Integrating Early Growth Information to Monitor Winter Wheat Powdery Mildew Using Multi-Temporal Landsat-8 Imagery

Powdery mildew is one of the dominant diseases in winter wheat. The accurate monitoring of powdery mildew is important for crop management and production. Satellite-based remote sensing monitoring has been proven as an efficient tool for regional disease detection and monitoring. However, the information provided by single-date satellite scene is hard to achieve acceptable accuracy for powdery mildew disease, and incorporation of early period contextual information of winter wheat can improve this situation. In this study, a multi-temporal satellite data based powdery mildew detecting approach had been developed for regional disease mapping. Firstly, the Lansat-8 scenes that covered six winter wheat growth periods (expressed in chronological order as periods 1 to 6) were collected to calculate typical vegetation indices (VIs), which include disease water stress index (DSWI), optimized soil adjusted vegetation index (OSAVI), shortwave infrared water stress index (SIWSI), and triangular vegetation index (TVI). A multi-temporal VIs-based k-nearest neighbors (KNN) approach was then developed to produce the regional disease distribution. Meanwhile, a backward stepwise elimination method was used to confirm the optimal multi-temporal combination for KNN monitoring model. A classification and regression tree (CART) and back propagation neural networks (BPNN) approaches were used for comparison and validation of initial results. VIs of all periods except 1 and 3 provided the best multi-temporal data set for winter wheat powdery mildew monitoring. Compared with the traditional single-date (period 6) image, the multi-temporal images based KNN approach provided more disease information during the disease development, and had an accuracy of 84.6%. Meanwhile, the accuracy of the proposed approach had 11.5% and 3.8% higher than the multi-temporal images-based CART and BPNN models’, respectively. These results suggest that the use of satellite images for early critical disease infection periods is essential for improving the accuracy of monitoring models. Additionally, satellite imagery also assists in monitoring powdery mildew in late wheat growth periods.

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Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture

Agriculture ◽

10.3390/agriculture8070116 ◽

2018 ◽

Vol 8 (7) ◽

pp. 116 ◽

Cited By ~ 30

Author(s):

Alessandro Matese ◽

Salvatore Di Gennaro

Keyword(s):

Water Stress ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Stress Index ◽

Crop Water Stress Index ◽

Practical Applications ◽

Water Stress Index ◽

Sensing Applications ◽

Wide Range ◽

Precision Viticulture

High spatial ground resolution and highly flexible and timely control due to reduced planning time are the strengths of unmanned aerial vehicle (UAV) platforms for remote sensing applications. These characteristics make them ideal especially in the medium–small agricultural systems typical of many Italian viticulture areas of excellence. UAV can be equipped with a wide range of sensors useful for several applications. Numerous assessments have been made using several imaging sensors with different flight times. This paper describes the implementation of a multisensor UAV system capable of flying with three sensors simultaneously to perform different monitoring options. The intra-vineyard variability was assessed in terms of characterization of the state of vines vigor using a multispectral camera, leaf temperature with a thermal camera and an innovative approach of missing plants analysis with a high spatial resolution RGB camera. The normalized difference vegetation index (NDVI) values detected in different vigor blocks were compared with shoot weights, obtaining a good regression (R2 = 0.69). The crop water stress index (CWSI) map, produced after canopy pure pixel filtering, highlighted the homogeneous water stress areas. The performance index developed from RGB images shows that the method identified 80% of total missing plants. The applicability of a UAV platform to use RGB, multispectral and thermal sensors was tested for specific purposes in precision viticulture and was demonstrated to be a valuable tool for fast multipurpose monitoring in a vineyard.

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On the use of different approaches based on photochemical reflectance index and surface temperature to monitor the water status of winter wheat in semi-arid regions

10.5194/egusphere-egu2020-14106 ◽

2020 ◽

Author(s):

Zoubair Rafi ◽

Valérie Le Dantec ◽

Olivier Merlin ◽

Said Khabba ◽

Patrick Mordelet ◽

...

Keyword(s):

Water Stress ◽

Winter Wheat ◽

Surface Temperature ◽

Water Status ◽

Stress Index ◽

Coefficient Of Determination ◽

Temperature Index ◽

Water Stress Index ◽

Semi Arid

Agriculture is considered to be the human activity that consumes the most mobilized water on a global scale. However, crops planted in semi-arid areas regularly face periods of moderate to extreme water stress. Such water stress periods have a considerable impact on the seasonal yield of these crops. In order to participate in a more rational irrigation water management, monitoring of the rapid changes in plant water status is necessary. For this purpose, the combination of two different wavelength ranges will be explored : an index based on Xanthophyll cycle (Photochemical Reflectance Index, PRI) and a commonly-used index from thermal infrared spectral range (LST). An experiment on winter wheat was carried out over two agricultural campaigns (2016 to 2018) in the Haouz basin, which is located in the Marrakech region, to better assimilate the temporal dynamics of PRI and surface temperature. In this study, four different approaches are proposed to study the functioning of wheat : 1- an approach based on solar angle to remove the structure effect (PRI0) from the PRI signal and to derive a water stress index PRIj, 2- an approach based on global radiation (Rg) to extrapolate a theoretical PRI (PRIth) for Rg equal to zero and to calculate a water stress index PRIlin, 3- an approach that determines an optimal PRI (PRIpot) on the basis of the available water content (AWC) criterion in order to derive a stress index I-PRI and 4- an energy balance approach to extract dry and wet surface temperatures in order to establish a normalized surface temperature index (Tnorm). The results of this work show a strong correlation between the PRI0 and the Leaf Area Index with a coefficient of determination equal to 0.92, indicating that it is possible to isolate the structural effects of wheat on the PRI signal. In addition, over the range of variation in AWC, a significant correlation with PRIj, PRIjlin and I-PRI was observed with coefficients of determination of 0.71, 0.42 and 0.24, respectively. In contrast to the Tnorm, which varies only for values of AWC below 30%, a coefficient of determination of 0.22 is obtained. Finally, the PRI allows us to acquire early and complete information on the response of wheat to change in AWC as opposed to the surface temperature index, revealing the potential of the PRI to monitor the water status of plants and their responses to changing environmental conditions.

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Upscaling Evapotranspiration with Parsimonious Models in a North Carolina Vineyard

Agronomy ◽

10.3390/agronomy9030152 ◽

2019 ◽

Vol 9 (3) ◽

pp. 152 ◽

Cited By ~ 2

Author(s):

Christian Dold ◽

Joshua Heitman ◽

Gill Giese ◽

Adam Howard ◽

John Havlin ◽

...

Keyword(s):

North Carolina ◽

Water Stress ◽

Water Use ◽

Land Surface ◽

Vegetation Index ◽

Multiple Linear Regression Model ◽

Water Dynamics ◽

Stress Index ◽

Crop Water Stress Index ◽

Water Stress Index

Water stress can positively or negatively impact grape yield and yield quality, and there is a need for wine growers to accurately regulate water use. In a four-year study (2010–2013), energy balance fluxes were measured with an eddy-covariance (EC) system in a North Carolina vineyard (Vitis vinifera cv. Chardonnay), and evapotranspiration (ET) and the Crop Water Stress Index (CWSI) calculated. A multiple linear regression model was developed to upscale ET using air temperature (Ta), vapor pressure deficit (VPD), and Landsat-derived Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI). Daily ET reached values of up to 7.7 mm day−1, and the annual ET was 752 ± 59 mm, as measured with the EC system. The grapevine CWSI was between 0.53–0.85, which indicated moderate water stress levels. Median vineyard EVI was between 0.22 and 0.72, and the EVI range (max–min) within the vineyard was 0.18. The empirical models explained 75%–84% of the variation in ET, and all parameters had a positive linear relationship to ET. The Root Mean Square Error (RMSE) was 0.52–0.62 mm. This study presents easily applicable approaches to analyzing water dynamics and ET. This may help wine growers to cost-effectively quantify water use in vineyards.

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Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield

Agriculture ◽

10.3390/agriculture11100958 ◽

2021 ◽

Vol 11 (10) ◽

pp. 958

Author(s):

Anzhen Qin ◽

Dongfeng Ning ◽

Zhandong Liu ◽

Sen Li ◽

Ben Zhao ◽

...

Keyword(s):

Water Stress ◽

Winter Wheat ◽

Cropping System ◽

Stress Index ◽

Crop Water ◽

Crop Water Stress Index ◽

Summer Maize ◽

Water Stress Index ◽

Center Pivot ◽

Crop Water Stress

The temperature-based crop water stress index (CWSI) can accurately reflect the extent of crop water deficit. As an ideal carrier of onboard thermometers to monitor canopy temperature (Tc), center pivot irrigation systems (CPIS) have been widely used in precision irrigation. However, the determination of reliable CWSI thresholds for initiating the CPIS is still a challenge for a winter wheat–summer maize cropping system in the North China Plain (NCP). To address this problem, field experiments were carried out to investigate the effects of CWSI thresholds on grain yield (GY) and water use efficiency (WUE) of winter wheat and summer maize in the NCP. The results show that positive linear functions were fitted to the relationships between CWSI and canopy minus air temperature (Tc − Ta) (r2 > 0.695), and between crop evapotranspiration (ETc) and Tc (r2 > 0.548) for both crops. To make analysis comparable, GY and WUE data were normalized to a range of 0.0 to 1.0, corresponding the range of CWSI. With the increase in CWSI, a positive linear relationship was observed for WUE (r2 = 0.873), while a significant inverse relationship was found for the GY (r2 = 0.915) of winter wheat. Quadratic functions were fitted for both the GY (r2 = 0.856) and WUE (r2 = 0.629) of summer maize. By solving the cross values of the two GY and WUE functions for each crop, CWSI thresholds were proposed as being 0.252 for winter wheat, and 0.229 for summer maize, corresponding to a Tc − Ta threshold value of 0.925 and 0.498 °C, respectively. We conclude that farmers can achieve the dual goals of high GY and high WUE using the optimal thresholds proposed for a winter wheat–summer maize cropping system in the NCP.

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Evaluation of satellite based indices for primary production estimates in a sparse savanna in the Sudan

Biogeosciences Discussions ◽

10.5194/bgd-5-2985-2008 ◽

2008 ◽

Vol 5 (4) ◽

pp. 2985-3011 ◽

Cited By ~ 3

Author(s):

M. Sjöström ◽

J. Ardö ◽

L. Eklundh ◽

B. A. El-Tahir ◽

H. A. M. El-Khidir ◽

...

Keyword(s):

Water Stress ◽

Primary Production ◽

Satellite Data ◽

Vegetation Index ◽

Gross Primary Production ◽

Arid Ecosystems ◽

Stress Index ◽

Accurate Information ◽

Water Stress Index ◽

Production Modelling

Abstract. One of the more frequently applied methods for integrating controls on primary production through satellite data is the Light Use Efficiency (LUE) approach. Satellite indices such as the Enhanced Vegetation Index (EVI) and the Shortwave Infrared Water Stress Index (SIWSI) have previously shown promise as predictors of primary production in several different environments. In this study, we evaluate EVI and SIWSI derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor against in-situ measurements from central Sudan in order to asses their applicability in LUE-based primary production modelling within a water limited environment. Results show a strong correlation between EVI against gross primary production (GPP), demonstrating the significance of EVI for deriving information on primary production with relatively high accuracy at similar areas. Evaluation of SIWSI however, reveal that the fraction of vegetation apparently is to low for the index to provide accurate information on canopy water content, indicating that the use of SIWSI as a predictor of water stress in satellite data-driven primary production modelling in similar semi-arid ecosystems is limited.

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Vineyard Variability Analysis through UAV-Based Vigour Maps to Assess Climate Change Impacts

Agronomy ◽

10.3390/agronomy9100581 ◽

2019 ◽

Vol 9 (10) ◽

pp. 581 ◽

Cited By ~ 9

Author(s):

Luís Pádua ◽

Pedro Marques ◽

Telmo Adão ◽

Nathalie Guimarães ◽

António Sousa ◽

...

Keyword(s):

Climate Change ◽

Water Stress ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Crop Yields ◽

Moisture Stress ◽

Temporal Analysis ◽

Stress Index ◽

Crop Water Stress Index ◽

Multi Temporal

Climate change is projected to be a key influence on crop yields across the globe. Regarding viticulture, primary climate vectors with a significant impact include temperature, moisture stress, and radiation. Within this context, it is of foremost importance to monitor soils’ moisture levels, as well as to detect pests, diseases, and possible problems with irrigation equipment. Regular monitoring activities will enable timely measures that may trigger field interventions that are used to preserve grapevines’ phytosanitary state, saving both time and money, while assuring a more sustainable activity. This study employs unmanned aerial vehicles (UAVs) to acquire aerial imagery, using RGB, multispectral and thermal infrared sensors in a vineyard located in the Portuguese Douro wine region. Data acquired enabled the multi-temporal characterization of the vineyard development throughout a season through the computation of the normalized difference vegetation index, crop surface models, and the crop water stress index. Moreover, vigour maps were computed in three classes (high, medium, and low) with different approaches: (1) considering the whole vineyard, including inter-row vegetation and bare soil; (2) considering only automatically detected grapevine vegetation; and (3) also considering grapevine vegetation by only applying a normalization process before creating the vigour maps. Results showed that vigour maps considering only grapevine vegetation provided an accurate representation of the vineyard variability. Furthermore, significant spatial associations can be gathered through (i) a multi-temporal analysis of vigour maps, and (ii) by comparing vigour maps with both height and water stress estimation. This type of analysis can assist, in a significant way, the decision-making processes in viticulture.

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Monitoring Crop Water Status and Irrigation Needs Using Multispectral Airborne Sensors

HortScience ◽

10.21273/hortsci.30.4.905d ◽

1995 ◽

Vol 30 (4) ◽

pp. 905D-905

Author(s):

Thomas R. Clarke ◽

M. Susan Moran

Keyword(s):

Water Stress ◽

Near Infrared ◽

Water Status ◽

Canopy Cover ◽

Stress Index ◽

Crop Water ◽

Vegetative Cover ◽

Crop Water Stress Index ◽

Water Stress Index ◽

Subsurface Drip

Water application efficiency can be improved by directly monitoring plant water status rather than depending on soil moisture measurements or modeled ET estimates. Plants receiving sufficient water through their roots have cooler leaves than those that are water-stressed, leading to the development of the Crop Water Stress Index based on hand-held infrared thermometry. Substantial error can occur in partial canopies, however, as exposed hot soil contributes to deceptively warm temperature readings. Mathematically comparing red and near-infrared reflectances provides a measure of vegetative cover, and this information was combined with thermal radiance to give a two-dimensional index capable of detecting water stress even with a low percentage of canopy cover. Thermal, red, and near-infrared images acquired over subsurface drip-irrigated cantaloupe fields demonstrated the method's ability to detect areas with clogged emitters, insufficient irrigation rate, and system water leaks.

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An insight to the performance of crop water stress index for olive trees

Agricultural Water Management ◽

10.1016/j.agwat.2012.12.004 ◽

2013 ◽

Vol 118 ◽

pp. 79-86 ◽

Cited By ~ 47

Author(s):

N. Agam ◽

Y. Cohen ◽

J.A.J. Berni ◽

V. Alchanatis ◽

D. Kool ◽

...

Keyword(s):

Water Stress ◽

Stress Index ◽

Crop Water ◽

Crop Water Stress Index ◽

Water Stress Index ◽

Olive Trees ◽

Crop Water Stress

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Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

Remote Sensing ◽

10.3390/rs13061144 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1144

Author(s):

Mahendra Bhandari ◽

Shannon Baker ◽

Jackie C. Rudd ◽

Amir M. H. Ibrahim ◽

Anjin Chang ◽

...

Keyword(s):

Winter Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Grain Filling ◽

Unmanned Aerial Systems ◽

High Temporal Resolution ◽

Wheat Growth ◽

Spatial Domains ◽

Temporal And Spatial

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

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Water stress indices for the sugarcane crop on different irrigated surfaces

Revista Brasileira de Engenharia Agrícola e Ambiental ◽

10.1590/1807-1929/agriambi.v20n10p925-929 ◽

2016 ◽

Vol 20 (10) ◽

pp. 925-929 ◽

Cited By ~ 3

Author(s):

Rodrigo G. Brunini ◽

José E. P. Turco

Keyword(s):

Water Stress ◽

Raw Materials ◽

Irrigation System ◽

Canopy Temperature ◽

Ambient Air ◽

Saccharum Officinarum ◽

Stress Index ◽

Stress Indices ◽

Sugarcane Crop ◽

Water Stress Index

ABSTRACT Sugarcane (Saccharum officinarum L.) is a crop of vital importance to Brazil, in the production of sugar and ethanol, power generation and raw materials for various purposes. Strategic information such as topography and canopy temperature can provide management technologies accessible to farmers. The objective of this study was to determine water stress indices for sugarcane in irrigated areas, with different exposures and slopes. The daily water stress index of the plants and the water potential in the soil were evaluated and the production system was analyzed. The experiment was carried out in an “Experimental Watershed”, using six surfaces, two horizontal and the other ones with 20 and 40% North and South exposure slopes. Water stress level was determined by measuring the temperatures of the vegetation cover and the ambient air. Watering was carried out using a drip irrigation system. The results showed that water stress index of sugarcane varies according to exposure and slope of the terrain, while areas whose water stress index was above 5.0 oC had lower yield values.

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