Soil moisture estimates over sporadically flooded farmlands: synergies and biases of remote sensing and in situ sources

Cappelletti, L.; Sörensson, Anna A.; Salvia, M.; Ruscica, Romina; Spennemman, Pablo; Fernandez-Long, M.; Jobbágy, Estéban G.

doi:10.1080/01431161.2022.2152755

Cited by 2 publications

(1 citation statement)

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“…Within agricultural environments, soil moisture is essential information. Its monitoring is necessary to better understand soil conditions in the field, and to anticipate risks that are linked to moisture saturation or to drought [7][8][9]. However, such monitoring can be difficult and costly, given substantial spatial and temporal variation in soil moisture, which is incurred by the diversity of soil textures, precipitation regimes or other meteorological or surface characteristics.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Surface Soil Moisture over Wheat Fields during Growing Season Using C-Band Polarimetric SAR Data

Goïta,

Magagi,

Beauregard

et al. 2023

Remote Sensing

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Accurate estimation and regular monitoring of soil moisture is very important for many agricultural, hydrological, or climatological applications. Our objective was to evaluate potential contributions of polarimetry to soil moisture estimation during crop growing cycles using RADARSAT-2 C-band images. The research focused on wheat field data collected during Soil Moisture Active Passive Validation Experiment (SMAPVEX12) conducted in 2012 in Manitoba (Canada). A sensitivity analysis was performed to select the most relevant non-polarimetric and polarimetric variables extracted from RADARSAT-2, and statistical models were developed to estimate soil moisture. In fine, three models were developed and validated: a non-polarimetric model based on cross-polarized backscattering coefficient σHV0; a polarimetric mixed model using six polarimetric and non-polarimetric retained variables after the sensitivity analysis; and a simplified polarimetric mixed model considering only the phase difference (ϕHH−VV) and the co-polarized backscattering coefficient σHH0. The validation reveals significant positive contributions of polarimetry. It shows that the non-polarimetric model has a much larger error (RMSE = 0.098 m3/m3) and explains only 19% of observed soil moisture variation compared to the polarimetric mixed model, which has an error of 0.087 m3/m3, with an explained variance of 44%. The simplified model has the lowest error (0.074 m3/m3) and explains 53.5% of soil moisture variation.

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Section: Introductionmentioning

confidence: 99%