Remote sensing of soil moisture

Gruber, A.; Peng, Jian

doi:10.1016/b978-0-12-822974-3.00019-7

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…Yet, it is crucial to be aware of the limitations of remote sensing tools. Despite its indispensable role, remote sensing is constrained by factors such as coarse temporal and spatial resolutions, limited penetration depth, and incompatible governing hydrologic principles (Mohanty et al, 2017;Gruber and Peng, 2022). As an alternative, hydrological models have been commonly used to simulate and calibrate this variable in the context of agricultural-drought forecasts (Hao et al, 2018).…”

Section: Agricultural Droughtmentioning

confidence: 99%

Review article: Towards improved drought prediction in the Mediterranean region – modeling approaches and future directions

Zellou,

El Moçayd,

Bergou

2023

Nat. Hazards Earth Syst. Sci.

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Abstract. There is a scientific consensus that the Mediterranean region (MedR) is warming and as the temperature continues to rise, droughts and heat waves are becoming more frequent, severe, and widespread. Given the detrimental effects of droughts, it is crucial to accelerate the development of forecasting and early warning systems to minimize their negative impact. This paper reviews the current state of drought modeling and prediction applied in the MedR, including statistical, dynamical, and hybrid statistical–dynamical models. By considering the multifaceted nature of droughts, the study encompasses meteorological, agricultural, and hydrological drought forms and spans a variety of forecast scales, from weekly to annual timelines. Our objective is to pinpoint the knowledge gaps in literature and to propose potential research trajectories to improve the prediction of droughts in this region. The review finds that while each method has its unique strengths and limitations, hybrid statistical–dynamical models appear to hold the most promising potential for skillful prediction with seasonal to annual lead times. However, the application of these methods is still challenging due to the lack of high-quality observational data and the limited computational resources. Finally, the paper concludes by discussing the importance of using a combination of sophisticated methods such as data assimilation techniques, machine learning models, and copula models and of integrating data from different sources (e.g., remote sensing data, in situ measurements, and reanalysis) to improve the accuracy and efficiency of drought forecasting.

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Section: Agricultural Droughtmentioning

confidence: 99%

Review article: Towards improved drought prediction in the Mediterranean region – modeling approaches and future directions

Zellou,

El Moçayd,

Bergou

2023

Nat. Hazards Earth Syst. Sci.

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“…Knowledge about state variables of the Earth is a primary source of information when striving to model the state and dynamics of processes in the Earth and environment. Among others, soil moisture (SM) has been identified as an essential climate variable when studying land surface ecosystems (Gruber and Peng, 2022) and is, therefore, a variable of particular interest. SM measurements for local-and regional-scale studies are still often measured sparsely (Schröter et al, 2015), i.e., by sampling a low number of measurements at distinct points distributed over the survey area or along a few trajectories in a larger area, leaving large gaps of information in the resultant dataset.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic prediction by means of the propagation of response variable uncertainty through a Monte Carlo approach in regression random forest: Application to soil moisture regionalization

Dega

Dietrich

Schrön

et al. 2023

Front. Environ. Sci.

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Probabilistic predictions aim to produce a prediction interval with probabilities associated with each possible outcome instead of a single value for each outcome. In multiple regression problems, this can be achieved by propagating the known uncertainties in data of the response variables through a Monte Carlo approach. This paper presents an analysis of the impact of the training response variable uncertainty on the prediction uncertainties with the help of a comparison with probabilistic prediction obtained with quantile regression random forest. The result is an uncertainty quantification of the impact on the prediction. The approach is illustrated with the example of the probabilistic regionalization of soil moisture derived from cosmic-ray neutron sensing measurements, providing a regional-scale soil moisture map with data uncertainty quantification covering the Selke river catchment, eastern Germany.

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“…Soil moisture (SM) is an essential agricultural parameter used to estimate various environmental and agricultural activities such as climate change, drought prediction, and irrigation scheduling, especially for estimating water stress in agrarian land [1], [2]. Thus it has become the center of attraction among other agricultural research areas.…”

Section: Introductionmentioning

confidence: 99%

“…Calibration of the low-cost capacitive sensor has been performed in [1], [13] and validation with the gravimetric method shown for an automated soil moisture monitoring platform. Various investigation on the calibration method, calibration performance, and verification has been conducted in [14], [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis and Calibration of Low Cost Resistive and Capacitive Soil Moisture Sensor

Chowdhury¹,

Sen²,

Janardhanan³

2022

Preprint

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Soil moisture is an essential parameter in agriculture. It determines several environmental and agricultural activities such as climate change, drought prediction, irrigation, etc. Smart irrigation management requires continuous soil moisture monitoring to reduce unnecessary water usage. In recent times, the use of low-cost sensors is becoming popular among farmers for soil moisture monitoring. In this paper, a comparison of low-cost resistive and capacitive soil moisture sensors is demonstrated in two ways. One way is the calibration of the sensors in gravimetric and volumetric water content, and the other is the sensors' response analysis when different quantities of water are added to the same amount of soil. The analysis shown in this work is essential before choosing costeffective sensors for any soil moisture monitoring platform.

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Remote sensing of soil moisture

Cited by 5 publications

References 28 publications

Review article: Towards improved drought prediction in the Mediterranean region – modeling approaches and future directions

Review article: Towards improved drought prediction in the Mediterranean region – modeling approaches and future directions

Probabilistic prediction by means of the propagation of response variable uncertainty through a Monte Carlo approach in regression random forest: Application to soil moisture regionalization

Comparative Analysis and Calibration of Low Cost Resistive and Capacitive Soil Moisture Sensor

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