Evaluation of single crop coefficient curves derived from Landsat satellite images for major crops in Iran

Mokhtari, Ali; Noory, Hamideh; Vazifedoust, Majid; Palouj, Mojtaba; Bakhtiari, Atousa; Barikani, Elham; Afrooz, Ramezan Ali Zabihi; Fereydooni, Fatemeh; Naeni, Ali Sadeghi; Pourshakouri, Farrokh; Badiehneshin, Alireza; Afrasiabian, Yasamin

doi:10.1016/j.agwat.2019.03.024

Cited by 9 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The length of the growing season is profoundly influenced by the planting dates, crop varieties and climatic elements so that the shortest and longest length is usually occurred in the A_M_VW climate and the SA_Cd_W climate, respectively. Mokhtari et al (2019) analyzed the lengths of the growing seasons of major crops in three different agro-climate zones (SA_Cd_W, A_Cl_W, and A_M_VW) of Iran using satellite data and concluded similar results. The higher air temperature in the both A_Cl_W and A_Cl_VW climates causes the lengths of the growing seasons to be shorter.…”

Section: The Length Of the Growing Season And Reference Evapotranspir...mentioning

confidence: 83%

See 1 more Smart Citation

A web-based decision support system for assessing water productivity of major crops in Iran under different climatic conditions

Vazifedoust

Mokhtari

Barikani³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Knowledge on Water productivity (WP) of strategic crops, nationwide, will result in optimizing the consumption of agricultural water, proper cropping pattern and more financial benefits. In this study, a national web-based simulation portal was developed to evaluate the maximum achievable WP on a national scale. The National Water Portal (NWP) was consisting of four national databases (climatic, soil, crop and spatial data), a lump water balance model and a graphical user interface (GUI) to support computing the irrigation water requirements and evaluate the WP indicators at farm to national scale on the network. WP indicators defined as yield per crop evapotranspiration (WPETc), yield per net irrigation requirement (WPNIR), and the financial benefit per consumed water (WPEco) was calculated for the dominant strategic crop consist of winter wheat, barley, rice, maize, sugar beet and sugarcane. Net irrigation requirement was estimated using a lamp water balance model based on the dual crop coefficient approach presented by FAO 56. The results indicated that winter wheat and barley with NIR of 258 to 4235 m3 ha− 1 has the highest WPEco among the studied crops and rice with NIR of 4495 to 8907 m3 ha− 1 stands in the next category. WPEco for maize and forage maize (3747 to 7083 m3 ha− 1) was higher than WPEco for sugarcane and sugar beet. Sugar cane with NIR of 18318 m3 ha− 1 had distinguishably lower WPEco value among the studied crops because of its long growing season. The results suggested sugar cane to be replaced by with sugar beet (NIR from 5100 to 11896 m3 ha− 1) with 4 times higher WPEco.

show abstract

Section: The Length Of the Growing Season And Reference Evapotranspir...mentioning

confidence: 83%

“…The data on growth length of major crops was also validated by time series of vegetation indices derived from Landsat and Sentinels images over 120 plains in Iran (Mokhtari et al 2019).…”

Section: Climate Databasementioning

confidence: 99%

A web-based decision support system for assessing water productivity of major crops in Iran under different climatic conditions

Vazifedoust

Mokhtari

Barikani³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The center of Karaj is on latitude 35° 50′ N and longitude 50° 56′ E. Karaj has 67 000 ha of agricultural land and agricultural productivity is very high. Annually, about a million tons of various agricultural products are produced in this city 17 . The areas of fields in Ray and Karaj are approximately 53 ha and 37 ha, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Annually, about a million tons of various agricultural products are produced in this city. 17 The areas of fields in Ray and Karaj are approximately 53 ha and 37 ha, respectively.…”

Section: Study Areamentioning

confidence: 99%

Remotely sensed vegetation indices for crop nutrition mapping

Sharifi

2020

J Sci Food Agric

View full text Add to dashboard Cite

BACKGROUND: Spectral reflectance measurements and vegetation indices have been used widely to monitor plant nutritional status. Remote sensing methods are very fast, convenient, and non-destructive techniques for estimating crop nutrition indices. With the rapid development of remote sensing satellites, unmanned aerial vehicles, and the Internet of Things, remote sensing will increasingly be used in crop nutrition diagnosis. This can lead to lower operating costs for fertilization and can minimize loss of productivity. RESULTS: In this study, satellite images were used for 2 years to evaluate the use of vegetation indices for crop nutrition mapping, in two regions, located in the cities of Ray and Karaj. For each farm, eight reference points in each year were selected to determine the crop nutrition status for model development and accuracy assessment. Then, ten spectral vegetation indices were used to calculate the crop nutrition content at mid-season and at the end of the season. The results indicated that transformed chlorophyll absorption in reflectance index (TCARI) and modified chlorophyll absorption in reflectance index (MCARI) had the highest R 2 and the lowest RMSE values, respectively (R 2 TCARI = 0.83, RMSE TCARI = 12.47 kg ha −1 and R 2 MCARI = 0.75 and RMSE MCARI = 14.47 kg ha −1). CONCLUSION: The performance of TCARI and MCARI was strong across the two farms and allowed the creation of highly accurate crop nutrition maps. In conclusion, the use of the near infra-red and red-edge bands in mid-season led to better results than vegetation indices calculated at the end of the season or that do not included the red-edge band.

show abstract

“…Nevertheless, there are widely used operating satellites and UAVs which lack the ability to capture images in the thermal portions of the spectrum. Hence, some researchers have taken advantage of data fusion methods based on the relationship between Moderate Resolution Imaging Spectroradiometer (MODIS) LST product and Vegetation Indices (Vis) in order to eliminate the need for thermal bands when calculating potential ET using Landsat 8 and Sentinel-2 [26,27]. Xue et al [28] proposed a modified data mining sharpener approach to estimate a 30-m high temporal resolution LST by fusing ECOSTRESS/VIIRS data with harmonized Landsat and Sentinel-2 surface reflectance data.…”

Section: Introductionmentioning

confidence: 99%

Actual Evapotranspiration from UAV Images: A Multi-Sensor Data Fusion Approach

et al. 2021

Self Cite

View full text Add to dashboard Cite

Multispectral imaging using Unmanned Aerial Vehicles (UAVs) has changed the pace of precision agriculture. Actual evapotranspiration (ETa) from the very high spatial resolution of UAV images over agricultural fields can help farmers increase their production at the lowest possible cost. ETa estimation using UAVs requires a full package of sensors capturing the visible/infrared and thermal portions of the spectrum. Therefore, this study focused on a multi-sensor data fusion approach for ETa estimation (MSDF-ET) independent of thermal sensors. The method was based on sharpening the Landsat 8 pixels to UAV spatial resolution by considering the relationship between reference ETa fraction (ETrf) and a Vegetation Index (VI). Four Landsat 8 images were processed to calculate ETa of three UAV images over three almond fields. Two flights coincided with the overpasses and one was in between two consecutive Landsat 8 images. ETrf was chosen instead of ETa to interpolate the Landsat 8-derived ETrf images to obtain an ETrf image on the UAV flight. ETrf was defined as the ratio of ETa to grass reference evapotranspiration (ETr), and the VIs tested in this study included the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and Land Surface Water Index (LSWI). NDVI performed better under the study conditions. The MSDF-ET-derived ETa showed strong correlations against measured ETa, UAV- and Landsat 8-based METRIC ETa. Also, visual comparison of the MSDF-ET ETa maps was indicative of a promising performance of the method. In sum, the resulting ETa had a higher spatial resolution compared with thermal-based ETa without the need for the Albedo and hot/cold pixels selection procedure. However, wet soils were poorly detected, and in cases of continuous cloudy Landsat pixels the long interval between the images may cause biases in ETa estimation from the MSDF-ET method. Generally, the MSDF-ET method reduces the need for very high resolution thermal information from the ground, and the calculations can be conducted on a moderate-performance computer system because the main image processing is applied on Landsat images with coarser spatial resolutions.

show abstract

Evaluation of single crop coefficient curves derived from Landsat satellite images for major crops in Iran

Cited by 9 publications

References 26 publications

A web-based decision support system for assessing water productivity of major crops in Iran under different climatic conditions

A web-based decision support system for assessing water productivity of major crops in Iran under different climatic conditions

Remotely sensed vegetation indices for crop nutrition mapping

Actual Evapotranspiration from UAV Images: A Multi-Sensor Data Fusion Approach

Contact Info

Product

Resources

About