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 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.

CHLOROGENIC ACID CONTENT OF SUNFLOWER SEED FLOUR AS AFFECTED BY SEEDING AND HARVEST DATE

1976 ◽  
Vol 56 (4) ◽  
pp. 901-905 ◽  
Author(s):  
D. G. DORRELL
Keyword(s):  
Chlorogenic Acid ◽  
Acid Content ◽  
Vegetative Growth ◽  
Sunflower Seed ◽  
Growing Season ◽  
Growing Degree Days ◽  
Degree Days ◽  
Normal Field ◽  
Seed Flour ◽  
Seeding Date

The effect of seeding date on the chlorogenic acid content of sunflower seed flour was determined by seeding the cultivars Krasnodarets and Peredovik at seven dates, starting on 14 May, over 3 yr. Sequential plantings were made at increments of approximately 70 growing degree days (base = 5.6 C). Plants were harvested at normal field maturity. The time and rate of deposition of chlorogenic acid was determined by harvesting plants at 7-day intervals from 21 to 49 days after flowering. The seeds were dehulled and defatted before determining the chlorogenic acid content of the flour. Chlorogenic acid content declined steadily from an average of 4.22% for the first seeding to 3.30% for the last seeding. About one-half of the total chlorogenic acid was present 21 days after flowering. Deposition continued rapidly for the next 14 days then the level began to stabilize. Delay in seeding tended to shorten the period of vegetative growth and shift the deposition of chlorogenic acid to a cooler portion of the growing season. It is suggested that a combination of these factors caused the reduction in chlorogenic acid content of sunflower flour.

scholarly journals Changes in Vegetation Phenology and Productivity in Alaska Over the Past Two Decades

2020 ◽  
Vol 12 (10) ◽  
pp. 1546 ◽  
Author(s):  
Christopher Potter ◽  
Olivia Alexander
Keyword(s):  
Time Series ◽  
Coastal Plain ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Data Sets ◽  
Vegetation Phenology ◽  
Vegetation Growth ◽  
The Past ◽  
Moderate Resolution Imaging Spectroradiometer

Understanding trends in vegetation phenology and growing season productivity at a regional scale is important for global change studies, particularly as linkages can be made between climate shifts and the vegetation’s potential to sequester or release carbon into the atmosphere. Trends and geographic patterns of change in vegetation growth and phenology from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets were analyzed for the state of Alaska over the period 2000 to 2018. Phenology metrics derived from the MODIS Normalized Difference Vegetation Index (NDVI) time-series at 250 m resolution tracked changes in the total integrated greenness cover (TIN), maximum annual NDVI (MAXN), and start of the season timing (SOST) date over the past two decades. SOST trends showed significantly earlier seasonal vegetation greening (at more than one day per year) across the northeastern Brooks Range Mountains, on the Yukon-Kuskokwim coastal plain, and in the southern coastal areas of Alaska. TIN and MAXN have increased significantly across the western Arctic Coastal Plain and within the perimeters of most large wildfires of the Interior boreal region that burned since the year 2000, whereas TIN and MAXN have decreased notably in watersheds of Bristol Bay and in the Cook Inlet lowlands of southwestern Alaska, in the same regions where earlier-trending SOST was also detected. Mapping results from this MODIS time-series analysis have identified a new database of localized study locations across Alaska where vegetation phenology has recently shifted notably, and where land cover types and ecosystem processes could be changing rapidly.

scholarly journals Estimation of different data compositions for early-season crop type classification

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4834 ◽  
Author(s):  
Pengyu Hao ◽  
Mingquan Wu ◽  
Zheng Niu ◽  
Li Wang ◽  
Yulin Zhan
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Classification Performance ◽  
Distribution Maps ◽  
Production Forecasting ◽  
Crop Type ◽  
Ndvi Time Series ◽  
Multi Temporal

Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer’s accuracies (PAs) and user’s accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.

scholarly journals Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil

2012 ◽  
Vol 47 (9) ◽  
pp. 1270-1278 ◽  
Author(s):  
Daniel de Castro Victoria ◽  
Adriano Rolim da Paz ◽  
Alexandre Camargo Coutinho ◽  
Jude Kastens ◽  
J. Christopher Brown
Keyword(s):  
Time Series ◽  
Crop Production ◽  
Vegetation Index ◽  
The State ◽  
Mato Grosso ◽  
Cropland Area ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Production Areas

The objective of this work was to evaluate a simple, semi‑automated methodology for mapping cropland areas in the state of Mato Grosso, Brazil. A Fourier transform was applied over a time series of vegetation index products from the moderate resolution imaging spectroradiometer (Modis) sensor. This procedure allows for the evaluation of the amplitude of the periodic changes in vegetation response through time and the identification of areas with strong seasonal variation related to crop production. Annual cropland masks from 2006 to 2009 were generated and municipal cropland areas were estimated through remote sensing. We observed good agreement with official statistics on planted area, especially for municipalities with more than 10% of cropland cover (R² = 0.89), but poor agreement in municipalities with less than 5% crop cover (R² = 0.41). The assessed methodology can be used for annual cropland mapping over large production areas in Brazil.

scholarly journals Vegetation response to upstream water yield in the Heihe river by time series analysis of MODIS data

2010 ◽  
Vol 7 (4) ◽  
pp. 4177-4218 ◽  
Author(s):  
L. Jia ◽  
H. Shang ◽  
M. Menenti
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Time Series Analysis ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Water Yield ◽  
Heihe River ◽  
Lower Reach ◽  
Vegetation Growth ◽  
Ejina Oasis

Abstract. Liquid and solid precipitation is abundant in the high elevation, upper reach of the Heihe basin. The development of modern irrigation schemes in the middle reach of the basin is taking up an increasing share of fresh water resources, endangering the oasis and traditional irrigation systems in the lower reach. In this study, the response of vegetation in the Ejina Oasis in the lower reach of the Heihe River to the water yield of the upper catchment was analyzed by time series analysis of monthly observations of precipitation in the upper and lower catchment, river streamflow downstream of the modern irrigation schemes and satellite observations of vegetation index. Firstly, remote sensing data were used to monitor the vegetation dynamic for a long time period. Due to cloud-contamination, atmospheric influence and different solar angles, however, the quality and consistence of time series of remote sensing data is degraded. In this research we used a Fourier Transform method – the Harmonic Analysis of Time Series (HANTS) algorithm – to reconstruct cloud-free NDVI time series data from the Terra-MODIS dataset. Anomalies in precipitation, streamflow, and vegetation index are detected by comparing each year with the average year. The relationship between the anomalies in vegetation growth, the local precipitation and upstream water yield were analyzed. The same approach is used to identify, remove and gap-filling cloud contaminated observations in the satellite data for each year in the dataset. The results showed that: the previous year total runoff had a significant relationship with the vegetation growth in Ejina Oasis and that anomalies in monthly runoff of the Heihe River influenced the phenology of vegetation in the entire oasis during drier years. The time of maximum green-up was uniform throughout the oasis during wetter years, but showed a clear S–N gradient (downstream) during drier years.

scholarly journals Phenological Monitoring of Paddy Crop Using Time Series MODIS Data

Proceedings ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 19
Author(s):  
C. Dineshkumar ◽  
S. Nitheshnirmal ◽  
Ashutosh Bhardwaj ◽  
K. Nivedita Priyadarshini
Keyword(s):  
Time Series ◽  
Tamil Nadu ◽  
Vegetation Index ◽  
Google Earth ◽  
Paddy Fields ◽  
Series Data ◽  
Local Minima ◽  
Phenological Stages ◽  
Local Maxima ◽  
Modis Data

Rice is an important staple food crop worldwide, especially in India. Accurate and timely prediction of rice phenology plays a significant role in the management of water resources, administrative planning, and food security. In addition to conventional methods, remotely sensed time series data can provide the necessary estimation of rice phenological stages over a large region. Thus, the present study utilizes the 16-day composite Enhanced Vegetation Index (EVI) product with a spatial resolution of 250 m from the Moderate Resolution Imaging Spectroradiometer (MODIS) to monitor the rice phenological stages over Karur district of Tamil Nadu, India, using the Google Earth Engine (GEE) platform. The rice fields in the study area were classified using the machine learning algorithm in GEE. The ground truth was obtained from the paddy fields during crop production which was used for classifying the paddy grown area. After the classification of paddy fields, local maxima, and local minima present in each pixel of time series, the EVI product was used to determine the paddy growing stages in the study area. The results show that in the initial stage the pixel value of EVI in the paddy field shows local minima (0.23), whereas local maxima (0.41) were obtained during the peak vegetative stage. The results derived from the present study using MODIS data were cross-validated using the field data.

scholarly journals Mapping Maize Cultivated Area Combining MODIS EVI Time Series and the Spatial Variations of Phenology over Huanghuaihai Plain

2020 ◽  
Vol 10 (8) ◽  
pp. 2667 ◽  
Author(s):  
Xueting Wang ◽  
Sha Zhang ◽  
Lili Feng ◽  
Jiahua Zhang ◽  
Fan Deng
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Regression Equations ◽  
Summer Maize ◽  
Temporal Shift ◽  
Modis Evi ◽  
Multi Temporal ◽  
Moderate Resolution Imaging Spectroradiometer

Crop phenology is a significant factor that affects the precision of crop area extraction by using the multi-temporal vegetation indices (VIs) approach. Considering the phenological differences of maize among the different regions, the summer maize cultivated area was estimated by using enhanced vegetation index (EVI) time series images from the Moderate Resolution Imaging Spectroradiometer (MODIS) over the Huanghuaihai Plain in China. By analyzing the temporal shift in summer maize calendars, linear regression equations for simulating the summer maize phenology were obtained. The simulated maize phenology was used to correct the MODIS EVI time series curve of summer maize. Combining the mean absolute distance (MAD) and p-tile algorithm, the cultivated areas of summer maize were distinguished over the Hunaghuaihai Plain. The accuracy of the extraction results in each province was above 85%. Comparing the maize area of two groups from MODIS-estimated and statistical data, the validation results showed that the R2 reached 0.81 at the city level and 0.69 at the county level. It demonstrated that the approach in this study has the ability to effectively map the summer maize area over a large scale and provides a novel idea for estimating the planting area of other crops.

scholarly journals Greening and Browning Trends across Peru’s Diverse Environments

2020 ◽  
Vol 12 (15) ◽  
pp. 2418 ◽  
Author(s):  
Molly H. Polk ◽  
Niti B. Mishra ◽  
Kenneth R. Young ◽  
Kumar Mainali
Keyword(s):  
Time Series ◽  
Protected Area ◽  
Landscape Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Elevation Gradient ◽  
Positive Trend ◽  
Scientific Legacy ◽  
Alexander Von Humboldt ◽  
Moderate Resolution Imaging Spectroradiometer

If he were living today, Alexander von Humboldt would be using current technology to evaluate change in the Andes. Inspired by von Humboldt’s scientific legacy and the 2019 celebrations of his influence, we utilize a Moderate Resolution Imaging Spectroradiometer (MODIS)time-series vegetation index to ask questions of landscape change. Specifically, we use an 18-year record of Normalized Difference Vegetation Index (NDVI) data as a proxy to evaluate landscape change in Peru, which is well known for its high biological and ecological diversity. Continent-level evaluations of Latin America have shown sites with a positive trend in NDVI, or “greening” and “browning”, a negative trend in NDVI that suggests biophysical or human-caused reductions in vegetation. Our overall hypothesis was that the major biomes in Peru would show similar NDVI change patterns. To test our expectations, we analyzed the NDVI time-series with Thiel-Sen regression and evaluated Peru overall, by protected area status, by biome, and by biome and elevation. Across Peru overall, there was a general greening trend. By protected area status, surprisingly, the majority of greening occurred outside protected areas. The trends were different by biome, but there were hotspots of greening in the Amazon, Andean Highlands, and Drylands where greening dominated. In the Tropical Subtropical Dry Broadleaf Forest biome, greening and browning signals were mixed. Greening trends varied across the elevation gradient, switching from greening, to browning, and then back to greening as elevation increased. By biome and elevation, the results were variable. We further explored biome-specific drivers of greening and browning drawing on high-resolution imagery, the literature, and field expertise, much as we imagine von Humboldt might have approached similar questions of landscape dynamism.

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