Effective soil moisture sampling depth of L-band radiometry: A case study

https://hal.archives-ouvertes.fr/hal-03066927/document

Partitioning evapotranspiration of a drip-irrigated wheat crop: Inter-comparing eddy covariance-, sap flow-, lysimeter- and FAO-based methods

The next generation of weed seed germination models will need to account for variable soil microclimate conditions. To predict this microclimate environment we have developed a suite of individual tools (models) that can be used in conjunction with the next generation of weed seed germination models. The three tools that will be outlined here are GlobalTempSIM, GlobalRainSIM, and the soil temperature and moisture model (STM2). Each model was compared with several sets of observed data from worldwide locations. Overall, the climate predictors compared favorably. GlobalTempSIM had a bias between −2.7 and +0.9 C, mean absolute errors between 1.9 and 5.0 C, and an overall Willmott d-index of 0.79 to 0.95 (where d  =  1 represents total agreement between observed and modeled data) for 12 global validation sites in 2007. GlobalRainSIM had a bias for cumulative precipitation ranging from −210 to +305 mm, a mean absolute error between 29 and 311 mm, and a corresponding d-index of 0.78 to 0.99 for the sit...

Software tools for weed seed germination modeling.

The objective of this paper is to analyze the sensitivity of very high resolution TerraSAR-X radar data taken over bare soils to surface soil parameters and to study the spatial variability of these parameters at a fine scale (within a field plot). The relationship between the backscattering coefficient and the soil's parameters (moisture, surface roughness, texture, and local topography) was examined by means of four satellite images, as well as ground truth measurements, of each of the three agricultural plots, recorded during several field campaigns in the winter and spring of 2008. TerraSAR images demonstrate high potential for the identification of local variations of roughness and texture. An approach for the estimation of local moisture is proposed using an empirical method adapted to the scale of an individual field. The results show that, by using TerraSAR-X data to study bare agricultural fields, local variations in soil moisture can be retrieved with a root-mean-square error of 0.05 cm3 ·cm-3.

/pdf/analysis-of-local-variation-of-soil-surface-parameters-with-4lx15bn3ls.pdf

Analysis of Local Variation of Soil Surface Parameters With TerraSAR-X Radar Data Over Bare Agricultural Fields

Models of water movement in unsaturated soils require accurate representaƟ ons of the soil moisture retenƟ on and hydraulic conducƟ vity curves; however, commonly used laboratory methods and pedotransfer funcƟ ons (PTFs) are rarely verifi ed against fi eld condiƟ ons. In this study, we invesƟ gated the eff ects of using soil hydraulic property informaƟ on obtained from diff erent measurement and esƟ maƟ on techniques on one-dimensional model predicƟ ons of soil moisture content. Pairs of Ɵ me domain refl ectometry waveguides and tensiometers were installed at two depths in the side of a soil pit face to obtain in situ measurements. Undisturbed soil samples were taken near the instruments and subjected to parƟ cle size analysis, mulƟ step ouƞ low (MSO), and falling-head permeability tests to obtain esƟ mates of the soil moisture retenƟ on curves. Three scaling methods were then applied to improve the fi t of the various esƟ mates to the fi eld data. We found that soil hydraulic property esƟ mates obtained from inverse methods lead to the best simulaƟ ons of soil moisture dynamics, and that laboratory MSO tests or commonly used PTFs perform poorly. These laboratory and PTF esƟ mates can be dramaƟ cally improved, however, by simply constraining the range of possible moisture contents to the minimum and maximum measured in the fi eld. It appears that this method of scaling PTF results can be used to obtain soil hydraulic property inputs of suffi cient accuracy for plot-scale modeling eff orts without requiring expensive laboratory or in situ tests.

/pdf/the-effect-of-various-soil-hydraulic-property-estimates-on-12tblocvwn.pdf

The Effect of Various Soil Hydraulic Property Estimates on Soil Moisture Simulations

La degradation physique des sols agricoles et forestiers liee au tassement lors des passages d’engins est une preoccupation importante des acteurs de l’agriculture et de la foret. Mais au-dela de ce cercle, elle concerne egalement tous ceux qui se preoccupent des consequences environnementales de l’agriculture et de la gestion forestiere. En effet, le tassement des sols modifie a des consequences lourdes sur plusieurs processus tels que l’erosion hydrique, la denitrification, le stockage du carbone, la preservation de la biodiversite, la production. Dans ce cadre, le projet DST (Degradation physique des Sols agricoles et forestiers liee au Tassement) proposait cinq volets de recherche : impact, prevision, prevention, suivi et cartographie du tassement en France metropolitaine. Il a repose sur l’utilisation de modeles de tassement des sols et de fonctionnement du systeme sol/plante pour etudier les consequences sur les cultures et l’environnement, via la modification des proprietes (stockage, transfert, mecaniques) des sols. Ce projet transversal, associant des laboratoires de recherche travaillant en agriculture, en geophysique ou en genie civil et des organismes professionnels, s’est aussi appuye sur des experimentations variees, en laboratoire et sur le terrain. Les principaux resultats sont presentes dans cet article. Ils concernent : (i) l’impact du tassement, avec la mise en evidence d’un seuil d’indice des vides en sol limoneux (0,15 m3.m 3 dans les sols limoneux), au-dela duquel les performances des cultures et les proprietes hydrodynamiques sont a coup sur affectees, (ii) la modelisation, le projet ayant debouche sur la mise au point d’un modele 2-D de prevision de la deformation des sols, (iii) le suivi, avec l’evaluation de methodes geophysiques, non destructrices, permettant la cartographie des zones tassees, (iv) la prevention, a travers le developpement d’un outil de prevision de l’humidite du sol et des jours disponibles et, (v) la cartographie des zones a risques en France.

Dégradation physique des sols agricoles et forestiers liée au tassement : principaux résultats du projet GESSOL ADD-DST

[1] For many applications, soil water flow models require knowledge of soil properties and conditions that is difficult to obtain. In this study, we analyzed the impact of approximations on model prediction of soil water content when only limited soil property data are available. The TEC model of Chanzy and Bruckler (1993), representing soil heat and water flows, was selected since it is based on soil properties that can be estimated with the pedotransfer function, and it is driven by standard climatic data. The impact of the choices made for soil hydraulic properties, initial soil water conditions, and lower boundary conditions for a wide range of soil were studied. In this study, emphasis was placed on estimating moisture in the surface soil layer (0–30 cm) and was limited to wet and moderately dry soil conditions, which can be critical for applications in agriculture to determine trafficable days and in surface hydrology. The results from this study indicate the importance of soil hydraulic properties and moisture profile initialization, which may lead to errors larger than 0.10 m3 m−3 on soil moisture in the surface layer. The pedotransfer function proposed by Wosten (1997) led to the best simulated soil moisture in the top 30 cm, but initialization has a strong impact. Thus a “warming up” period of 70 days is needed to mitigate the initialization error. This duration can be reduced according to the amount of precipitation. We propose starting simulations 48 hours after the last significant rainfall with an initial water potential of −10 kPa. At the bottom of the soil, a gravitational flux at a depth of at least 80 cm is recommended to compute the boundary conditions. These recommendations for implementing a soil water transfer model were evaluated against independent experimental data, and a moisture accuracy (root mean square error) of 0.04 m3 m−3 was obtained.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2006WR005765

Accuracy of top soil moisture simulation using a mechanistic model with limited soil characterization

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Dégradation physique des sols agricoles et forestiers liée au tassement : principaux résultats du projet GESSOL ADD-DST

Accuracy of top soil moisture simulation using a mechanistic model with limited soil characterization