Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize

Zhang, F.; Zhou, Guangsheng

doi:10.1186/s12898-019-0233-0

Cited by 134 publications

(120 citation statements)

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“…Manual phenotyping is time consuming, expensive and error prone, and should be replaced by advanced, rapid and accurate technology to assess dynamic biomass and N status to describe WUE and NUE (Zhang and Zhou, 2019). Therefore, it is important to establish usefulness of UAV-based remote sensing over the traditional phenotyping approaches.…”

Section: Potential Of Uav-based Multispectral Traits To Assess Water mentioning

confidence: 99%

“…It is due to laborious and error prone work across the season in case large number of genotypes which might mislead the agriculturist during selection. Previously, few studies have been conducted in detecting water and nutrient status of plant using non-destructive remote sensing from both ground and aerial platforms (Haile et al, 2012;Li et al, 2018;Zheng et al, 2018;Zhang and Zhou, 2019). But there is no report on practical application of aerial platform to predict the WUE and NUE for evaluating variations in genotypes under different water and N-supply levels.…”

Section: Significance Of Uav-based Prediction Of Wue and Nue For Genomentioning

confidence: 99%

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Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat

Yang

Hassan

et al. 2020

Front. Plant Sci.

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Unmanned aerial vehicle (UAV) based remote sensing is a promising approach for nondestructive and high-throughput assessment of crop water and nitrogen (N) efficiencies. In this study, UAV was used to evaluate two field trials using four water (T0 = 0 mm, T1 = 80 mm, T2 = 120 mm, and T3 = 160 mm), and four N (T0 = 0, T1 = 120 kg ha −1 , T2 = 180 kg ha −1 , and T3 = 240 kg ha −1) treatments, respectively, conducted on three wheat genotypes at two locations. Ground-based destructive data of water and N indictors such as biomass and N contents were also measured to validate the aerial surveillance results. Multispectral traits including red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), red-edge chlorophyll index (RECI) and normalized green red difference index (NGRDI) were recorded using UAV as reliable replacement of destructive measurements by showing high r values up to 0.90. NGRDI was identified as the most efficient non-destructive indicator through strong prediction values ranged from R 2 = 0.69 to 0.89 for water use efficiencies (WUE) calculated from biomass (WUE.BM), and R 2 = 0.80 to 0.86 from grain yield (WUE.GY). RNDVI was better in predicting the phenotypic variations for N use efficiency calculated from nitrogen contents of plant samples (NUE.NC) with high R 2 values ranging from 0.72 to 0.94, while NDRE was consistent in predicting both NUE.NC and NUE.GY by 0.73 to 0.84 with low root mean square errors. UAV-based remote sensing demonstrates that treatment T2 in both water 120 mm and N 180 kg ha −1 supply trials was most appropriate dosages for optimum uptake of water and N with high GY. Among three cultivars, Zhongmai 895 was highly efficient in WUE and NUE across the water and N treatments. Conclusively, UAV can be used to predict time-series WUE and NUE across the season for selection of elite genotypes, and to monitor crop efficiency under varying N and water dosages.

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Section: Potential Of Uav-based Multispectral Traits To Assess Water mentioning

confidence: 99%

Section: Significance Of Uav-based Prediction Of Wue and Nue For Genomentioning

confidence: 99%

Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat

Yang

Hassan

et al. 2020

Front. Plant Sci.

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“…Timely fertiliser application with water supply is essential for a successful crop. Spectral data from Remote Sensing (RS) have been studied for many years for an adequate assessment of nutrient and water variability for yield optimisation [4][5][6] RS can be an effective tool in monitoring crop production [7-9] and estimating yield [10,11]. Early estimation of yield may allow better planning and forecasting the market prices and support food security based on the regional, national and global demand and supply.…”

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confidence: 99%

“…RS allows collecting information about crop production using non-destructive methods [12] on a large scale for many fields at the same time. Hyperspectral (HS) RS provides continuous narrow spectral data from 400 to 2500 nm and have been proved to capture the variations in spectral response of the crop for the detection of nitrogen (N) content [13,14], biomass [15] and water stress [6,16]. Development of HS sensors and their application in estimating crop biomass from multi-year data [17] has gained increasing attention in the recent years.…”

mentioning

confidence: 99%

Multi-Temporal Monsoon Crop Biomass Estimation Using Hyperspectral Imaging

et al. 2019

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Hyperspectral remote sensing is considered to be an effective tool in crop monitoring and estimation of biomass. Many of the previous approaches are from single year or single date measurements, even though the complete crop growth with multiple years would be required for an appropriate estimation of biomass. The aim of this study was to estimate the fresh matter biomass (FMB) by terrestrial hyperspectral imaging of the three crops (lablab, maize and finger millet) under different levels of nitrogen fertiliser and water supply. Further, the importance of the different spectral regions for the estimation of FMB was assessed. The study was conducted in two experimental layouts (rainfed (R) and irrigated (I)) at the University of Agricultural Sciences, Bengaluru, India. Spectral images and the FMB were collected over three years (2016)(2017)(2018) during the growing season of the crops. Random forest regression method was applied to build FMB models. R 2 validation (R 2 val ) and relative root mean square error prediction (rRMSEP) was used to evaluate the FMB models. The Generalised model (combination of R and I data) performed better for lablab (R 2 val = 0.53, rRMSEP = 13.9%), maize (R 2 val = 0.53, rRMSEP = 18.7%) and finger millet (R 2 val = 0.46, rRMSEP = 18%) than the separate FMB models for R and I. In the best derived model, the most important variables contributing to the estimation of biomass were in the wavelength ranges of 546-910 nm (lablab), 750-794 nm (maize) and 686-814 nm (finger millet). The deviation of predicted and measured FMB did not differ much among the different levels of N and water supply. However, there was a trend of overestimation at the initial stage and underestimation at the later stages of crop growth.to local water availability [2]. Irrigated crop production is a major contributor to the green revolution, which has enabled the country to be self-sufficient [3], accompanied by fertiliser application and other inputs in semi-arid parts of India. Timely fertiliser application with water supply is essential for a successful crop. Spectral data from Remote Sensing (RS) have been studied for many years for an adequate assessment of nutrient and water variability for yield optimisation [4][5][6] RS can be an effective tool in monitoring crop production [7-9] and estimating yield [10,11]. Early estimation of yield may allow better planning and forecasting the market prices and support food security based on the regional, national and global demand and supply. RS allows collecting information about crop production using non-destructive methods [12] on a large scale for many fields at the same time. Hyperspectral (HS) RS provides continuous narrow spectral data from 400 to 2500 nm and have been proved to capture the variations in spectral response of the crop for the detection of nitrogen (N) content [13,14], biomass [15] and water stress [6,16]. Development of HS sensors and their application in estimating crop biomass from multi-year data [17] has gained increasing attention in the recen...

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“…Moreover, the spectral features of water indices reflected water stress to a certain extent [24]. Due to the self-regulation mechanism of crop, the sensitivity of crop to water stress are reflected on its growth and development [79]. The linear regression result illustrated that though the irrigation level was different at the early stage of planting, the SWP value of cotton was almost the same for the mature stage.…”

Section: Discussionmentioning

confidence: 97%

Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

et al. 2020

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Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R 2 = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R 2 = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred.have devastating impacts on the environment [3]. For agriculture, drought can endanger food security which can have critical effects on the economy, geopolitics, and society [5]. Drought can also negatively affect agriculture production especially in arid and semiarid regions [6]. Therefore, detecting crop water stress is of vital importance to improve agricultural quality and productivity.Cotton is cultivated in arid and semiarid regions and is the most important fiber crop in the world [7]. Cotton is susceptible to water stress in all growth stages, particularly during the flowering and boll development stages [8]. Moreover, water deficit is an important factor limiting growth and development in cotton, including plant height, leaf and root size, as well as economic yield and fiber quality [8]. To better understand the cotton's response and adaption to water stress, continuous monitoring of cotton water status in large a...

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Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize

Cited by 134 publications

References 50 publications

Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat

Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat

Multi-Temporal Monsoon Crop Biomass Estimation Using Hyperspectral Imaging

Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

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