Application of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring

Krishna, Gopal; Sahoo, Rabi Narayan; Singh, Pratika; Patra, Himesh; Bajpai, Vaishangi; Das, Bappa; Kumar, Sudhir; Dhandapani, R.; Vishwakarma, Chandrapal; Pal, Madan; Chinnusamy, Viswanathan

doi:10.1080/10106049.2019.1618922

Cited by 36 publications

(19 citation statements)

References 34 publications

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“…Compared with the full spectrum, the calibration model with sensitive bands selected by B+VIP had higher estimation accuracy and better stability. This indicated that variable selection could reduce model complexity and improve model performance due to the removal of irrelevant and interference variables (Krishna et al, 2019). The B-coefficient and VIP value of the PLSR model reflected the weight of variables in predicting PWC, and they can be used as the basis for band selection (Sharabian et al, 2014;Wang et al, 2017).…”

Section: Approaches To Sensitive Band Selectionmentioning

confidence: 99%

Potential of Multivariate Statistical Technique Based on the Effective Spectra Bands to Estimate the Plant Water Content of Wheat Under Different Irrigation Regimes

et al. 2021

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Real-time, nondestructive, and accurate estimation of plant water status is important to the precision irrigation of winter wheat. The objective of this study was to develop a method to estimate plant water content (PWC) by using canopy spectral proximal sensing data. Two experiments under different water stresses were conducted in 2014–2015 and 2015–2016. The PWC and canopy reflectance of winter wheat were collected at different growth stages (the jointing, booting, heading, flowering, and filling stages in 2015 and the jointing, booting, flowering, and filling stages in 2016). The performance of different spectral transformation approaches was further compared. Based on the optimal pretreatment, partial least squares regression (PLSR) and four combination methods [i.e., PLSR-stepwise regression (SR), PLSR-successive projections algorithm (SPA), PLSR-random frog (RF), and PLSR-uninformative variables elimination (UVE)] were used to extract the sensitive bands of PWC. The results showed that all transformed spectra were closely correlated to PWC. The PLSR models based on the first derivative transformation method exhibited the best performance (coefficient of determination in calibration, R2C = 0.96; root mean square error in calibration, RMSEC = 20.49%; ratio of performance to interquartile distance in calibration, RPIQC = 9.19; and coefficient of determination in validation, R2V = 0.86; root mean square error in validation, RMSEV = 46.27%; ratio of performance to interquartile distance in validation, RPIQV = 4.34). Among the combination models, the PLSR model established with the sensitive bands from PLSR-RF demonstrated a good performance for calibration and validation (R2C = 0.99, RMSEC = 11.53%, and RPIQC = 16.34; and R2V = 0.84, RMSEV = 44.40%, and RPIQV = 4.52, respectively). This study provides a theoretical basis and a reference for estimating PWC of winter wheat by using canopy spectral proximal sensing data.

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Section: Approaches To Sensitive Band Selectionmentioning

confidence: 99%

Potential of Multivariate Statistical Technique Based on the Effective Spectra Bands to Estimate the Plant Water Content of Wheat Under Different Irrigation Regimes

et al. 2021

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“…For instance, Xu, et al [21] used multispectral data derived from Landsat OLI and MODIS datasets to quantify crop water content with an optimal R 2 of 0.78. Additionally, Sibanda, Onisimo, Dube and Mabhaudhi [20] utilised Sentinel-2 MSI to estimate canopy water content using EWT and FMC to an rRMSE of 20.8% and 18.45%, respectively, while Krishna, et al [22] used the combination of hyperspectral sensors and partial least squares regression to estimate rice crop water stress with an R 2 of 0.94. However, despite these successes, the application of satellite data in characterising water indicators at farm scale is restricted by their relatively coarser spatial and temporal resolutions [23].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Estimation of Maize Leaf Water Content Using Machine Learning Techniques and Unmanned Aerial Vehicle (UAV)-Based Proximal and Remotely Sensed Data

et al. 2021

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Determining maize water content variability is necessary for crop monitoring and in developing early warning systems to optimise agricultural production in smallholder farms. However, spatially explicit information on maize water content, particularly in Southern Africa, remains elementary due to the shortage of efficient and affordable primary sources of suitable spatial data at a local scale. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit, near-real-time information for determining the maize crop water status at farm scale. Therefore, this study evaluated the utility of UAV-derived multispectral imagery and machine learning techniques in estimating maize leaf water indicators: equivalent water thickness (EWT), fuel moisture content (FMC), and specific leaf area (SLA). The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising the maize water indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC, and SLA were derived from the random forest regression (RFR) algorithm with an rRMSE of 3.13%, 1%, and 3.48%, respectively. Additionally, EWT and FMC yielded the highest predictive performance and were the most optimal indicators of maize leaf water content. The findings are critical towards developing a robust and spatially explicit monitoring framework of maize water status and serve as a proxy of crop health and the overall productivity of smallholder maize farms.

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“…Seasonally, it is important to detect episodic drought early enough to deploy irrigation and alleviate crop drought stress before actual yield losses occur [9]. Drought stress early in the season when the crop is juvenile typically results in reduced leaf area and light interception [10], which also reduces the maximum stomatal conductance (g s ) of the newly developed leaves [5,7], leading to reduced photosynthesis.…”

Section: Introductionmentioning

confidence: 99%

Diurnal and Seasonal Mapping of Water Deficit Index and Evapotranspiration by an Unmanned Aerial System: A Case Study for Winter Wheat in Denmark

et al. 2021

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Precision irrigation is a promising method to mitigate the impacts of drought stress on crop production with the optimal use of water resources. However, the reliable assessment of plant water status has not been adequately demonstrated, and unmanned aerial systems (UAS) offer great potential for spatiotemporal improvements. This study utilized UAS equipped with multispectral and thermal sensors to detect and quantify drought stress in winter wheat (Triticum aestivum L.) using the Water Deficit Index (WDI). Biennial field experiments were conducted on coarse sand soil in Denmark and analyses were performed at both diurnal and seasonal timescales. The WDI was significantly correlated with leaf stomatal conductance (R2 = 0.61–0.73), and the correlation was weaker with leaf water potential (R2 = 0.39–0.56) and topsoil water status (the highest R2 of 0.68). A semi-physical model depicting the relationship between WDI and fraction of transpirable soil water (FTSW) in the root zone was derived with R2 = 0.74. Moreover, WDI estimates were improved using an energy balance model with an iterative scheme to estimate the net radiation and land surface temperature, as well as the dual crop coefficient. The diurnal variation in WDI revealed a pattern of the ratio of actual to potential evapotranspiration, being higher in the morning, decreasing at noon hours and ‘recovering’ in the afternoon. Future work should investigate the temporal upscaling of evapotranspiration, which may be used to develop methods for site-specific irrigation recommendations.

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Application of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring

Cited by 36 publications

References 34 publications

Potential of Multivariate Statistical Technique Based on the Effective Spectra Bands to Estimate the Plant Water Content of Wheat Under Different Irrigation Regimes

Potential of Multivariate Statistical Technique Based on the Effective Spectra Bands to Estimate the Plant Water Content of Wheat Under Different Irrigation Regimes

A Comparative Estimation of Maize Leaf Water Content Using Machine Learning Techniques and Unmanned Aerial Vehicle (UAV)-Based Proximal and Remotely Sensed Data

Diurnal and Seasonal Mapping of Water Deficit Index and Evapotranspiration by an Unmanned Aerial System: A Case Study for Winter Wheat in Denmark

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