scholarly journals Assessing the Feasibility of Using Sentinel-2 Imagery to Quantify the Impact of Heatwaves on Irrigated Vineyards

2019 ◽  
Vol 11 (23) ◽  
pp. 2869 ◽  
Author(s):  
Alessia Cogato ◽  
Vinay Pagay ◽  
Francesco Marinello ◽  
Franco Meggio ◽  
Peter Grace ◽  
...  
Keyword(s):  
Heat Stress ◽  
Relative Humidity ◽  
Environmental Conditions ◽  
Vegetation Index ◽  
Growing Degree Days ◽  
Degree Days ◽  
Absorption Ratio ◽  
Red Edge ◽  
Medium Resolution ◽  
Chlorophyll Absorption

Heatwaves are common in many viticultural regions of Australia. We evaluated the potential of satellite-based remote sensing to detect the effects of high temperatures on grapevines in a South Australian vineyard over the 2016–2017 and 2017–2018 seasons. The study involved: (i) comparing the normalized difference vegetation index (NDVI) from medium- and high-resolution satellite images; (ii) determining correlations between environmental conditions and vegetation indices (Vis); and (iii) identifying VIs that best indicate heatwave effects. Pearson’s correlation and Bland–Altman testing showed a significant agreement between the NDVI of high- and medium-resolution imagery (R = 0.74, estimated difference −0.093). The band and the VI most sensitive to changes in environmental conditions were 705 nm and enhanced vegetation index (EVI), both of which correlated with relative humidity (R = 0.65 and R = 0.62, respectively). Conversely, SWIR (short wave infrared, 1610 nm) exhibited a negative correlation with growing degree days (R = −0.64). The analysis of heat stress showed that green and red edge bands—the chlorophyll absorption ratio index (CARI) and transformed chlorophyll absorption ratio index (TCARI)—were negatively correlated with thermal environmental parameters such as air and soil temperature and growing degree days (GDDs). The red and red edge bands—the soil-adjusted vegetation index (SAVI) and CARI2—were correlated with relative humidity. To the best of our knowledge, this is the first study demonstrating the effectiveness of using medium-resolution imagery for the detection of heat stress on grapevines in irrigated vineyards.

scholarly journals Vegetation phenology in Greenland and links to cryospheric change

2018 ◽  
Vol 59 (77) ◽  
pp. 59-68 ◽  
Author(s):  
Jeffery A. Thompson ◽  
Lora S. Koenig
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Greenland Ice Sheet ◽  
The Arctic ◽  
Growing Degree Days ◽  
Degree Days ◽  
Hydrologic Change ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cryospheric Change ◽  
Dry Out

ABSTRACTRecent greening of vegetation across the Arctic is associated with warming temperatures, hydrologic change and shorter snow-covered periods. Here we investigated trends for a subset of arctic vegetation on the island of Greenland. Vegetation in Greenland is unique due to its close proximity to the Greenland Ice Sheet and its proportionally large connection to the Greenlandic population through the hunting of grazing animals. The aim of this study was to determine whether or not longer snow-free periods (SFPs) were causing Greenlandic vegetation to dry out and become less productive. If vegetation was drying out, a subsequent aim of the study was to determine how widespread the drying was across Greenland. We utilized a 15-year time-series obtained by the MODerate Resolution Imaging Spectroradiometer (MODIS) to analyze the Greenland vegetation by deriving descriptors corresponding with the SFP, the number of cumulative growing degree-days and the time-integrated Normalized Difference Vegetation Index. While the productivity of most vegetated areas increased in response to longer growing periods, there were localized regions that exhibited signs consistent with the drying hypothesis. In these areas, vegetation productivity decreased in response to longer SFPs and more accumulated growing degree-days.

Investigating morpho-physiological traits and agro-meteorological indices in green gram [Vigna radiata L. Wilczek] for terminal heat tolerance

2016 ◽  
Author(s):  
Chandan Kumar ◽  
S. B. Mishra ◽  
Nilanjaya . ◽  
Chandra Mohan Singh
Keyword(s):  
Heat Stress ◽  
Seed Yield ◽  
Stress Condition ◽  
Growing Degree Days ◽  
Degree Days ◽  
Relative Temperature ◽  
Thermal Index ◽  
Heat Stress Condition ◽  
Use Efficiency ◽  
Temperature Depression

Forty genotypes of greengram were studied to ascertain the genetic variability and trait association among some important morpho-physiological traits and agro-meterological indices under heat stress condition. The results indicated that both GCV and PCV estimates were high for photo thermal index, heat use efficiency and seed yield. High heritability coupled with moderate genetic advance as per cent of mean was recorded for photo-thermal unit and relative temperature depression indicated that involvement of both additive and non-additive type of gene action and possibilities of effective selection for improvement of these traits. Seed yield showed significant and positive association with days to maturity, growing degree days, relative temperature depression and heat use efficiency. Based on variability, association and path analysis; heat use efficiency, maturity, photo thermal index and growing degree days were found most contributing indices/ traits should be considered as selection criteria for discrimination of outstanding greengram genotypes under heat stress condition.

scholarly journals Field-Scale Crop Seeding Date Estimation from MODIS Data and Growing Degree Days in Manitoba, Canada

2019 ◽  
Vol 11 (15) ◽  
pp. 1760 ◽  
Author(s):  
Taifeng Dong ◽  
Jiali Shang ◽  
Budong Qian ◽  
Jiangui Liu ◽  
Jing M. Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Crop Management ◽  
Remote Sensing Data ◽  
Growing Degree Days ◽  
Degree Days ◽  
Composite Surface ◽  
Modis Data ◽  
Seeding Date ◽  
Moderate Resolution Imaging Spectroradiometer

Information on crop seeding date is required in many applications; such as crop management and yield forecasting. This study presents a novel method to estimate crop seeding date at the field level from time-series 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data and growing degree days (GDD; base 5 °C; °C-days). The start of growing season (SOS) was first derived from time-series EVI2 (two-band Enhanced Vegetation Index) calculated from a MODIS 8-day composite surface reflectance product (MOD09Q1; Collection 6). Based on GDD; calculated from the Daymet gridded estimates of daily weather parameters; a simple model was developed to establish a linkage between the observed seeding date and the SOS. Calibration and validation of the model was conducted on three major crops; spring wheat; canola and oats; in the Province of Manitoba; Canada. The estimated SOS had a strong linear correlation with the observed seeding date; with a deviation of a few days depending on the year. The seeding date of the three crops can be calculated from the SOS by adjusting the number of days needed to accumulate GDD (AGDD) for emergence. The overall root-mean-square-difference (RMSD) of the estimated seeding date was less than 10 days. Validation showed that the accuracy of the estimated seeding date was crop-type independent. The developed method is useful for estimating the historical crop seeding date from remote sensing data in Canada; to support studies of the interactions among seeding date; crop management and crop yield under climate change. It is anticipated that this method can be adapted to other crops in other locations using the same or different satellite data.

scholarly journals Medium-Resolution Multispectral Data from Sentinel-2 to Assess the Damage and the Recovery Time of Late Frost on Vineyards

2020 ◽  
Vol 12 (11) ◽  
pp. 1896
Author(s):  
Alessia Cogato ◽  
Franco Meggio ◽  
Cassandra Collins ◽  
Francesco Marinello
Keyword(s):  
Recovery Time ◽  
Vegetation Index ◽  
Frost Damage ◽  
Total Acidity ◽  
Multispectral Data ◽  
Red Edge ◽  
Frost Event ◽  
Medium Resolution ◽  
Late Frost ◽  
Sentinel 2

In a climate-change context, the advancement of phenological stages may endanger viticultural areas in the event of a late frost. This study evaluated the potential of satellite-based remote sensing to assess the damage and the recovery time after a late frost event in 2017 in northern Italian vineyards. Several vegetation indices (VIs) normalized on a two-year dataset (2018–2019) were compared over a frost-affected area (F) and a control area (NF) using unpaired two-sample t-test. Furthermore, the must quality data (total acidity, sugar content and pH) of F and NF were analyzed. The VIs most sensitive in the detection of frost damage were Chlorophyll Absorption Ratio Index (CARI), Enhanced Vegetation Index (EVI), and Modified Triangular Vegetation Index 1 (MTVI1) (−5.26%, −16.59%, and −5.77% compared to NF, respectively). The spectral bands Near-Infrared (NIR) and Red Edge 7 were able to identify the frost damage (−16.55 and −16.67% compared to NF, respectively). Moreover, CARI, EVI, MTVI1, NIR, Red Edge 7, the Normalized Difference Vegetation Index (NDVI) and the Modified Simple Ratio (MSR) provided precise information on the full recovery time (+17.7%, +22.42%, +29.67%, +5.89%, +5.91%, +16.48%, and +8.73% compared to NF, respectively) approximately 40 days after the frost event. The must analysis showed that total acidity was higher (+5.98%), and pH was lower (−2.47%) in F compared to NF. These results suggest that medium-resolution multispectral data from Sentinel-2 constellation may represent a cost-effective tool for frost damage assessment and recovery management.

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

2019 ◽  
Vol 11 (8) ◽  
pp. 974 ◽  
Author(s):  
Cui ◽  
Zhao ◽  
Huang ◽  
Song ◽  
Ye ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Chlorophyll Content ◽  
Vegetation Index ◽  
Predictive Accuracy ◽  
Absorption Index ◽  
Spectral Indices ◽  
Crop Canopy ◽  
Leaf Chlorophyll ◽  
Red Edge ◽  
Chlorophyll Absorption

Leaf chlorophyll content (LCC) provides valuable information about the nutrition and photosynthesis statuses of crops. Vegetation index-based methods have been widely used in crop management studies for the non-destructive estimation of LCC using remote sensing technology. However, many published vegetation indices are sensitive to crop canopy structure, especially the leaf area index (LAI), when crop canopy spectra are used. Herein, to address this issue, we propose four new spectral indices (The red-edge-chlorophyll absorption index (RECAI), the red-edge-chlorophyll absorption index/optimized soil-adjusted vegetation index (RECAI/OSAVI), the red-edge-chlorophyll absorption index/ the triangular vegetation index (RECAI/TVI), and the red-edge-chlorophyll absorption index/the modified triangular vegetation index(RECAI/MTVI2)) and evaluate their performance for LCC retrieval by comparing their results with those of eight published spectral indices that are commonly used to estimate LCC. A total of 456 winter wheat canopy spectral data corresponding to physiological parameters in a wide range of species, growth stages, stress treatments, and growing seasons were collected. Five regression models (linear, power, exponential, polynomial, and logarithmic) were built to estimate LCC in this study. The results indicated that the newly proposed integrated RECAI/TVI exhibited the highest LCC predictive accuracy among all indices, where R2 values increased by more than 13.09% and RMSE values reduced by more than 6.22%. While this index exhibited the best association with LCC (0.708** ≤ r ≤ 0.819**) among all indices, RECAI/TVI exhibited no significant relationship with LAI (0.029 ≤ r ≤ 0.167), making it largely insensitive to LAI changes. In terms of the effects of different field management measures, the LCC predictive accuracy by RECAI/TVI can be influenced by erective winter wheat varieties, low N fertilizer application density, no water application, and early sowing dates. In general, the newly developed integrated RECAI/TVI was sensitive to winter wheat LCC with a reduction in the influence of LAI. This index has strong potential for monitoring winter wheat nitrogen status and precision nitrogen management. However, further studies are required to test this index with more diverse datasets and different crops.

scholarly journals USING A GEOGRAPHIC INFORMATION SYSTEM (GIS) TO CHARACTERIZE AND TO MAP GROWING SEASONS FOR VEGETABLES

HortScience ◽  
1992 ◽  
Vol 27 (11) ◽  
pp. 1177b-1177
Author(s):  
Joanne Logan ◽  
David L. Coffey
Keyword(s):  
Information System ◽  
Heat Stress ◽  
Geographic Information System ◽  
Growing Degree Days ◽  
Climatic Data ◽  
Vegetable Production ◽  
Degree Days ◽  
Fresh Market ◽  
Planting Dates ◽  
Growing Seasons

Vegetable production has become a multi-million dollar activity in Tennessee. The large number of options of planting dates and maturity classes of different vegetable species and cultivars result in a flexible, yet confusing, situation for the grower. A plentiful supply of vegetables for the processor, fresh market, or family table can be assured by the proper scheduling of planting and harvest of different crops and cultivars. Growers have very limited access to climatic data oriented to vegetable production in their locations. For the most part, they depend on planting maps on the backs of seed packets, or on extension bulletins with very general planting and harvest date recommendations. Tennessee consists of 4 climatic divisions that do not adequately describe the multitude of climates due to the diverse topography. The objective of this research was to create GIS maps of climatic variables important to vegetable production. Maps of temperatures, growing degree days, and rainfall, freeze and heat stress probabilities based on data from 72 locations in Tennessee were used to characterize the growing seasons for different vegetables.

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