Spatial mapping of growing degree days: an application of MODIS-based surface temperatures and enhanced vegetation index

2007 ◽  
Vol 1 (1) ◽  
pp. 013511 ◽  
Author(s):  
Quazi K. Hassan
Keyword(s):  
Vegetation Index ◽  
Spatial Mapping ◽  
Growing Degree Days ◽  
Degree Days ◽  
Enhanced Vegetation Index ◽  
Surface Temperatures

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.

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 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.

Evaluation of Finger Millet Varieties under Rainfed Condition of Eastern India

2017 ◽  
Vol 4 (03) ◽  
Author(s):  
M. K. Singh ◽  
VINOD KUMAR ◽  
SHAMBHU PRASAD
Keyword(s):  
Field Experiment ◽  
Finger Millet ◽  
Yield Potential ◽  
Growing Degree Days ◽  
Eastern India ◽  
Degree Days ◽  
Rainfed Condition ◽  
Net Returns ◽  
Humid Environment ◽  
Use Efficiency

A field experiment was carried out during the kharif of 2014 and 2015 to evaluate the yield potential, economics and thermal utilization in eleven finger millet varieties under the rainfed condition of the sub-humid environment of South Bihar of Eastern India. Results revealed that the significantly higher grain yield (20.41 q ha-1), net returns (Rs 25301) and B: C ratio (1.51) was with the finger millet variety ‘GPU 67’ but was being at par to ‘GPU28’and ‘RAU-8’, and significantly superior over remaining varieties. The highest heat units (1535.1oC day), helio-thermal units (7519.7oC day hours), phenothermal index (19.4 oC days day-1) were recorded with variety ‘GPU 67’ followed by ‘RAU 8’ and ‘GPU 28’ and lowest in ‘VL 149’ at 50 % anthesis stage. Similarly, the highest growing degree days (2100 oC day), helio-thermal units (11035.8 oC day hours) were noted with ‘GPU 67’ followed by ‘RAU 8’ and ‘GPU 28’ at maturity. The highest heat use efficiency (0.97 kg ha-1 oC day) and helio-thermal use efficiency (0.19 kg ha-1 oC day hour) were in ‘GPU 67’ followed by ‘VL 315’.

scholarly journals Determining the critical period for weed control in high-yielding cotton using common sunflower as a mimic weed

2019 ◽  
Vol 33 (6) ◽  
pp. 800-807 ◽  
Author(s):  
Graham W. Charles ◽  
Brian M. Sindel ◽  
Annette L. Cowie ◽  
Oliver G. G. Knox
Keyword(s):  
Weed Control ◽  
Critical Period ◽  
Yield Loss ◽  
Field Studies ◽  
Growing Degree Days ◽  
Degree Days ◽  
Weed Density ◽  
High Level ◽  
The Cost ◽  
Planting Season

AbstractField studies were conducted over six seasons to determine the critical period for weed control (CPWC) in high-yielding cotton, using common sunflower as a mimic weed. Common sunflower was planted with or after cotton emergence at densities of 1, 2, 5, 10, 20, and 50 plants m−2. Common sunflower was added and removed at approximately 0, 150, 300, 450, 600, 750, and 900 growing degree days (GDD) after planting. Season-long interference resulted in no harvestable cotton at densities of five or more common sunflower plants m−2. High levels of intraspecific and interspecific competition occurred at the highest weed densities, with increases in weed biomass and reductions in crop yield not proportional to the changes in weed density. Using a 5% yield-loss threshold, the CPWC extended from 43 to 615 GDD, and 20 to 1,512 GDD for one and 50 common sunflower plants m−2, respectively. These results highlight the high level of weed control required in high-yielding cotton to ensure crop losses do not exceed the cost of control.

scholarly journals Growth and Development of Purple Nutsedge Based on Days or Thermal Units

2015 ◽  
Vol 33 (2) ◽  
pp. 165-173 ◽  
Author(s):  
R.S.O. Lima ◽  
E.C.R. Machado ◽  
A.P.P. Silva ◽  
B.S. Marques ◽  
M.F. Gonçalves ◽  
...  
Keyword(s):  
Plant Growth ◽  
Mathematical Models ◽  
Growth And Development ◽  
Dry Matter ◽  
Cyperus Rotundus ◽  
Growing Degree Days ◽  
Plant Growth And Development ◽  
Degree Days ◽  
Purple Nutsedge ◽  
Independent Trials

This work was carried out with the objective of elaborating mathematical models to predict growth and development of purple nutsedge (Cyperus rotundus) based on days or accumulated thermal units (growing degree days). Thus, two independent trials were developed, the first with a decreasing photoperiod (March to July) and the second with an increasing photoperiod (August to November). In each trial, ten assessments of plant growth and development were performed, quantifying total dry matter and the species phenology. After that, phenology was fit to first degree equations, considering individual trials or their grouping. In the same way, the total dry matter was fit to logistic-type models. In all regressions four temporal scales possibilities were assessed for the x axis: accumulated days or growing degree days (GDD) with base temperatures (Tb) of 10, 12 and 15 oC. For both photoperiod conditions, growth and development of purple nutsedge were adequately fit to prediction mathematical models based on accumulated thermal units, highlighting Tb = 12 oC. Considering GDD calculated with Tb = 12 oC, purple nutsedge phenology may be predicted by y = 0.113x, while species growth may be predicted by y = 37.678/(1+(x/509.353)-7.047).

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