A New Integrated Vegetation Index for the Estimation of Winter Wheat Leaf Chlorophyll Content

Cui, Bin; Zhao, Qian; Huang, Wenjiang; Song, Xiaoyu; Ye, Huichun; Zhou, Xuliang

doi:10.3390/rs11080974

Cited by 67 publications

(41 citation statements)

References 45 publications

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“…In large areas (like in present study), remote sensing-based vegetation indices (VI) are a key tool for this purpose. VI that make uses red and near infrared wavelengths in their calculations, such as the EVI, are very sensitivity to plant leaf area [92][93][94] and photosynthetic pigments [95][96][97] variations, which in turn, can be strongly affected by drought, heat, and excessive irradiance. In this sense, using the EVI conilon plantations is particularly useful to reflect some possible physiological disorders caused by drought and heat, for instance, fall of leaves, pigment degradation, and limited nutrient absorption capacity.…”

Section: Detection Of the Drought And Heat Effects Over Coffee Plantamentioning

confidence: 99%

Impact of drought associated with high temperatures on Coffea canephora plantations: a case study in Espírito Santo State, Brazil

Venâncio

Filgueiras

Mantovani

et al. 2020

Sci Rep

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Droughts are major natural disasters that affect many parts of the world all years and recently affected one of the major conilon coffee-producing regions of the world in state of Espírito Santo, which caused a huge crisis in the sector. Therefore, the objective of this study was to conduct an analysis with technical-scientific basis of the real impact of drought associated with high temperatures and irradiances on the conilon coffee (Coffea canephora Pierre ex Froehner) plantations located in the north, northwest, and northeast regions of the state of Espírito Santo, Brazil. Data from 2010 to 2016 of rainfall, air temperature, production, yield, planted area and surface remote sensing were obtained from different sources, statistically analyzed, and correlated. The 2015/2016 season was the most affected by the drought and high temperatures (mean annual above 26 °C) because, in addition to the adverse weather conditions, coffee plants were already damaged by the climatic conditions of the previous season. The increase in air temperature has higher impact (negative) on production than the decrease in annual precipitation. The average annual air temperatures in the two harvest seasons that stood out for the lowest yields (i.e. 2012/2013 and 2015/2016) were approximately 1 °C higher than in the previous seasons. In addition, in the 2015/2016 season, the average annual air temperature was the highest in the entire series. The spatial and temporal distribution of Enhanced Vegetation Index values enabled the detection and perception of droughts in the conilon coffee-producing regions of Espírito Santo. The rainfall volume accumulated in the periods from September to December and from April to August are the ones that most affect coffee yield. The conilon coffee plantations in these regions are susceptible to new climate extremes, as they continue to be managed under irrigation and full sun. The adoption of agroforestry systems and construction of small reservoirs can be useful to alleviate these climate effects, reducing the risk of coffee production losses and contributing to the sustainability of crops in Espírito Santo.

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Section: Detection Of the Drought And Heat Effects Over Coffee Plantamentioning

confidence: 99%

Impact of drought associated with high temperatures on Coffea canephora plantations: a case study in Espírito Santo State, Brazil

Venâncio

Filgueiras

Mantovani

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Remote sensing techniques can be useful for the estimation of plant health conditions, including monitoring the nutritional status [1][2][3][4], the stress response [5][6][7], plant count [8,9], yield prediction [10][11][12], chlorophyll content [13][14][15], pest and disease identification [16,17], and biomass estimation [18], among others. Multisensory data is often used to accomplish this task, including the ones acquired by orbital sensors, aircraft or Unnamed Aerial Vehicle (UAV)-embedded cameras, terrestrial sensors, and field spectroradiometers, known as proximal sensors [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…This definition is relatively important as it can guide future research towards the development of equipment specifically designed to measure these regions [23]. Another type of contribution is that it can assist in creating spectral vegetation indices or other simpler mathematical models that contribute to identifying the different characteristics of plants [13,28].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

et al. 2020

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This paper presents a framework based on machine learning algorithms to predict nutrient content in leaf hyperspectral measurements. This is the first approach to evaluate macro- and micronutrient content with both machine learning and reflectance/first-derivative data. For this, citrus-leaves collected at a Valencia-orange orchard were used. Their spectral data was measured with a Fieldspec ASD FieldSpec® HandHeld 2 spectroradiometer and the surface reflectance and first-derivative spectra from the spectral range of 380 to 1020 nm (640 spectral bands) was evaluated. A total of 320 spectral signatures were collected, and the leaf-nutrient content (N, P, K, Mg, S, Cu, Fe, Mn, and Zn) was associated with them. For this, 204,800 (320 × 640) combinations were used. The following machine learning algorithms were used in this framework: k-Nearest Neighbor (kNN), Lasso Regression, Ridge Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). The training methods were assessed based on Cross-Validation and Leave-One-Out. The Relief-F metric of the algorithms’ prediction was used to determine the most contributive wavelength or spectral region associated with each nutrient. This approach was able to return, with high predictions (R2), nutrients like N (0.912), Mg (0.832), Cu (0.861), Mn (0.898), and Zn (0.855), and, to a lesser extent, P (0.771), K (0.763), and S (0.727). These accuracies were obtained with different algorithms, but RF was the most suitable to model most of them. The results indicate that, for the Valencia-orange leaves, surface reflectance data is more suitable to predict macronutrients, while first-derivative spectra is better linked to micronutrients. A final contribution of this study is the identification of the wavelengths responsible for contributing to these predictions.

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“…Remote sensing of agricultural fields is important to assist its management through a low-cost and non-destructive approach. The usage of remote sensing systems supports data acquisition in a more frequent and faster manner, being more valuable to evaluate plants than most traditional agronomic procedures [1,2]. In the nutritional analysis, different remote sensing techniques were evaluated recently [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

et al. 2019

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The traditional method of measuring nitrogen content in plants is a time-consuming and labor-intensive task. Spectral vegetation indices extracted from unmanned aerial vehicle (UAV) images and machine learning algorithms have been proved effective in assisting nutritional analysis in plants. Still, this analysis has not considered the combination of spectral indices and machine learning algorithms to predict nitrogen in tree-canopy structures. This paper proposes a new framework to infer the nitrogen content in citrus-tree at a canopy-level using spectral vegetation indices processed with the random forest algorithm. A total of 33 spectral indices were estimated from multispectral images acquired with a UAV-based sensor. Leaf samples were gathered from different planting-fields and the leaf nitrogen content (LNC) was measured in the laboratory, and later converted into the canopy nitrogen content (CNC). To evaluate the robustness of the proposed framework, we compared it with other machine learning algorithms. We used 33,600 citrus trees to evaluate the performance of the machine learning models. The random forest algorithm had higher performance in predicting CNC than all models tested, reaching an R2 of 0.90, MAE of 0.341 g·kg−1 and MSE of 0.307 g·kg−1. We demonstrated that our approach is able to reduce the need for chemical analysis of the leaf tissue and optimizes citrus orchard CNC monitoring.

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A New Integrated Vegetation Index for the Estimation of Winter Wheat Leaf Chlorophyll Content

Cited by 67 publications

References 45 publications

Impact of drought associated with high temperatures on Coffea canephora plantations: a case study in Espírito Santo State, Brazil

Impact of drought associated with high temperatures on Coffea canephora plantations: a case study in Espírito Santo State, Brazil

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

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