Development of Crop Coefficients Using Remote Sensing-Based Vegetation Index and Growing Degree Days

2016 ◽  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Growing Degree Days ◽  
Degree Days ◽  
Crop Coefficients

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.

Vegetation index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems

2011 ◽  
Vol 25 (26) ◽  
pp. 4050-4062 ◽  
Author(s):  
Edward P. Glenn ◽  
Christopher M. U. Neale ◽  
Doug J. Hunsaker ◽  
Pamela L. Nagler
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Natural Ecosystems ◽  
Crop Coefficients

scholarly journals Exploring the Use of Vegetation Indices for Validating Crop Transpiration Fluxes Computed with the MOHID-Land Model. Application to Vineyard

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1228
Author(s):  
Tiago B. Ramos ◽  
Lucian Simionesei ◽  
Ana R. Oliveira ◽  
Ramiro Neves ◽  
Hanaa Darouich
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Soil Water ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Soil Water Balance ◽  
Growing Seasons ◽  
Crop Coefficients

The success of an irrigation decision support system (DSS) much depends on the reliability of the information provided to farmers. Remote sensing data can expectably help validate that information at the field scale. In this study, the MOHID-Land model, the core engine of the IrrigaSys DSS, was used to simulate the soil water balance in an irrigated vineyard located in southern Portugal during three growing seasons. Modeled actual basal crop coefficients and transpiration rates were then compared with the corresponding estimates derived from the normalized difference vegetation index (NDVI) computed from Sentinel-2 imagery. On one hand, the hydrological model was able to successfully estimate the soil water balance during the monitored seasons, exposing the need for improved irrigation schedules to minimize percolation losses. On the other hand, remote sensing products found correspondence with model outputs despite the conceptual differences between both approaches. With the necessary precautions, those products can be used to complement the information provided to farmers for irrigation of vine crop, further contributing to the regular validation of model estimates in the absence of field datasets.

scholarly journals FAO-56 dual approach combined with multi-sensor remote sensing for regional evapotranspiration estimations

2013 ◽  
Vol 10 (6) ◽  
pp. 8117-8144
Author(s):  
R. Amri ◽  
M. Zribi ◽  
Z. Lili-Chabaane ◽  
C. Szczypta ◽  
J. C. Calvet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
North Africa ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Dual Approach ◽  
Semi Arid Region ◽  
Crop Coefficients ◽  
The University ◽  
Regional Evapotranspiration

Abstract. The aim of this paper is to use a dual, modified version of the FAO-56 methodology for the estimation of regional evapotranspiration. The proposed approach combines the FAO-56 technique with remote sensing. Two vegetation classes are considered in the evapotranspiration estimations. In the case of cereals, crop coefficients and cover fractions are estimated using relationships established with the Normalized Difference Vegetation Index (NDVI), retrieved from SPOT-VGT data. In order to characterize the soil, a relationship is established between evaporation and the retrieved soil moisture values, based on the ERS/WSC products developed by the University of Vienna. This approach is applied to a semi-arid region in central Tunisia (North Africa) and is validated over 1991–2007 period using simulations from the ISBA-A-gs physical SVAT model. The ISBA soil moisture outputs are validated using remotely sensed ERS/WSC products. Finally, a comparison is made between the ISBA and FAO approaches, for the same studied site.

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.

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