Soil moisture estimation from inverse modeling using multiple criteria functions

Charoenhirunyingyos, Sujittra; Honda, Kiyoshi; Kamthonkiat, Daroonwan; Ines, Amor Valeriano M.

doi:10.1016/j.compag.2010.12.004

Cited by 20 publications

(12 citation statements)

References 28 publications

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“…These results will improve contributions to crop models, which need SSM in soil layers deeper than 10 cm [2]. Rootzone moisture can be used as an index in early-warning systems (especially for drought) and for predicting crop yields [63]. Regardless of its importance, rootzone moisture is not always available for immediate applications in fields.…”

Section: Field Crop Resultsmentioning

confidence: 99%

Improvement of the Soil Moisture Retrieval Procedure Based on the Integration of UAV Photogrammetry and Satellite Remote Sensing Information

et al. 2021

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In countries characterized by arid and semi-arid climates, a precise determination of soil moisture conditions on the field scale is critically important, especially in the first crop growth stages, to schedule irrigation and to avoid wasting water. The objective of this study was to apply the operative methodology that allowed surface soil moisture (SSM) content in a semi-arid environment to be estimated. SSM retrieval was carried out by combining two scattering models (IEM and WCM), supplied by backscattering coefficients at the VV polarization obtained from the C-band Synthetic Aperture Radar (SAR), a vegetation descriptor NDVI obtained from the optical sensor, among other essential parameters. The inversion of these models was performed by Neural Networks (NN). The combined models were calibrated by the Sentinel 1 and Sentinel 2 data collected on bare soil, and in cereal, pea and onion crop fields. To retrieve SSM, these scattering models need accurate measurements of the roughness surface parameters, standard deviation of the surface height (hrms) and correlation length (L). This work used a photogrammetric acquisition system carried on Unmanned Aerial Vehicles (UAV) to reconstruct digital surface models (DSM), which allowed these soil roughness parameters to be acquired in a large portion of the studied fields. The obtained results showed that the applied improved methodology effectively estimated SSM on bare and cultivated soils in the principal early growth stages. The bare soil experimentation yielded an R2 = 0.74 between the estimated and observed SSMs. For the cereal field, the relation between the estimated and measured SSMs yielded R2 = 0.71. In the experimental pea fields, the relation between the estimated and measured SSMs revealed R2 = 0.72 and 0.78, respectively, for peas 1 and peas 2. For the onion experimentation, the highest R2 equaled 0.5 in the principal growth stage (leaf development), but the crop R2 drastically decreased to 0.08 in the completed growth phase. The acquired results showed that the applied improved methodology proves to be an effective tool for estimating the SSM on bare and cultivated soils in the principal early growth stages.

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Section: Field Crop Resultsmentioning

confidence: 99%

Improvement of the Soil Moisture Retrieval Procedure Based on the Integration of UAV Photogrammetry and Satellite Remote Sensing Information

et al. 2021

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“…whereas 3D heterogeneity in soil type (SSURGO, 2014) on one hand, and imprecise soil moisture quantification at different vertical layers at regional scale (Charoenhirunyingyos et al, 2011) on the other hand, will result in inaccuracy in the estimates (de Araújo et al, 2017).…”

Section: Crop Coefficient Methodsmentioning

confidence: 99%

A meteorological‐based crop coefficient model for estimation of daily evapotranspiration

Varmaghani

Eichinger

Prueger

2021

Hydrological Processes

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Estimation of daily evapotranspiration (ET) over cloudy regions highly desires models which rely on meteorological data only. Notwithstanding, the conventional crop coefficient (K c) method requires detailed knowledge of geo/biophysical properties of the coupled land-vegetation system, precipitation, and soil moisture. Six Eddy Covariance (EC) towers in Iowa, California and New Hampshire of the USA (covering corn, soybeans, prairie, and deciduous forest) were selected. Investigation on 6 years (2007-2012) 15-min micrometeorological records of these sites revealed that there is an indubitable strong interaction between relative humidity (RH), reference ET (ET o), and actual ET at different timescales. This allowed to bypass the need for the non-meteorological inputs and express K c as a second-order polynomial function of RH and ET o , the ambient regression evapotranspiration model (AREM). The coefficients of the empirical function are crop-specific and may require calibration over different soil types. The mean absolute percentage error (MAPE) of the regression against daily EC observations was 17% during the growing season, and 32% throughout the year with root mean square error (RMSE) of 0.74 mm day −1 and coefficient of determination of 0.71. The model was fully operational (MAPE of 34% and RMSE of 0.82 mm day −1) over the four Iowan sites based on inputs from local weather stations and NLDAS-2 forcing data of NASA. AREM was capable of capturing the dynamics of ET at 15-min and daily timescales irrespective of varying complexities associated with biophysical, geophysical and climatological states.

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“…4.2. Nevertheless, the matric potential at PWP can vary greatly among plant species, ranging from as low as − (Ridler et al 2012) Noah (Santanello et al 2007;Small 2007, 2010) SEtHyS (Coudert et al 2008) SWAP (Jhorar et al 2002(Jhorar et al , 2004Das et al 2008;Ines and Mohanty 2008;Singh et al 2010;Charoenhirunyingyos et al 2011;Shin et al 2013) WAVE (Ritter et al 2003) Forest 3-PG (Coops et al 2012) Crop models APSIM (Florin et al 2011) CERES (Link et al 2006;Braga and Jones 2004;Dente et al 2008) CropGro (Irmak et al 2001;Ferreyra et al 2006) STICS (Guérif et al 2006;Varella et al 2010aVarella et al , 2010bJégo et al 2012Jégo et al , 2015Sreelash et al 2012Sreelash et al , 2017Yemadje-Lammoglia et al 2018) Coupled soil-vegetation models Agro-hydrological models TNT2 (Ferrant et al 2016) 16,000 to -3500 kPa for xerophilic species to higher than -1000 kPa for hydrophilic species (Gobat et al 2004). The water content at -1500 kPa can thus differ from that "at the lowest limit in the field," as used in the definition of TTSW (Ratliff et al 1983).…”

Section: Permanent Wilting Pointmentioning

confidence: 99%

Available water capacity from a multidisciplinary and multiscale viewpoint. A review

Isabelle

Buis

Lagacherie

et al. 2022

Agron. Sustain. Dev.

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Soil–plant–atmosphere models and certain land surface models usually require information about the ability of soils to store and release water. Thus, a critical soil parameter for such reservoir-like models is the available water capacity (AWC), which is usually recognized as the most influential parameter when modeling water transfer. AWC does not have a single definition despite its wide use by scientists in research models, by regional managers as land-management tools and by farmers as decision-aid tools. Methods used to estimate AWC are also diverse, including laboratory measurements of soil samples, field monitoring, use of pedotransfer functions, and inverse modeling of soil-vegetation models. However, the resulting estimates differ and, depending on the method and scale, may have high uncertainty. Here, we review the many definitions of AWC, as well as soil and soil–plant approaches used to estimate it from local to larger spatial scales. We focus especially on the limits and uncertainties of each method. We demonstrate that in soil science, AWC represents a capacity—the size of the water reservoir that plants can use—whereas in agronomy, it represents an ability—the quantity of water that a plant can withdraw from the soil. We claim that the two approaches should be hybridized to improve the definitions and estimates of AWC. We also recommend future directions: (i) adapt pedotransfer functions to provide information about plants, (ii) integrate newly available information from soil mapping in spatial inverse-modeling applications, and (iii) integrate model-inversion results into methods for digital soil mapping.

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Soil moisture estimation from inverse modeling using multiple criteria functions

Cited by 20 publications

References 28 publications

Improvement of the Soil Moisture Retrieval Procedure Based on the Integration of UAV Photogrammetry and Satellite Remote Sensing Information

Improvement of the Soil Moisture Retrieval Procedure Based on the Integration of UAV Photogrammetry and Satellite Remote Sensing Information

A meteorological‐based crop coefficient model for estimation of daily evapotranspiration

Available water capacity from a multidisciplinary and multiscale viewpoint. A review

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