Estimation of daily average downward shortwave radiation from MODIS data using principal components regression method: Fars province case study

Barzin, Razieh; Shirvani, Amin; Lotfi, Hossein

doi:10.1515/intag-2016-0035

Cited by 6 publications

(4 citation statements)

References 17 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, several VIs were derived from a 5-band multispectral sensor. Multispectral bands are visually and numerically similar; on the other hand, they are often highly correlated [23,24]. To avoid the issue associated with VI calculation, row data was converted to percentage reflectance to signify the quantitative data.…”

Section: Vegetation Indicesmentioning

confidence: 99%

Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn

et al. 2020

Self Cite

View full text Add to dashboard Cite

Changes in spatial and temporal variability in yield estimation are detectable through plant biophysical characteristics observed at different phenological development stages of corn. A multispectral red-edge sensor mounted on an Unmanned Aerial Systems (UAS) can provide spatial and temporal information with high resolution. Spectral analysis of UAS acquired spatiotemporal images can be used to develop a statistical model to predict yield based on different phenological stages. Identifying critical vegetation indices (VIs) and significant spectral information could lead to increased yield prediction accuracy. The objective of this study was to develop a yield prediction model at specific phenological stages using spectral data obtained from a corn field. The available spectral bands (red, blue, green, near infrared (NIR), and red-edge) were used to analyze 26 different VIs. The spectral information was collected from a cornfield at Mississippi State University using a MicaSense multispectral red-edge sensor, mounted on a UAS. In this research, a new empirical method used to reduce the effects of bare soil pixels in acquired images was introduced. The experimental design was a randomized complete block that consisted of 16 blocks with 12 rows of corn planted in each block. Four treatments of nitrogen (N) including 0, 90, 180, and 270 kg/ha were applied randomly. Random forest was utilized as a feature selection method to choose the best combination of variables for different stages. Multiple linear regression and gradient boosting decision trees were used to develop yield prediction models for each specific phenological stage by utilizing the most effective variables at each stage. At the V3 (3 leaves with visible leaf collar) and V4-5 (4-5 leaves with visible leaf collar) stages, the Optimized Soil Adjusted Vegetation Index (OSAVI) and Simplified Canopy Chlorophyll Content Index (SCCCI) were the single dominant variables in the yield predicting models, respectively. A combination of the Green Atmospherically Resistant Index (GARI), Normalized Difference Red-Edge (NDRE), and green Normalized Difference Vegetation Index (GNDVI) at V6-7, SCCCI, and Soil-Adjusted Vegetation Index (SAVI) at V10,11, and SCCCI, Green Leaf Index (GLI), and Visible Atmospherically Resistant Index (VARIgreen) at tasseling stage (VT) were the best indices for predicting grain yield of corn. The prediction models at V10 and VT had the greatest accuracy with a coefficient of determination of 0.90 and 0.93, respectively. Moreover, the SCCCI as a combined index seemed to be the most proper index for predicting yield at most of the phenological stages. As corn development progressed, the models predicted final grain yield more accurately.

show abstract

Section: Vegetation Indicesmentioning

confidence: 99%

Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Changes in climate were calculated from results of a regional climate model (RegCM) nested within a General Circulation Model (GCM) from National Center for Atmospheric Research (NCAR) at Boulder, Colorado. The quantified changes in the future climate (i.e., maximum/minimum temperatures, solar radiation, precipitation, wind speed) were predicted using the RegCM climate model [7]. The weather input required to run GOSSYM is on a daily basis.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Dependent Variables on Utilizing Cotton Simulation Model

Beigi¹

2021

IJSEA

View full text Add to dashboard Cite

This research is focused on utilizing cotton simulation model (GOSSYM) to investigate the impact of different dependent variables such as different amount of Carbone Dioxide, rainfall, different Nitrogen Level, UV-B with or without irrigation, or different soil type in a MID-season cultivar for predicting and finding the influence of environmental factors on cotton yield, plant height, main stem nodes, and major phenological events such as squaring, flowering and boll opening during 30 years from 1964 to 1993 in Razan, Hamedan, Iran.

show abstract

“…The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites provides detailed and consistent atmospheric, terrestrial, and oceanic products, and studies have been developed for SSI mapping from pairs of MODIS products [2,32]. Barzin et al (2017) proposed a combination of principal components analysis (PCA) and regression models for estimating daily average downward solar radiation using MODIS data for ten synoptic stations in Fars Province, Iran, with a root mean square error (RMSE) of 0.9-2.04 MJ/(m 2 •d) [33]. The largest uncertainties resulting from SSI retrieval arise from inadequate information on cloud properties.…”

Section: Introductionmentioning

confidence: 99%

A Lookup-Table-Based Approach to Estimating Surface Solar Irradiance from Geostationary and Polar-Orbiting Satellite Data

Zhang

Huang

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Incoming surface solar irradiance (SSI) is essential for calculating Earth's surface radiation budget and is a key parameter for terrestrial ecological modeling and climate change research. Remote sensing images from geostationary and polar-orbiting satellites provide an opportunity for SSI estimation through directly retrieving atmospheric and land-surface parameters. This paper presents a new scheme for estimating SSI from the visible and infrared channels of geostationary meteorological and polar-orbiting satellite data. Aerosol optical thickness and cloud microphysical parameters were retrieved from Geostationary Operational Environmental Satellite (GOES) system images by interpolating lookup tables of clear and cloudy skies, respectively. SSI was estimated using pre-calculated offline lookup tables with different atmospheric input data of clear and cloudy skies. The lookup tables were created via the comprehensive radiative transfer model, Santa Barbara Discrete Ordinate Radiative Transfer (SBDART), to balance computational efficiency and accuracy. The atmospheric attenuation effects considered in our approach were water vapor absorption and aerosol extinction for clear skies, while cloud parameters were the only atmospheric input for cloudy-sky SSI estimation. The approach was validated using one-year pyranometer measurements from seven stations in the SURFRAD (SURFace RADiation budget network). The results of the comparison for 2012 showed that the estimated SSI agreed with ground measurements with correlation coefficients of 0.94, 0.69, and 0.89 with a bias of 26.4 W/m 2 , −5.9 W/m 2 , and 14.9 W/m 2 for clear-sky, cloudy-sky, and all-sky conditions, respectively. The overall root mean square error (RMSE) of instantaneous SSI was 80.0 W/m 2 (16.8%), 127.6 W/m 2 (55.1%), and 99.5 W/m 2 (25.5%) for clear-sky, cloudy-sky (overcast sky and partly cloudy sky), and all-sky (clear-sky and cloudy-sky) conditions, respectively. A comparison with other state-of-the-art studies suggests that our proposed method can successfully estimate SSI with a maximum improvement of an RMSE of 24 W/m 2. The clear-sky SSI retrieval was sensitive to aerosol optical thickness, which was largely dependent on the diurnal surface reflectance accuracy. Uncertainty in the pre-defined horizontal visibility for 'clearest sky' will eventually lead to considerable SSI retrieval error. Compared to cloud effective radius, the retrieval error of cloud optical thickness was a primary factor that determined the SSI estimation accuracy for cloudy skies. Our proposed method can be used to estimate SSI for clear and one-layer cloud sky, but is not suitable for multi-layer clouds overlap conditions as a lower-level cloud cannot be detected by the optical sensor when a higher-level cloud has a higher optical thickness.

show abstract

Estimation of daily average downward shortwave radiation from MODIS data using principal components regression method: Fars province case study

Cited by 6 publications

References 17 publications

Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn

Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn

Investigation of Dependent Variables on Utilizing Cotton Simulation Model

A Lookup-Table-Based Approach to Estimating Surface Solar Irradiance from Geostationary and Polar-Orbiting Satellite Data

Contact Info

Product

Resources

About