Estimation of root-zone soil moisture using crop water stress index (CWSI) in agricultural fields

Akuraju, Venkata Radha; Ryu, Dongryeol; George, Biju

doi:10.1080/15481603.2021.1877009

Cited by 12 publications

(5 citation statements)

References 54 publications

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“…The crop water stress index (CWSI), which considers canopy temperature, is also commonly used to invert soil moisture. Akuraju et al [29] used the CWSI to invert the RZSM of Dookie farm, which was the average of soil moisture measured at four layers of 0-30 cm, 30-60 cm, 60-90 cm, and 90-120 cm, and the results showed that the estimated RZSM had errors of 3.9% and 5.3% for the two cropping seasons, respectively. Saeidi et al [30] constructed the correlation between the CWSI and RZSM using five regression models: linear, exponential, logarithmic, polynomial, and power exponential, and the results showed that the regression models with polynomial (R = 0.987) and power exponential (R = 0.65) estimated the soil moisture with relatively best accuracy.…”

Section: Statistical Regressionmentioning

confidence: 99%

A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing

Li,

Sun,

Zhao

2023

Remote Sensing

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Root zone soil moisture (RZSM) controls vegetation transpiration and hydraulic distribution processes and plays a key role in energy and water exchange between land surface and atmosphere; hence, accurate estimation of RZSM is crucial for agricultural irrigation management practices. Traditional methods to measure soil moisture at stations are laborious and spatially uneven, making it difficult to obtain soil moisture data on a large scale. Remote sensing techniques can provide soil moisture in a large-scale range, but they can only provide surface soil moisture (SSM) with a depth of approximately 5–10 cm. In order to obtain a large range of soil moisture for deeper soil layers, especially the crop root zone with a depth of about 100–200 cm, numerous methods based on remote sensing inversion have been proposed. This paper analyzes and summarizes the research progress of remote sensing-based RZSM estimation methods in the past few decades and classifies these methods into four categories: empirical methods, semi-empirical methods, physics-based methods, and machine learning methods. Then, the advantages and disadvantages of various methods are outlined. Additionally an outlook on the future development of RZSM estimation methods is made and discussed.

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Section: Statistical Regressionmentioning

confidence: 99%

A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing

Li,

Sun,

Zhao

2023

Remote Sensing

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“…For example, the shortwave infrared band (SWIR) which is available from most remote sensors is proposed to be useful for improving soil moisture estimates ( Sadeghi et al, 2017 ). A number of RS-derived indices, including Leaf Area Index (LAI), temperature vegetation condition index (TVDI), and crop water stress index (CWSI) ( Patel et al, 2009 ; Sánchez et al, 2012 ; Chen, Willgoose & Saco, 2015 ; Akuraju, Ryu & George, 2021 ), have also been reported to be suitable covariates for soil moisture modeling. Other commonly used covariates for soil moisture modeling include albedo, solar radiation, potential evapotranspiration, and soil hydraulic properties ( Table S1 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States

Xia

Watts

Machmuller

et al. 2022

PeerJ

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Background High-resolution soil moisture estimates are critical for planning water management and assessing environmental quality. In-situ measurements alone are too costly to support the spatial and temporal resolutions needed for water management. Recent efforts have combined calibration data with machine learning algorithms to fill the gap where high resolution moisture estimates are lacking at the field scale. This study aimed to provide calibrated soil moisture models and methodology for generating gridded estimates of soil moisture at multiple depths, according to user-defined temporal periods, spatial resolution and extent. Methods We applied nearly one million national library soil moisture records from over 100 sites, spanning the U.S. Midwest and West, to build Quantile Random Forest (QRF) calibration models. The QRF models were built on covariates including soil moisture estimates from North American Land Data Assimilation System (NLDAS), soil properties, climate variables, digital elevation models, and remote sensing-derived indices. We also explored an alternative approach that adopted a regionalized calibration dataset for the Western U.S. The broad-scale QRF models were independently validated according to sampling depths, land cover type, and observation period. We then explored the model performance improved with local samples used for spiking. Finally, the QRF models were applied to estimate soil moisture at the field scale where evaluation was carried out to check estimated temporal and spatial patterns. Results The broad-scale QRF model showed moderate performance (R2 = 0.53, RMSE = 0.078 m3/m3) when data points from all depth layers (up to 100 cm) were considered for an independent validation. Elevation, NLDAS-derived moisture, soil properties, and sampling depth were ranked as the most important covariates. The best model performance was observed for forest and pasture sites (R2 > 0.5; RMSE < 0.09 m3/m3), followed by grassland and cropland (R2 > 0.4; RMSE < 0.11 m3/m3). Model performance decreased with sampling depths and was slightly lower during the winter months. Spiking the national QRF model with local samples improved model performance by reducing the RMSE to less than 0.05 m3/m3 for grassland sites. At the field scale, model estimates illustrated more accurate temporal trends for surface than subsurface soil layers. Model estimated spatial patterns need to be further improved and validated with management data. Conclusions The model accuracy for top 0–20 cm soil depth (R2 > 0.5, RMSE < 0.08 m3/m3) showed promise for adopting the methodology for soil moisture monitoring. The success of spiking the national model with local samples showed the need to collect multi-year high frequency (e.g., hourly) sensor-based field measurements to improve estimates of soil moisture for a longer time period. Future work should improve model performance for deeper depths with additional hydraulic properties and use of locally-selected calibration datasets.

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“…Estimated CWSI has an exponential relationship with soil moisture and a linear relationship with soil water potential. This relationship between CWSI and soil moisture was used in a wheat field to inversely estimate root zone soil moisture, which has high spatio‐temporal variability and is difficult to measure with contemporary remote sensing capabilities (Akuraju et al., 2021).…”

Section: Sensors and Platforms For Observing Cz Disturbancementioning

confidence: 99%

Seeing the Disturbed Forest for the Trees: Remote Sensing Is Underutilized to Quantify Critical Zone Response to Unprecedented Disturbance

et al. 2023

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Understanding the severity and extent of near surface critical zone (CZ) disturbances and their ecosystem response is a pressing concern in the face of increasing human and natural disturbances. Predicting disturbance severity and recovery in a changing climate requires comprehensive understanding of ecosystem feedbacks among vegetation and the surrounding environment, including climate, hydrology, geomorphology, and biogeochemistry. Field surveys and satellite remote sensing have limited ability to effectively capture the spatial and temporal variability of disturbance and CZ properties. Technological advances in remote sensing using new sensors and new platforms have improved observations of changes in vegetation canopy structure and productivity; however, integrating measures of forest disturbance from various sensing platforms is complex. By connecting the potential for remote sensing technologies to observe different CZ disturbance vectors, we show that lower severity disturbance and slower vegetation recovery are more difficult to quantify. Case studies in montane forests from the western United States highlight new opportunities, including evaluating post‐disturbance forest recovery at multiple scales, shedding light on understory vegetation regrowth, detecting specific physiological responses, and refining ecohydrological modeling. Learning from regional CZ disturbance case studies, we propose future directions to synthesize fragmented findings with (a) new data analysis using new or existing sensors, (b) data fusion across multiple sensors and platforms, (c) increasing the value of ground‐based observations, (d) disturbance modeling, and (e) synthesis to improve understanding of disturbance.

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Estimation of root-zone soil moisture using crop water stress index (CWSI) in agricultural fields

Cited by 12 publications

References 54 publications

A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing

A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing

Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States

Seeing the Disturbed Forest for the Trees: Remote Sensing Is Underutilized to Quantify Critical Zone Response to Unprecedented Disturbance

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