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

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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Drought Risk Assessment in the Khushab Region of Pakistan Using Satellite Remote Sensing and Geospatial Methods

International Journal of Economic and Environmental Geology ◽

10.46660/ijeeg.vol10.iss1.2019.217 ◽

2019 ◽

Vol 10 (1) ◽

pp. 48-56 ◽

Cited By ~ 4

Author(s):

Muhammad Khubaib Abuzar ◽

Muhammad Shafiq ◽

Syed Amer Mahmood ◽

Muhammad Irfan ◽

Tayyaba Khalil ◽

...

Keyword(s):

Remote Sensing ◽

Social Life ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Standardized Precipitation Index ◽

Temporal Analysis ◽

Agricultural Drought ◽

Natural Phenomenon ◽

Drought Risk ◽

Gis And Remote Sensing

Drought is a harmful and slow natural phenomenon that has significant effects on the economy, social life,agriculture and environment of the country. Due to its slow process it is difficult to study this phenomenon. RemoteSensing and GIS tools play a key role in studying different hazards like droughts. The main objective of the study wasto investigate drought risk by using GIS and Remote Sensing techniques in district Khushab, Pakistan. Landsat ETMimages for the year 2003, 2009 and 2015 were utilized for spatial and temporal analysis of agricultural andmeteorological drought. Normalized difference vegetation index (NDVI) Standardized Precipitation Index (SPI) andrainfall anomaly indices were calculated to identify the drought prone areas in the study area. To monitormeteorological drought SPI values were used and NDVI was calculated for agricultural drought. These indices wereintegrated to compute the spatial and temporal drought maps. Three zones; no drought, slight drought and moderatedrought were identified. Final drought map shows that 30.21% of the area faces moderate drought, 28.36% faces slightdrought while nearly 41.3% faces no drought situation. Drought prevalence and severity is present more in the southernpart of Khushab district than the northern part. Most of the northern part is not under any type of drought. Thus, anoverall outcome of this study shows that risk areas can be assessed appropriately by integration of various data sourcesand thereby management plans can be prepared to deal with the hazard.

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Drought Risk Assessment in the Khushab Region of Pakistan Using Satellite Remote Sensing and Geospatial Methods

International Journal of Economic and Environmental Geology ◽

10.46660/ojs.v10i1.217 ◽

2019 ◽

Vol 10 (1) ◽

pp. 48-56

Author(s):

Muhammad Khubaib Abuzar ◽

Muhammad Shafiq ◽

Syed Amer Mahmood ◽

Muhammad Irfan ◽

Tayyaba Khalil ◽

...

Keyword(s):

Remote Sensing ◽

Social Life ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Standardized Precipitation Index ◽

Temporal Analysis ◽

Agricultural Drought ◽

Natural Phenomenon ◽

Drought Risk ◽

Gis And Remote Sensing

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

Australian Journal of Agricultural Research ◽

10.1071/ar9950113 ◽

1995 ◽

Vol 46 (1) ◽

pp. 113 ◽

Cited By ~ 72

Author(s):

RCG Smith ◽

J Adams ◽

DJ Stephens ◽

PT Hick

Keyword(s):

Spatial Variation ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spatial Information ◽

Stem Elongation ◽

Wheat Yield ◽

Growing Season ◽

Wheat Crop ◽

Canopy Development ◽

Final Yield

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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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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The Use of Satellite Information (MODIS/Aqua) for Phenological and Classification Analysis of Plant Communities

Forests ◽

10.3390/f10070561 ◽

2019 ◽

Vol 10 (7) ◽

pp. 561 ◽

Cited By ~ 3

Author(s):

Yulia Ivanova ◽

Anton Kovalev ◽

Oleg Yakubailik ◽

Vlad Soukhovolsky

Keyword(s):

Time Series ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Plant Communities ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Weather Conditions ◽

Linear Discriminant ◽

Canonical Correlations

Vegetation indices derived from remote sensing measurements are commonly used to describe and monitor vegetation. However, the same plant community can have a different NDVI (normalized difference vegetation index) depending on weather conditions, and this complicates classification of plant communities. The present study develops methods of classifying the types of plant communities based on long-term NDVI data (MODIS/Aqua). The number of variables is reduced by introducing two integrated parameters of the NDVI seasonal series, facilitating classification of the meadow, steppe, and forest plant communities in Siberia using linear discriminant analysis. The quality of classification conducted by using the markers characterizing NDVI dynamics during 2003–2017 varies between 94% (forest and steppe) and 68% (meadow and forest). In addition to determining phenological markers, canonical correlations have been calculated between the time series of the proposed markers and the time series of monthly average air temperatures. Based on this, each pixel with a definite plant composition can be characterized by only four values of canonical correlation coefficients over the entire period analyzed. By using canonical correlations between NDVI and weather parameters and employing linear discriminant analysis, one can obtain a highly accurate classification of the study plant communities.

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Are Wheat Hybrids More Affected by Weed Competition than Conventional Cultivars?

Agronomy ◽

10.3390/agronomy10040526 ◽

2020 ◽

Vol 10 (4) ◽

pp. 526

Author(s):

Marco Milan ◽

Silvia Fogliatto ◽

Massimo Blandino ◽

Francesco Vidotto

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Weather Conditions ◽

Hybrid Wheat ◽

Wheat Varieties ◽

Canopy Development ◽

Productivity Losses ◽

Crop Density ◽

Weed Infestation ◽

Wheat Hybrids

Seeding rates of hybrid wheat varieties are typically much lower than conventional varieties due to their higher seed costs, which could potentially delay canopy development leading to greater weed pressures. To test whether hybrid wheat crops are more affected by weed pressure than conventional cultivars, a conventional variety (“Illico”) and a hybrid (“Hystar”), were compared in a three-year (2012–2016) field study at two sites in Northern Italy. Weed infestation was mainly characterized by weeds with an early growth pattern, and in only a few seasons did the hybrid crops show a higher weed density than the conventional cultivar. Despite the lower sowing rate, hybrids were able to achieve a similar crop density to the conventional cultivar even in years of delayed sowing or dry weather conditions. Normalized Difference Vegetation Index values were generally similar between cultivars across the years, regardless of the presence of weeds, except during the springtime. Occasionally, the test weight was significantly higher in weeded plots than un-weeded plots. Overall, the two cultivars showed similar yields within the same year. These results indicate that on fields with a low weed burden, and where these weeds emerge early, cultivars may not be significantly affected by productivity losses.

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Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping

Remote Sensing ◽

10.3390/rs11192228 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2228 ◽

Cited By ~ 17

Author(s):

Ali Nasrallah ◽

Nicolas Baghdadi ◽

Mohammad El Hajj ◽

Talal Darwish ◽

Hatem Belhouchette ◽

...

Keyword(s):

Winter Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Incidence Angle ◽

Weather Conditions ◽

Crop Monitoring ◽

Temporal Series ◽

Automated Method ◽

Low Incidence ◽

Phenological Phases

The ability of Synthetic Aperture Radar (SAR) Sentinel-1 data to detect the main wheat phenological phases was investigated in the Bekaa plain of Lebanon. Accordingly, the temporal variation of Sentinel-1 (S1) signal was analyzed as a function of the phenological phases’ dates observed in situ (germination; heading and soft dough), and harvesting. Results showed that S1 data, unlike the Normalized Difference Vegetation Index (NDVI) data, were able to estimate the dates of theses phenological phases due to significant variations in S1 temporal series at the dates of germination, heading, soft dough, and harvesting. Particularly, the ratio VV/VH at low incidence angle (32–34°) was able to detect the germination and harvesting dates. VV polarization at low incidence angle (32–34°) was able to detect the heading phase, while VH polarization at high incidence angle (43–45°) was better than that at low incidence angle (32–34°), in detecting the soft dough phase. An automated approach for main wheat phenological phases’ determination was then developed on the western part of the Bekaa plain. This approach modelled the S1 SAR temporal series by smoothing and fitting the temporal series with Gaussian functions (up to three Gaussians) allowing thus to automatically detect the main wheat phenological phases from the sum of these Gaussians. To test its robustness, the automated method was applied on the northern part of the Bekaa plain, in which winter wheat is harvested usually earlier because of the different weather conditions. The Root Mean Square Error (RMSE) of the estimation of the phenological phases’ dates was 2.9 days for germination, 5.5 days for heading, 5.1 days soft dough, 3.0 days for West Bekaa’s harvesting, and 4.5 days for North Bekaa’s harvesting. In addition, a slight underestimation was observed for germination and heading of West Bekaa (−0.2 and −1.1 days, respectively) while an overestimation was observed for soft dough of West Bekaa and harvesting for both West and North Bekaa (3.1, 0.6, and 3.6 days, respectively). These results are encouraging, and thus prove that S1 data are powerful as a tool for crop monitoring, to serve enhanced crop management and production handling.

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Analysis of meteorological variations on wheat yield and its estimation using remotely sensed data. A case study of selected districts of Punjab Province, Pakistan (2001-14)

Italian Journal of Agronomy ◽

10.4081/ija.2017.897 ◽

2017 ◽

Vol 12 (3) ◽

Cited By ~ 3

Author(s):

Rafia Mumtaz ◽

Shahbaz Baig ◽

Iram Fatima

Keyword(s):

Climate Change ◽

Land Use ◽

Crop Yield ◽

Vegetation Index ◽

Wheat Yield ◽

Least Square ◽

Wheat Crop ◽

Main Challenge ◽

Normalised Difference Vegetation Index ◽

The Impact

Land management for crop production is an essential human activity that supports life on Earth. The main challenge to be faced by the agriculture sector in coming years is to feed the rapidly growing population while maintaining the key resources such as soil fertility, efficient land use, and water. Climate change is also a critical factor that impacts agricultural production. Among others, a major effect of climate change is the potential alterations in the growth cycle of crops which would likely lead to a decline in the agricultural output. Due to the increasing demand for proper agricultural management, this study explores the effects of meteorological variation on wheat yield in Chakwal and Faisalabad districts of Punjab, Pakistan and used normalised difference vegetation index (NDVI) as a predictor for yield estimates. For NDVI data (2001-14), the NDVI product of Moderate Resolution Imaging spectrometer (MODIS) 16-day composites data has been used. The crop area mapping has been realised by classifying the satellite data into different land use/land covers using iterative self-organising (ISO) data clustering. The land cover for the wheat crop was mapped using a crop calendar. The relation of crop yield with NDVI and the impact of meteorological parameters on wheat growth and its yield has been analysed at various development stages. A strong correlation of rainfall and temperature was found with NDVI data, which determined NDVI as a strong predictor of yield estimation. The wheat yield estimates were obtained by linearly regressing the reported crop yield against the time series of MODIS NDVI profiles. The wheat NDVI profiles have shown a parabolic pattern across the growing season, therefore parabolic least square fit (LSF) has been applied prior to linear regression. The coefficients of determination (<em>R</em><sup>2</sup>) between the reported and estimated yield was found to be 0.88 and 0.73, respectively, for Chakwal and Faisalabad. This indicates that the method is capable of providing yield estimates with competitive accuracies prior to crop harvest, which can significantly aid the policy guidance and contributes to better and timely decisions.

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