.; Leenhardt, D.; et al. On the use of hydrological models and satellite data to study the water budget of river basins affected by human activities: Examples from the Garonne Basin of France. Surv. Geophys. 2016, in press.
30. Calvet, J.-C.; Noilhan, J.; Roujean, J.-L.; Bessemoulin, P.; Cabelguenne, M.; Olioso, A.; Wigneron, J.-P. An interactive vegetation SVAT model tested against data from six contrasting sites. Agric. For. Meteorol. 1998, 92, 73-95.
31. Canal, N.; Calvet, J.-C.; Decharme, B.; Carrer, D.; Lafont, S.; Pigeon, G. Evaluation of root water uptake in the ISBA-A-Gs land surface model using agricultural yield statistics over France. Hydrol Earth Syst Sci 2014, 18, 4979-4999.
32. Brisson, N.; Mary, B.; Ripoche, D.; Jeuffroy, H.; Ruget, F.; Nicollaud, B.; Gate, P.; Devienne-Barret, F.; Antonioletti, R.; Durr, C.; et al. Stics: A generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agron. Paris 1998, 18, 311-346.
33. Moreau, P.; Viaud, V.; Parnaudeau, V.; Salmon-Monviola, J.; Durand, P. An approach for global sensitivity analysis of a complex environmental model to spatial inputs and parameters: A case study of an agro-hydrological model. Environ. Model. Softw. 2013, 47, 74-87.
34. BVEA. Availabel oneline: http://www.ecolab.omp.eu/bvea/ (accessed on 14 February 2016).
35. Béziat, P.; Ceschia, E.; Dedieu, G. Carbon balance of three crop succession over two cropland sites in South West of France. Agric. For. Meteorol. 2009, 149, 1628-1645.
36. Tallec, T.; Béziat, P.; Jarosz, N.; Rivalland, V.; Ceschia, E. Crop's water use efficiencies in temperate climate:Comparison of stand, ecosystem and agronomical approaches. Agric. For. Meteorol. 2013, 168, 69-81.
37. Ferrant, S.; Laplanche, C.; Durbe, G.; Probst, A.; Dugast, P.; Durand, P.; Sanchez-Perez, J.M.; Probst, J.L. Continuous measurement of nitrate concentration in a highly event-responsive agricultural catchment in South-West of France: Is the gain of information useful? Hydrol. Proc. 2012, 12, 1751-1763.
38. Roussiez, V.; Probst, A.; Probst, J.-L. Significance of floods in metal dynamics and export in a small agricultural catchment. J. Hydrol. 2013, 499, 71-81.
39. Taghavi, L.; Probst, J.-L.; Merlina, G.; Marchand, A.-L.; Durbe, G.; Probst, A. Flood event impact on pesticide transfer in a small agricultural catchment (Montoussé at Auradé, South West France). Int. J. Environ. Anal. Chem. 2010, 90, 390-405.
40. Macary, F.; Almeida-Dias, J.; Uny, D.; Probst, A. Assessment of the Effects of best environmental practices on reducing pesticide contamination in surface water, using multi-criteria modelling combined with a GIS. Int. J. Multicriteria Decis. Mak. 2013, 3, 178-211.
41. 2 et La Composition Chimique Des Eaux de Surface. Ph.D. Thesis, Université de Toulouse, Toulouse, France, March 2008.
42. Chern, J.S.; Wu, A.M.; Lin, S.F. Lesson learned from Formosat-2 mission operations. Acta Astronaut. 2006, 59, 344-350.
43. Hagolle, O.; Dedieu, G.; Mougenot, B.; Debaeker, V.; Duchemin, B.; Meygret, A. Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: Application to formoat-2 images. Remote Sens. Environ. 2008, 112, 1689-1701.
44. Hagolle, O.; Huc, M.; Pascual, D.V.; Dedieu, G. A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENuS, LANDSAT and SENTINEL-2 Images. Remote Sens. Environ. 2010, 114, 1747-1755.
45. Claverie, M.; Demarez, V.; Duchemin, B.; Hagolle, O.; Ducrot, D.; Dejoux, J.F.; Huc, M.; Keravec, P.; Béziat, P.; Fieuzal, R.; et al. Maize and sunflower biomass estimation in Southwest France using high spatial and temporal resolution remote sensing data. Remote Sens. Environ. 2012, 124, 844-857.
46. Baret, F.; Hagolle, O.; Geiger, B.; Bicheron, P.; Miras, B.; Huc, M.; Berthelot, B.; Nino, F.;Weiss, M.; Samain, O.; et al. LAI, fAPAR and fCover CYCLOPES Global products derived from VEGETATION. Part 1: principles of the algorithm. Remote Sens. Environ. 2007, 3, 275-286.
47. Jacquemoud, S.; Verhoef, W.; Baret, F.; Bacour, C.; Zarco-Tejada, P.J.; Asner, G.P.; François, C.; Ustin, S.L. PROSPECT + SAIL models: A review of use for vegetation characterization. Remote Sens. Environ. 2009, 113, 56-66.
48. Claverie, M.; Vermote, E.F.; Weiss, M.; Baret, F.; Hagolle, O.; Demarez, V. Validation of coarse spatial resolution LAI and FAPAR time series over cropland in Southwest France. Remote Sens. Environ. 2013, 139, 216-230.
49. Beven, K. Distributed modelling in hydrology: Applications of topmodel concept. Adv. Hydrol. Process. 1997, 350, 59-79.
50. Burns, I. A model for predicting the redistribution of salts applied to fallow soils after excess of rainfall or evaporation. J. Soil Sci. 1974, 25, 165-178.
51. Sobol, I.M. Sensitivity estimates for non linear mathematical models. Math. Model. Comput. Exp. 1993, 1, 407-414.
52. Trnka, M.; Eitzinger, J.; Kapler, P.; Dubrovský, M.; Semerádová, D.; Žalud, Z.; Formayer, H. Effect of estimated daily global solar radiation data on the results of crop growth models. Sensors 2007, 7, 2330-2362.
53. Gmur, S.; Vogt, D.; Zabowski, D.; Moskal, L.M. Hyperspectral analysis of soil nitrogen, carbon, carbonate, and organic matter using regression trees. Sensors 2012, 12, 10639-10658.
54. Duchemin, B.; Maisongrande, P.; Boulet, G.; Benhadj, I. A Simple algorithm for yield estimates: evaluation for semi-arid irrigated winter wheat monitored with green Leaf Area Index. Environ. Model. Softw. 2008, 23, 876-892.
55. Claverie, M. Estimation spatialisee de la biomasse et des besoins en eau des cultures a l'aide de donnees satellitales a hautes resolutions spatiale et temporelle: Application Aux agrosystemes du sud-ouest de la France. Ph.D. Thesis, Université de Toulouse, Toulouse, France, 2012.
56. Grabs, T.; Seibert, J.; Bishop, K.; Laudon, H. Modeling spatial patterns of saturated areas: A comparison of the topographic wetness index and a dynamic distributed model. J. Hydrol. 2009, 373, 15-23.
57. Saadi, S.; Simonneaux, V.; Boulet, G.; Raimbault, B.; Mougenot, B.; Fanise, P.; Ayari, H.; Lili-Chabaane, Z. Monitoring irrigation consumption using high resolution NDVI image time series: Calibration and validation in the Kairouan Plain (Tunisia). Remote Sens. 2015, 7, 13005-13028.
58. Baghdadi, N.; Aubert, M.; Zribi, M. Use of TerraSAR-X data to retrieve soil moisture over bare soil agricultural fields. IEEE Geosci. Remote Sens. Lett. 2012, 9, 512-516.
59. Demarez, V.; Duthoit, S.; Baret, F.; Weiss, M.; Dedieu, G. Estimation of leaf area and clumping indexes of crops with hemispherical photographs. Agric. For. Meteorol. 2008, 148, 644-655.