Cotton Irrigation Scheduling Using Remotely Sensed and Fao-56 Basal Crop Coefficients

Hunsaker, Douglas J.; Barnes, Edward M.; Clarke, Thomas R.; Fitzgerald, Glenn J.; Pinter, P. J.

doi:10.13031/2013.19197

Cited by 126 publications

(86 citation statements)

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“…Reflectance based crop coefficient method has been studied, among others, by Reginato et al (1985), Neale et al (1989Neale et al ( , 2003, and Hunsaker et al (2005). Furthermore, crop coefficients (K c ) have been related to vegetation indices such as PVI (Jackson et al 1980;Heilman et al 1982), NDVI (Bausch and Neale 1987;Neale et al 1989), and SAVI (Bausch 1993;Neale et al 1996).…”

Section: Reflectance-based Crop Coefficient Methodsmentioning

confidence: 99%

ET mapping for agricultural water management: present status and challenges

et al. 2007

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Evapotranspiration (ET) is an essential component of the water balance. Remote sensing based agrometeorological models are presently most suited for estimating crop water use at both field and regional scales. Numerous ET algorithms have been developed to make use of remote sensing data acquired by sensors on airborne and satellite platforms. In this paper, a literature review was done to evaluate numerous commonly used remote sensing based algorithms for their ability to estimate regional ET accurately. The reported estimation accuracy varied from 67 to 97% for daily ET and above 94% for seasonal ET indicating that they have the potential to estimate regional ET accurately. However, there are opportunities to further improving these models for accurately estimating all energy balance components. The spatial and temporal remote sensing data from the existing set of earth observing satellite platforms are not sufficient enough to be used in the estimation of spatially distributed ET for on-farm irrigation management purposes, especially at a field scale level (*10 to 200 ha). This will be constrained further if the thermal sensors on future Landsat satellites are abandoned. However, research opportunities exist to improve the spatial and temporal resolution of ET by developing algorithms to increase the spatial resolution of reflectance and surface temperature data derived from Landsat/ ASTER/MODIS images using same/other-sensor high resolution multi-spectral images.

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Section: Reflectance-based Crop Coefficient Methodsmentioning

confidence: 99%

ET mapping for agricultural water management: present status and challenges

et al. 2007

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“…The remotely sensed normalised difference vegetation index (NDVI) computed from crop canopy reflectance measurements in the red and near-infrared (NIR) wavelengths has been found to be a useful tool in computing accurate crop coefficients for a broad range of crops [69]. Singh et al [70] has demonstrated the use of the calculated reference ET and the remotely sensed NDVI in estimating the water use of cotton.…”

Section: Weather-based Sensingmentioning

confidence: 99%

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

et al. 2017

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Globally, the irrigation of crops is the largest consumptive user of fresh water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water but do not compromise crop quality and yield. Precision irrigation already achieves this goal, in part. The goal of precision irrigation is to accurately supply the crop water need in a timely manner and as spatially uniformly as possible. However, to maximize the benefits of precision irrigation, additional technologies need to be enabled and incorporated into agriculture. This paper discusses how incorporating adaptive decision support systems into precision irrigation management will enable significant advances in increasing the efficiency of current irrigation approaches. From the literature review, it is found that precision irrigation can be applied in achieving the environmental goals related to sustainability. The demonstrated economic benefits of precision irrigation in field-scale crop production is however minimal. It is argued that a proper combination of soil, plant and weather sensors providing real-time data to an adaptive decision support system provides an innovative platform for improving sustainability in irrigated agriculture. The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil-plant-atmosphere system while considering operational limitations and agronomic objectives in arriving at optimal irrigation decisions. It is concluded that significant improvements in crop yield and water savings can be achieved by incorporating model predictive control into precision irrigation decision support tools. Further improvements in water savings can also be realized by including deficit irrigation as part of the overall irrigation management strategy. Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks that can be applied to evaluate adaptive decision support strategies related to irrigation.

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“…Two important indicators of these parameters-leaf area index (LAI) and above ground biomass (AGB)-were used to monitor crop canopy structural development and growth changes and to estimate yield. The reasonable and reliable estimation of LAI and biomass can improve crop fertilizer applications [2], water irrigation [3,4], disease and weed control [5,6], and grain production marketing [7][8][9]. LAI and biomass change seasonally under different environmental conditions, and therefore, it is important to timely estimate their values.…”

Section: Introductionmentioning

confidence: 99%

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

et al. 2015

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Leaf area index (LAI) and biomass are frequently used target variables for agricultural and ecological remote sensing applications. Ground measurements of winter wheat LAI and biomass were made from March to May 2014 in the Yangling district, Shaanxi, Northwest China. The corresponding remotely sensed data were obtained from the earth-observation satellites Huanjing (HJ) and RADARSAT-2. The objectives of this study were (1) to investigate the relationships of LAI and biomass with several optical spectral vegetation indices (OSVIs) and radar polarimetric parameters (RPPs), (2) , p < 0.01) were highly correlated with biomass. The estimation accuracy of LAI and biomass was better using the COSVI-RPPs than using the OSVIs and RPPs alone. The results revealed that the PLSR regression equation better estimated LAI and biomass than the MSR regression equation based on all the COSVI-RPPs, OSVIs, and RPPs. Our results indicated that the COSVI-RPPs can be used to robustly estimate LAI and biomass. This study may provide a guideline for improving the estimations of LAI and biomass of winter wheat using multisource remote sensing data.

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Cotton Irrigation Scheduling Using Remotely Sensed and Fao-56 Basal Crop Coefficients

Cited by 126 publications

References 0 publications

ET mapping for agricultural water management: present status and challenges

ET mapping for agricultural water management: present status and challenges

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

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