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Estimating the Leaf Water Status and Grain Yield of Wheat under Different Irrigation Regimes Using Optimized Two- and Three-Band Hyperspectral Indices and Multivariate Regression Models. WATER 2021. [DOI: 10.3390/w13192666] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Spectral reflectance indices (SRIs) often show inconsistency in estimating plant traits across different growth conditions; thus, it is still necessary to develop further optimized SRIs to guarantee the performance of SRIs as a simple and rapid approach to accurately estimate plant traits. The primary goal of this study was to develop optimized two- and three-band vegetation- and water-SRIs and to apply different multivariate regression models based on these SRIs for accurately estimating the relative water content (RWC), gravimetric water content (GWCF), and grain yield (GY) of two wheat cultivars evaluated under three irrigation regimes (100%, 75%, and 50% of crop evapotranspiration (ETc)) for two seasons. Results showed that the three plant traits and all SRIs showed significant differences (p < 0.05) between the three irrigation treatments for each wheat cultivar. The three-band water-SRIs (NWIs-3b) showed the best performance in estimating the three plant traits for both cultivars (R2 > 0.80), and RWC and GWCF under 75% ETc (R2 ≥ 0.65). Four out of six three-band vegetation-SRIs (NDVIs-3b) performed better than any other SRIs for estimating GY under 100% ETc and 50% ETC, and RWC under 100% ETc (R2 ≥ 0.60). All types of SRIs demonstrated excellent performance in estimating the three plant traits (R2 ≥ 0.70) when the data of all growth conditions were combined and analyzed together. The NWIs-3b coupled with Random Forest models predicted the three plant traits with satisfactory accuracy for the calibration (R2 ≥ 0.96) and validation (R2 ≥ 0.93) datasets. The overall results of this study elucidate that extracting an optimized NWIs-3b from the full spectrum data and combined with an appropriate regression technique could be a practical approach for managing deficit irrigation regimes of crops through accurately, timely, and non-destructively monitoring the water status and final potential yield.
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Lassalle G. Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: Recommendations and guidelines based on a meta-review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 788:147758. [PMID: 34020093 DOI: 10.1016/j.scitotenv.2021.147758] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/05/2021] [Accepted: 05/10/2021] [Indexed: 06/12/2023]
Abstract
This review outlines the advances achieved in monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing over the last 50 years. A broad diversity of methods based on field and imaging spectroscopy were developed in that field for precision farming and environmental monitoring purposes. From the 466 articles reviewed, we identified the main factors to consider to achieve accurate monitoring of plant stress, namely: The plant species and the stressor to monitor, the goal (detection or quantification), and scale (field or broad-scale) of monitoring, and the need for controlled experiments. Based on these factors, we then provide recommendations and guidelines for the development of reliable methods to monitor 11 major biotic and abiotic plant stressors. For each stressor, the effects on plant health and reflectance are described and the most suited spectral regions, scale, spatial resolution, and processing approaches to achieve accurate monitoring are presented. As a perspective, we discuss two major components that should be implemented in future methods to improve stress monitoring: The discrimination of plant stressors with similar effects on plants and the transferability of the methods across scales.
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Affiliation(s)
- Guillaume Lassalle
- University of Campinas, UNICAMP, PO Box 6152, 13083-855 Campinas, SP, Brazil.
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Zhang J, Zhang W, Xiong S, Song Z, Tian W, Shi L, Ma X. Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content. PLANT METHODS 2021; 17:34. [PMID: 33789711 PMCID: PMC8011113 DOI: 10.1186/s13007-021-00737-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Accepted: 03/23/2021] [Indexed: 05/19/2023]
Abstract
BACKGROUND The leaf water content estimation model is established by hyperspectral technology, which is crucial and provides technical reference for precision irrigation. METHODS In this study, two consecutive years of field experiments (different irrigation times and seven wheat varieties) in 2018-2020 were performed to obtain the canopy spectra reflectance and leaf water content (LWC) data. The characteristic bands related to LWC were extracted from correlation coefficient method (CA) and x-Loading weight method (x-Lw). Five modeling methods, spectral index and four other methods (Partial Least-Squares Regression (PLSR), Random Forest Regression (RFR), Extreme Random Trees (ERT), and K-Nearest Neighbor (KNN)) based characteristic bands, were employed to construct LWC estimation models. RESULTS The results showed that the canopy spectral reflectance increased with the increase of irrigation times, especially in the near-infrared band (750-1350 nm). The prediction accuracy of the newly developed differential spectral index DVI (R1185, R1307) was higher than that of the existing spectral index, with R2 of 0.85 and R2 of 0.78 for the calibration and validation, respectively. Due to a large amount of hyperspectral data, the correlation coefficient method (CA) and x-Loading weight (x-Lw) were used to select the water characteristic bands (100 and 28 characteristic bands, respectively) from the full spectrum. We found that the accuracy of the model based on the characteristic bands was not significantly lower than that of the full spectrum-based models. Among these models, the ERT- x-Lw model performed the best (R2 and RMSE of 0.88 and 1.46; 0.84 and 1.62 for the calibration and validation, respectively). In addition, the accuracy of the LWC estimation model constructed by ERT-x-Lw was higher than that of DVI (R1185, R1307). CONCLUSION The two models based on ERT-x-Lw and DVI (R1185, R1307) can effectively predict wheat leaf water content. The results provide a technical reference and a basis for crop water monitoring and diagnosis under similar production conditions.
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Affiliation(s)
- Juanjuan Zhang
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, People's Republic of China
- Science College of Information and Management, Henan Agricultural University, #63 Nongye Road, Zhengzhou, Henan, 450002, People's Republic of China
| | - Wen Zhang
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, People's Republic of China
- College of Agronomy, Henan Agricultural University, #63 Nongye Road, Zhengzhou, Henan, 450002, People's Republic of China
- Science College of Information and Management, Henan Agricultural University, #63 Nongye Road, Zhengzhou, Henan, 450002, People's Republic of China
| | - Shuping Xiong
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, People's Republic of China
- College of Agronomy, Henan Agricultural University, #63 Nongye Road, Zhengzhou, Henan, 450002, People's Republic of China
- Science College of Information and Management, Henan Agricultural University, #63 Nongye Road, Zhengzhou, Henan, 450002, People's Republic of China
| | - Zhaoxiang Song
- Adelphi University, # One South Avenue, Garden City, NY, 11530-0701, USA
| | - Wenzhong Tian
- Luoyang of Agriculture and Forestry, #1 Nongke Road, Luoyang, 471000, Henan, People's Republic of China
| | - Lei Shi
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, People's Republic of China
- Science College of Information and Management, Henan Agricultural University, #63 Nongye Road, Zhengzhou, Henan, 450002, People's Republic of China
| | - Xinming Ma
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, People's Republic of China.
- College of Agronomy, Henan Agricultural University, #63 Nongye Road, Zhengzhou, Henan, 450002, People's Republic of China.
- Science College of Information and Management, Henan Agricultural University, #63 Nongye Road, Zhengzhou, Henan, 450002, People's Republic of China.
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Integration of Spectral Reflectance Indices and Adaptive Neuro-Fuzzy Inference System for Assessing the Growth Performance and Yield of Potato under Different Drip Irrigation Regimes. CHEMOSENSORS 2021. [DOI: 10.3390/chemosensors9030055] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Simultaneous and timely assessment of growth and water status-related plant traits is critical for precision irrigation management in arid regions. Here, we used proximal hyperspectral sensing tools to estimate biomass fresh weight (BFW), biomass dry weight (BDW), canopy water content (CWC), and total tuber yield (TTY) of two potato varieties irrigated with 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Plant traits were assessed remotely using published and newly constructed vegetation and water spectral reflectance indices (SRIs). We integrated genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS) models to predict the measured traits based on all SRIs. The different plant traits and SRIs varied significantly (p < 0.05) between the three irrigation regimes for the two varieties. The values of plant traits and majority SRIs showed a continuous decrease from the 100% ETc to the 50% ETc. Water-SRIs performed better than vegetation-SRIs for estimating the four plant traits. Almost all indices of the two SRI types had a weak relationship with the four plant traits (R2 = 0.00–0.37) under each irrigation regime. However, the majority of vegetation-SRIs and all water-SRIs showed strong relationships with BFW, CWC, and TTY (R2 ≥ 0.65) and moderate relationships with BDW (R2 ≥ 0.40) when the data of all irrigation regimes and varieties were analyzed together for each growing season or the data of all irrigation regimes, varieties, and seasons were combined together. The ANFIS-GA model predicted plant traits with satisfactory accuracy in both calibration (R2 = 1.0) and testing (R2 = 0.72–0.97) modes. The results indicate that SRI-based ANFIS models can improve plant trait estimation. This analysis also confirmed the benefits of applying GA to ANFIS to estimate plant responses to different growth conditions.
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Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status. REMOTE SENSING 2021. [DOI: 10.3390/rs13030536] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.
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El-Hendawy S, Al-Suhaibani N, Hassan W, Tahir M, Schmidhalter U. Hyperspectral reflectance sensing to assess the growth and photosynthetic properties of wheat cultivars exposed to different irrigation rates in an irrigated arid region. PLoS One 2017; 12:e0183262. [PMID: 28829809 PMCID: PMC5567659 DOI: 10.1371/journal.pone.0183262] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 08/01/2017] [Indexed: 11/18/2022] Open
Abstract
Simultaneous indirect assessment of multiple and diverse plant parameters in an exact and expeditious manner is becoming imperative in irrigated arid regions, with a view toward creating drought-tolerant genotypes or for the management of precision irrigation. This study aimed to evaluate whether spectral reflectance indices (SRIs) in three parts of the electromagnetic spectrum ((visible-infrared (VIS), near-infrared (NIR)), and shortwave-infrared (SWIR)) could be used to track changes in morphophysiological parameters of wheat cultivars exposed to 1.00, 0.75, and 0.50 of the estimated evapotranspiration (ETc). Significant differences were found in the parameters of growth and photosynthetic efficiency, and canopy spectral reflectance among the three cultivars subjected to different irrigation rates. All parameters were highly and significantly correlated with each other particularly under the 0.50 ETc treatment. The VIS/VIS- and NIR/VIS-based indices were sufficient and suitable for assessing the growth and photosynthetic properties of wheat cultivars similar to those indices based on NIR/NIR, SWIR/NIR, or SWIR/SWIR. Almost all tested SRIs proved to assess growth and photosynthetic parameters, including transpiration rate, more efficiently when regressions were analyzed for each water irrigation rate individually. This study, the type of which has rarely been conducted in irrigated arid regions, indicates that spectral reflectance data can be used as a rapid and non-destructive alternative method for assessment of the growth and photosynthetic efficiency of wheat under a range of water irrigation rates.
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Affiliation(s)
- Salah El-Hendawy
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
- Department of Agronomy, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt
| | - Nasser Al-Suhaibani
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Wael Hassan
- Department of Agricultural Botany, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt
- Department of Biology, Quwayiyah College of Science and Humanities, Shaqra University, Shaqra, Saudi Arabia
| | - Mohammad Tahir
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Urs Schmidhalter
- Chair of Plant Nutrition, Department of Plant Sciences, Technical University of Munich, Freising-Weihenstephan, Germany
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