On the Synergy between Optical and TIR Observations for the Retrieval of Soil Moisture Content

Temimi, M.; Hain, Christopher; Zhan, Xiwu; Rabin, Robert M.; Anderson, Martha C.; Notarnicola, Claudia; Stepinski, Jan; Bonhomme, Amelise

doi:10.1201/b15610-19

Cited by 7 publications

(2 citation statements)

References 88 publications

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“…Accurate evapotranspiration Quantifying evapotranspiration is crucial for determining irrigation needs and the water footprint of pasture landscape systems [9][10][11][12][13]. A widely used approach to determine crop water consumption for irrigation management is through the estimation of ETa from the surface energy balance (SEB) based on high-resolution satellite observations of the land surface and meteorological data [14][15][16][17]. The SEB models can be categorized as one-source, two-layer, two-patch, dual-source, multi-patch, and multi-layer models.…”

Section: Introductionmentioning

confidence: 99%

Performance of Satellite-Based Evapotranspiration Models in Temperate Pastures of Southern Chile

Moletto‐Lobos

Mattar

Barichivich

2020

Water

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Farmers in the temperate zone of southern Chile have started to irrigate historically rainfed pastures during recent years to reduce dairy productivity losses against increasingly severe summer droughts. The lack of information on pasture water requirements (i.e., evapotranspiration), however, hampers the implementation of efficient irrigation programs. Here, we use in-situ observations to evaluate the skill of four remote sensing Surface Energy Balance (SEB) models and two satellite-based global evapotranspiration products (PML_V2 and GLEAM) to estimate actual evapotranspiration (ETa) of pastures in southern Chile during 2014–2017. Daily ETa measured at an evaluation site over the period ranges between 1.2 mm and 6.2 mm day−1 during the growing season (October–March), with an annual maximum of about 4.8 mm day−1 in January and a minimum 0.6 mm day−1 in June. Only the Simplified SEB (SEBS) model and its operational variant (SSEBop) and the PML_V2 global evapotranspiration product perform well, capturing 63–79% of the variance of in-situ evapotranspiration with an error between 0.75 mm day−1 and 1.1 mm day−1. The readily available PML_V2 product can be used as a convenient way to determine average water footprint of pastures and the two SEBs models can be implemented to monitor irrigation requirements in near-real time from field to regional scales. These results demonstrated a high potential of satellite observations for monitoring evapotranspiration and quantify the water footprint of pastures in southern Chile for a sustainable irrigation practice.

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Section: Introductionmentioning

confidence: 99%

Performance of Satellite-Based Evapotranspiration Models in Temperate Pastures of Southern Chile

Moletto‐Lobos

Mattar

Barichivich

2020

Water

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“…Many studies have focused on deriving surface soil moisture content from synergistic use of remote sensing data acquired simultaneously in the optical and thermal infrared spectrum. The so-called 'universal triangular relationship' is a widely used method for estimating soil moisture (Nichols et al, 2011;Sobrino et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Advances in Soil Moisture Retrieval from Multispectral Remote Sensing Using Unmanned Aircraft Systems and Machine Learning Techniques

Araya¹,

Fryjoff-Hung²,

Anderson³

et al. 2020

Preprint

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Abstract. We developed machine learning models to retrieve surface soil moisture (0–4 cm) from high resolution multispectral imagery using terrain attributes and local climate covariates. Using a small unmanned aircraft system (UAS) equipped with a multispectral sensor we captured high resolution imagery in part to create a high-resolution digital elevation model (DEM) as well as quantify relative vegetation photosynthetic status. We tested four different machine learning algorithms. The boosted regression tree algorithm gave the best prediction with mean absolute error of 3.8 % volumetric water content. The most important variables for the prediction of soil moisture were precipitation, reflectance in the red wavelengths, potential evapotranspiration, and topographic position indices (TPI). Our results demonstrate that the dynamics of soil water status across heterogeneous terrain may be adequately described and predicted by UAS remote sensing data and machine learning. Our modeling approach and the variable importance and relationships we have assessed in this study should be useful for management and environmental modeling tasks where spatially explicit soil moisture information is important.

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Soil Moisture Retrieved From a Combined Optical and Passive Microwave Approach

Mattar

Santamaría-Artigas

Sobrino

et al. 2016

Satellite Soil Moisture Retrieval

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On the Synergy between Optical and TIR Observations for the Retrieval of Soil Moisture Content

Cited by 7 publications

References 88 publications

Performance of Satellite-Based Evapotranspiration Models in Temperate Pastures of Southern Chile

Performance of Satellite-Based Evapotranspiration Models in Temperate Pastures of Southern Chile

Advances in Soil Moisture Retrieval from Multispectral Remote Sensing Using Unmanned Aircraft Systems and Machine Learning Techniques

Soil Moisture Retrieved From a Combined Optical and Passive Microwave Approach

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