scholarly journals Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV)

2017 ◽  
Vol 9 (8) ◽  
pp. 828 ◽  
Author(s):  
Suyoung Park ◽  
Dongryeol Ryu ◽  
Sigfredo Fuentes ◽  
Hoam Chung ◽  
Esther Hernández-Montes ◽  
...  
Keyword(s):  
Water Stress ◽  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Adaptive Estimation ◽  
Crop Water ◽  
High Resolution Imagery ◽  
Aerial Vehicle ◽  
Crop Water Stress

scholarly journals Simplified Evaluation of Cotton Water Stress Using High Resolution Unmanned Aerial Vehicle Thermal Imagery

2019 ◽  
Vol 11 (3) ◽  
pp. 267 ◽  
Author(s):  
Jiang Bian ◽  
Zhitao Zhang ◽  
Junying Chen ◽  
Haiying Chen ◽  
Chenfeng Cui ◽  
...  
Keyword(s):  
Water Stress ◽  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Canopy Temperature ◽  
Reference Temperature ◽  
Mild Stress ◽  
Crop Water ◽  
Aerial Vehicle ◽  
Irrigation Treatments ◽  
Crop Water Stress

Irrigation water management and real-time monitoring of crop water stress status can enhance agricultural water use efficiency, crop yield, and crop quality. The aim of this study was to simplify the calculation of the crop water stress index (CWSI) and improve its diagnostic accuracy. Simplified CWSI (CWSIsi) was used to diagnose water stress for cotton that has received four different irrigation treatments (no stress, mild stress, moderate stress, and severe stress) at the flowering and boll stage. High resolution thermal infrared and multispectral images were taken using an Unmanned Aerial Vehicle remote sensing platform at midday (local time 13:00), and stomatal conductance (gs), transpiration rate (tr), and cotton root zone soil volumetric water content (θ) were concurrently measured. The soil background pixels of thermal images were eliminated using the Canny edge detection to obtain a unimodal histogram of pure canopy temperatures. Then the wet reference temperature (Twet), dry reference temperature (Tdry), and mean canopy temperature (Tl) were obtained from the canopy temperature histogram to calculate CWSIsi. The other two methods of CWSI evaluation were empirical CWSI (CWSIe), in which the temperature parameters were determined by measuring natural reference cotton leaves, and statistical CWSI (CWSIs), in which Twet was the mean of the lowest 5% of canopy temperatures and Tdry was the air temperature (Tair) + 5 °C. Compared with CWSIe, CWSIs and spectral indices (NDVI, TCARI, OSAVI, TCARI/OSAVI), CWSIsi has higher correlation with gs (R2 = 0.660) and tr (R2 = 0.592). The correlation coefficient (R) for θ (0–45 cm) and CWSIsi is also high (0.812). The plotted high-resolution map of CWSIsi shows the different distribution of cotton water stress in different irrigation treatments. These findings demonstrate that CWSIsi, which only requires parameters from a canopy temperature histogram, may potentially be applied to precision irrigation management.

scholarly journals Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing

2019 ◽  
Vol 11 (6) ◽  
pp. 605 ◽  
Author(s):  
Liyuan Zhang ◽  
Huihui Zhang ◽  
Yaxiao Niu ◽  
Wenting Han
Keyword(s):  
Water Stress ◽  
High Resolution ◽  
Relative Humidity ◽  
Air Temperature ◽  
Vegetation Index ◽  
Multispectral Imagery ◽  
Crop Water ◽  
Significant Difference ◽  
Maturation Stages ◽  
Crop Water Stress

Mapping maize water stress status and monitoring its spatial variability at a farm scale are a prerequisite for precision irrigation. High-resolution multispectral images acquired from an unmanned aerial vehicle (UAV) were used to evaluate the applicability of the data in mapping water stress status of maize under different levels of deficit irrigation at the late vegetative, reproductive and maturation growth stages. Canopy temperature, field air temperature and relative humidity obtained by a handheld infrared thermometer and a portable air temperature/relative humidity meter were used to establish a crop water stress index (CWSI) empirical model under the weather conditions in Ordos, Inner Mongolia, China. Nine vegetation indices (VIs) related to crop water stress were derived from the UAV multispectral imagery and used to establish CWSI inversion models. The results showed that non-water-stressed baseline had significant difference in the reproductive and maturation stages with an increase of 2.1 °C, however, the non-transpiring baseline did not change significantly with an increase of 0.1 °C. The ratio of transformed chlorophyll absorption in reflectance index (TCARI) and renormalized difference vegetation index (RDVI), and the TCARI and soil-adjusted vegetation index (SAVI) had the best correlations with CWSI. R2 values were 0.47 and 0.50 for TCARI/RDVI and TCARI/SAVI at the reproductive and maturation stages, respectively; and 0.81 and 0.80 for TCARI/RDVI and TCARI/SAVI at the late reproductive and maturation stages, respectively. Compared to CWSI calculated by on-site measurements, CWSI values retrieved by VI-CWSI regression models established in this study had more abilities to assess the field variability of crop and soil. This study demonstrates the potentiality of using high-resolution UAV multispectral imagery to map maize water stress.

scholarly journals Monitoring vineyards with UAV and multi-sensors for the assessment of water stress and grape maturity

2017 ◽  
Vol 5 (2) ◽  
pp. 37-50 ◽  
Author(s):  
I. Soubry ◽  
P. Patias ◽  
V. Tsioukas
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Efficient Solution ◽  
Grape Variety ◽  
Agricultural Applications ◽  
Aerial Vehicle ◽  
Remote Sensing Indices

This paper deals with the monitoring of vineyards for the assessment of water stress and grape maturity using an unmanned aerial vehicle (UAV) equipped with multispectral/infrared and red-green-blue (RGB) cameras. The study area is the Gerovassiliou winery in the region of Epanomi, Greece, cultivated with the local grape variety of Malagouzia. Fifteen flights were conducted with a fixed-wing UAV during the months of April to August 2015 with a mean interval of 2 weeks. The flight images were photogrammetrically processed for the production of orthoimages and then used to extract indices for the detection of water stress. Grape samples were collected 2 days before harvest and then analyzed and correlated with remote sensing indices. The TCARI/OSAVI index showed the best correlation with the grape samples with regards to maturity and the likelihood of water stress. Furthermore, the final results were of high resolution as far as farm purposes are concerned (a scale of 1:500 for all three sensors). These facts suggest that the instruments used in this study represent a fast, reliable, and efficient solution to the evaluation of crops for agricultural applications.

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