Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite

This paper reports the relationship between the spatial variation in mean wheat yield/ha of 50 Local Government Areas in Western Australia and satellite measures of the Normalized Difference Vegetation Index (NDVI). Yield/ha was based on estimates of the area harvested and actual grain received by the Cooperative Bulk Handling Ltd. The study area covered 16.3 million ha, in which 2.9 million ha of wheat were sown and 4.66 million tonnes of grain harvested. This was 78% of the total Western Australian wheat crop. Spatial variations in NDVI in early July, at around stem elongation, accounted for 46% of the spatial variation in final yield. This increased to 56% of yield variance around the onset of anthesis at the end of August. It remained high until early November (48%) when crops were senescing or senescent. A combination of NDVI from late August and early November accounted for 70% of the yield variance. In comparison, total rainfall during the 1992 growing season from April to October, the main determinant of yield variations, accounted for 28% of the yield variation. The significant correlation of NDVI with final yield by the middle of the growing season 3 to 5 months before harvest indicates the feasibility of making useful yield forecasts from this time onwards. In addition, the NDVI could provide useful spatial information on the significance of the yield/canopy development/water use relationship which underlies this correlation.

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Wheat Yield Estimation with NDVI Values Using a Proximal Sensing Tool

Remote Sensing ◽

10.3390/rs12172749 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2749

Author(s):

Marta Aranguren ◽

Ander Castellón ◽

Ana Aizpurua

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Crop Yields ◽

Wheat Yield ◽

Growing Season ◽

N Fertilization ◽

Wheat Crop ◽

Growth Stages ◽

Climate Conditions ◽

N Status

Nitrogen (N) splitting is critical to achieving high crop yields without having negative effects on the environment. Monitoring crop N status throughout the wheat growing season is key to finding the balance between crop N requirements and fertilizer needs. Three soft winter wheat fertilization trials under rainfed conditions in Mediterranean climate conditions were monitored with a RapidScan CS-45 (Holland Scientific, Lincoln, NE, USA) instrument to determine the normalized difference vegetation index (NDVI) values at the GS30, GS32, GS37, and GS65 growth stages. The threshold NDVI values in the Cezanne variety were 0.7–0.75 at the GS32, GS37, and GS65 growing stages. However, for the GS30 growing stage, a threshold value could not be established precisely. At this stage, N deficiency may not affect wheat yield, as long as the N status increases at GS32 stage and it is maintained thereafter. Following the NDVI dynamic throughout the growing season could help to predict the yields at harvest time. Therefore, the ΣNDVI from GS30 to GS65 explains about 80% of wheat yield variability. Therefore, a given yield could be achieved with different dynamics in wheat NDVI values throughout the growing cycle. The determined ranges of the NDVI values might be used for developing new fertilization strategies that are able to adjust N fertilization to wheat crop needs.

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Correlation of the vegetation index (NDVI) and phytometric parameters at different stages of field crops development

PROCEEDINGS OF V INTERNATIONAL SCIENTIFIC CONFERENCE “CURRENT STATE, PROBLEMS AND PROSPECTS OF THE DEVELOPMENT OF AGRARIAN SCIENCE” ◽

10.33952/2542-0720-2020-5-9-10-123 ◽

2020 ◽

Author(s):

Yu.A. Gulyanov ◽

Keyword(s):

Spring Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Stem Elongation ◽

Grain Filling ◽

Growing Season ◽

Practical Application ◽

Field Crops ◽

The Relationship

The main goal of our research was to identify the relationship between the normalized difference vegetation index (NDVI) and the area of assimilation surface (AS) of spring wheat crops during the growing season, as well as to develop practical application of the findings. Throughout the growing season, the area of assimilation surface of T. aestivum increases much faster than the vegetation index NDVI. The smallest AS (282.7 m2/ha), which corresponded to 0.01 units of the NDVI (calculated factor) was observed during the tillering stage. It reaches its maximum values – 331.7–406.1–383.7 m2/ha (1.20–1.47–1.39 times higher) from stem elongation to the end of flowering. During the grain filling and maturation, these values decrease to 336.2 m2/ha but still are 1.19 times higher than the initial ones.

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Monitoring agricultural drought using geospatial techniques: a case study of Thal region of Punjab, Pakistan

Journal of Water and Climate Change ◽

10.2166/wcc.2020.232 ◽

2020 ◽

Vol 11 (S1) ◽

pp. 203-216 ◽

Cited By ~ 1

Author(s):

Muhammad Amin ◽

Mobushir Riaz Khan ◽

Sher Shah Hassan ◽

Aftab Ahmad Khan ◽

Muhammad Imran ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Wheat Yield ◽

Standardized Precipitation Index ◽

Weather Conditions ◽

Agricultural Drought ◽

Wheat Crop ◽

Drought Risk ◽

Risk Map ◽

Agricultural Risk

Abstract The Thal region of Punjab often experiences dry weather conditions with extreme variability in rainfall on a spatiotemporal scale during Rabi cropping season. The current study assesses the impacts of agricultural drought on wheat crops for 2000–2015. MOD13Q1 and CHIRPS data were used for identifying and assessing variation in agricultural drought patterns and severity. Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Stress Vegetation Index (STVI) and wheat crop yield anomalies were computed to characterize the gravity of drought across the Thal region. The results indicate that the wheat Rabi cropping seasons of the years 2000–2002 experienced extreme agricultural drought, with a spatial difference in severity level causing low and poor yield, while the years 2011 and 2014 were almost normal among all the years, leaving varied impacts on wheat yield. The combined agricultural risk map was generated by integrating the agricultural and meteorological droughts severity maps. The combined risk map generated using weighted overlay analysis of all the parameters indicate that the total Thal area can be classified into slight, moderate and no drought covering 28.12, 12.76, and 59.12% respectively of the total area. Hence an agricultural risk map would be extremely helpful as a tool to guide the decision-making process for monitoring drought risk on agricultural productivity.

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Physiological and genetic drivers underpinning canopy development are associated with durum wheat yield in rainfed environments

10.1101/2021.08.21.457180 ◽

2021 ◽

Author(s):

Yichen Kang ◽

Shanice Van Haeften ◽

Daniela Bustos-Korts ◽

Stjepan Vukasovic ◽

Sana Ullah Khan ◽

...

Keyword(s):

Association Mapping ◽

Durum Wheat ◽

Water Use ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Wheat Yield ◽

Yield Component ◽

Canopy Closure ◽

Canopy Development ◽

Selection For

Durum wheat (Triticum turgidum L. ssp. Durum) is largely grown in rainfed production systems around the world. New cultivars with improved adaptation to water-limited environments are required to sustain productivity in the face of climate change. Physiological traits related to canopy development underpin the production of biomass and yield, as they interact with solar radiation and affect the timing of water use throughout the growing season. Despite their importance, there is limited research on the relationship between canopy development and yield in durum wheat, in particular studies exploring temporal canopy dynamics under field conditions. This study reports the genetic dissection of canopy development in a durum wheat nested-association mapping population evaluated for longitudinal normalized difference vegetation index (NDVI) measurements. Association mapping was performed to identify quantitative trait loci (QTL) for time-point NDVI and spline-smoothed NDVI trajectory traits. Yield effects associated with QTL for canopy development were explored using data from four rainfed field trials. Four QTL were associated with yield in specific environments, and notably, were not associated with a yield penalty in any environment. Alleles associated with slow canopy closure increased yield. This was likely due to a combined effect of optimised timing of water-use and pleiotropic effects on yield component traits, including spike number and spike length. Overall, this study suggests that slower canopy closure is beneficial for durum wheat production in rainfed environments. Selection for traits or loci associated with canopy development may indirectly improve yield and support selection for more resilient and productive cultivars in water limited environments.

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Normalized Difference Vegetation Index as a Tool for Wheat Yield Estimation: A Case Study from Faisalabad, Pakistan

The Scientific World JOURNAL ◽

10.1155/2014/725326 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 17

Author(s):

Syeda Refat Sultana ◽

Amjed Ali ◽

Ashfaq Ahmad ◽

Muhammad Mubeen ◽

M. Zia-Ul-Haq ◽

...

Keyword(s):

Grain Yield ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Field Experiments ◽

Wheat Yield ◽

Grain Filling ◽

Research Area ◽

Wheat Crop ◽

Growth Stages ◽

Wheat Cultivars

For estimation of grain yield in wheat, Normalized Difference Vegetation Index (NDVI) is considered as a potential screening tool. Field experiments were conducted to scrutinize the response of NDVI to yield behavior of different wheat cultivars and nitrogen fertilization at agronomic research area, University of Agriculture Faisalabad (UAF) during the two years 2008-09 and 2009-10. For recording the value of NDVI, Green seeker (Handheld-505) was used. Split plot design was used as experimental model in, keeping four nitrogen rates (N1= 0 kg ha−1,N2= 55 kg ha−1,N3=110 kg ha−1, andN4= 220 kg ha−1) in main plots and ten wheat cultivars (Bakkhar-2001, Chakwal-50, Chakwal-97, Faisalabad-2008, GA-2002, Inqlab-91, Lasani-2008, Miraj-2008, Sahar-2006, and Shafaq-2006) in subplots with four replications. Impact of nitrogen and difference between cultivars were forecasted through NDVI. The results suggested that nitrogen treatment N4(220 kg ha−1) and cultivar Faisalabad-2008 gave maximum NDVI value (0.85) at grain filling stage among all treatments. The correlation among NDVI at booting, grain filling, and maturity stages with grain yield was positive (R2 = 0.90;R2 = 0.90;R2 = 0.95), respectively. So, booting, grain filling, and maturity can be good depictive stages during mid and later growth stages of wheat crop under agroclimatic conditions of Faisalabad and under similar other wheat growing environments in the country.

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Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽

10.3390/agronomy11081486 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1486

Author(s):

Chris Cavalaris ◽

Sofia Megoudi ◽

Maria Maxouri ◽

Konstantinos Anatolitis ◽

Marios Sifakis ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Model Development ◽

Wheat Yield ◽

Growth Period ◽

Yield Data ◽

Water Index ◽

Normalized Difference Water Index ◽

Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

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An LSWI-Based Method for Mapping Irrigated Areas in China Using Moderate-Resolution Satellite Data

Remote Sensing ◽

10.3390/rs12244181 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4181

Author(s):

Kunlun Xiang ◽

Wenping Yuan ◽

Liwen Wang ◽

Yujiao Deng

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Statistical Data ◽

Spatial Information ◽

Hydrological Cycle ◽

Mainland China ◽

Mean Annual Precipitation ◽

Irrigated Area ◽

Moderate Resolution

Accurate spatial information about irrigation is crucial to a variety of applications, such as water resources management, water exchange between the land surface and atmosphere, climate change, hydrological cycle, food security, and agricultural planning. Our study proposes a new method for extracting cropland irrigation information using statistical data, mean annual precipitation and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type data and surface reflectance data. The approach is based on comparing the land surface water index (LSWI) of cropland pixels to that of adjacent forest pixels with similar normalized difference vegetation index (NDVI). In our study, we validated the approach over mainland China with 612 reference samples (231 irrigated and 381 non-irrigated) and found the accuracy of 62.09%. Validation with statistical data also showed that our method explained 86.67 and 58.87% of the spatial variation in irrigated area at the provincial and prefecture levels, respectively. We further compared our new map to existing datasets of FAO/UF, IWMI, Zhu and statistical data, and found a good agreement with the irrigated area distribution from Zhu’s dataset. Results show that our method is an effective method apply to mapping irrigated regions and monitoring their yearly changes. Because the method does not depend on training samples, it can be easily repeated to other regions.

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Analysis of Drought Characteristics in Xilingol Grassland of Northern China Based on SPEI and Its Impact on Vegetation

Mathematical Problems in Engineering ◽

10.1155/2017/5209173 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Siqin Tong ◽

Yuhai Bao ◽

Rigele Te ◽

Qiyun Ma ◽

Si Ha ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Pearson Correlation ◽

Growing Season ◽

Vegetation Coverage ◽

Semiarid Region ◽

Linear Regression Method ◽

Upward Trend ◽

Drought Effect ◽

Positive Correlation

This research is based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (NDVI) which represent the drought and vegetation condition on land. Take the linear regression method and Pearson correlation analysis to study the spatial and temporal evolution of SPEI and NDVI and the drought effect on vegetation. The results show that (1) during 1961–2015, SPEI values at different time scales showed a downward trend; SPEI-12 has a mutation in 1997 and the SPEI value significantly decreased after this year. (2) During 2000–2015, the annual growing season SPEI has an obvious upward trend in time and the apparent wetting spatially. (3) In the recent 16 years, the growing season NDVI showed an upward trend and more than 80% of the total area’s vegetation increased in Xilingol. (4) Vegetation coverage in Xilingol grew better in humid years and opposite in arid years. SPEI and NDVI had a significant positive correlation; 98% of the region showed positive correlation, indicating that meteorological drought affects vegetation growth more in arid and semiarid region. (5) The effect of drought on vegetation has lag effect, and the responses of different grassland types to different scales of drought were different.

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Phenology-Based Residual Trend Analysis of MODIS-NDVI Time Series for Assessing Human-Induced Land Degradation

Sensors ◽

10.3390/s18113676 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3676 ◽

Cited By ~ 5

Author(s):

Hao Chen ◽

Xiangnan Liu ◽

Chao Ding ◽

Fang Huang

Keyword(s):

Trend Analysis ◽

Land Degradation ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Growing Season ◽

Practical Method ◽

Large Area ◽

Modis Ndvi ◽

Daily Precipitation Data ◽

Improved Model

Land degradation is a widespread environmental issue and an important factor in limiting sustainability. In this study, we aimed to improve the accuracy of monitoring human-induced land degradation by using phenological signal detection and residual trend analysis (RESTREND). We proposed an improved model for assessing land degradation named phenology-based RESTREND (P-RESTREND). This method quantifies the influence of precipitation on normalized difference vegetation index (NDVI) variation by using the bivariate linear regression between NDVI and precipitation in pre-growing season and growing season. The performances of RESTREND and P-RESTREND for discriminating land degradation caused by climate and human activities were compared based on vegetation-precipitation relationship. The test area is in Western Songnen Plain, Northeast China. It is a typical region with a large area of degraded drylands. The MODIS 8-day composite reflectance product and daily precipitation data during 2000–2015 were used. Our results showed that P-RESTREND was more effective in distinguishing different drivers of land degradation than the RESTREND. Degraded areas in the Songnen grasslands can be effectively detected by P-RESTREND. Therefore, this modified model can be regarded as a practical method for assessing human-induced land degradation.

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Impacts of temperature extremes on European vegetation during the growing season

10.5194/bg-2017-189 ◽

2017 ◽

Author(s):

Lukas Baumbach ◽

Jonatan F. Siegmund ◽

Magdalena Mittermeier ◽

Reik V. Donner

Keyword(s):

Land Cover ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Extreme Temperature ◽

Terrestrial Ecosystems ◽

Growing Season ◽

Temperature Extremes ◽

Minor Effect ◽

European Continent

Abstract. Temperature is a key factor controlling plant growth and vitality in the temperate climates of the mid-latitudes like in vast parts of the European continent. Beyond the effect of average conditions, the timings and magnitudes of temperature extremes play a particularly crucial role, which needs to be better understood in the context of projected future rises in the frequency and/or intensity of such events. In this work, we employ event coincidence analysis (ECA) to quantify the likelihood of simultaneous occurrences of extremes in daytime land surface temperature anomalies and the normalized difference vegetation index (NDVI). We perform this analysis for entire Europe based upon remote sensing data, differentiating between three periods corresponding to different stages of plant development during the growing season. In addition, we analyze the typical elevation and land cover type of the regions showing significantly large event coincidences rates to identify the most severely affected vegetation types. Our results reveal distinct spatio-temporal impact patterns in terms of extraordinarily large co-occurrence rates between several combinations of temperature and NDVI extremes. Croplands are among the most frequently affected land cover types, while elevation is found to have only a minor effect on the spatial distribution of corresponding extreme weather impacts. These findings provide important insights into the vulnerability of European terrestrial ecosystems to extreme temperature events and demonstrate how event-based statistics like ECA can provide a valuable perspective on environmental nexuses.

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