Surface Aerodynamic Temperature Modeling over Rainfed Cotton

Chávez, José L.; Howell, Terry A.; Gowda, Prasanna H.; Copeland, Karen S.; Prueger, John H.

doi:10.13031/2013.30081

Cited by 14 publications

(2 citation statements)

References 45 publications

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“…In a study over rainfed cotton subjected to a highly advective environment, in the Texas High Plains, near Bushland, Texas, USA, Chávez et al (2010) , or -4.9±16.3%); which resulted in an improvement of the estimation of latent heat fluxes (error -6±35 W m -2 , or -1.8±9.7%). It seems that incorporating the interactions between the crop row layout and wind direction, in the modelling of T o , better describes the dynamic turbulent mixing for the generation of H.…”

Section: Cotton T O Modelling-dryland Agriculture Casementioning

confidence: 99%

Remote Sensing and Aerodynamic Temperature-Based Energy Balance Models to Estimate Crop Evapotranspiration Rates

Chávez¹

2023

JAS

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Different methods exist to measure or estimate actual crop evapotranspiration (ETa). However, some methods require a large number of data input or strict field conditions. Remote sensing based ETa algorithms based on extreme thermal pixels (hot and cold) have limitations when required extreme pixels are not present in the acquired thermal infra-red imagery. In addition, satellite overpass frequency and spatial pixel resolution may be a limitation for some agricultural fields and micro-climates. Surface energy balance methods that use surface radiometric temperatures often fail to perform well under drought, limited irrigation, salt affected soils, or under sparse vegetation conditions. One option is to measure or estimate the crop/surface sensible heat flux through the aerodynamic temperature approach, then calculate the available energy and solve the energy balance for latent heat flux. Thus, this study presents different published algorithms that characterize the crop or field surface aerodynamic temperature and then applies them to different conditions for evaluation. Determining spatial ETa continuously has the potential to improve the irrigation water management decision making. The aerodynamic temperature approach was initially developed with good results as a function of surface radiometric temperature, air temperature, crop leaf area index, and wind speed or surface aerodynamic resistance. However, the inclusion of the crop fractional percent cover and of a new resistance term (turbulent-mixing row resistance) greatly improved the estimation of the sensible heat and latent heat fluxes, when evaluated with heat flux data derived from eddy covariance energy balance towers. Results also indicate that the aerodynamic method has transferability potential to different regions, crops, and irrigation methods than the conditions encountered in the method development.

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Section: Cotton T O Modelling-dryland Agriculture Casementioning

confidence: 99%

Remote Sensing and Aerodynamic Temperature-Based Energy Balance Models to Estimate Crop Evapotranspiration Rates

Chávez¹

2023

JAS

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“…Some of the fundamental challenges in ET mapping through SEB models have evolved due to, (i) the inequality between LST and the aerodynamic temperature (T 0 ), which is essentially responsible for the exchanges of H [124,125,148,149]; (ii) a non-unique relationship between T 0 and LST due to differences between the roughness lengths (i.e., effective source/sink heights) for momentum (z 0M ) and heat (z 0H ) within vegetation canopy and substrate complex [124,[150][151][152]; (iii) the unavailability of a universally agreed model to estimate spatially explicit T 0 [125,153]; (iv) the lack of a physical-based or analytical g A model as a true representative for spatial application [124,125]; (v) complexities in g A parameterization to accommodate the differences between the scalar roughness lengths of heat (z 0H ) and momentum (z 0M ) transfer. Such parameterizations are significantly affected by land-surface heterogeneity and could cause the ET severely overestimated in the semi-arid or arid ecosystems.…”

Section: Et Modelingmentioning

confidence: 99%

Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review

et al. 2019

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Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems.

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Determining maize water stress through a remote sensing-based surface energy balance approach

2020

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Surface Aerodynamic Temperature Modeling over Rainfed Cotton

Cited by 14 publications

References 45 publications

Remote Sensing and Aerodynamic Temperature-Based Energy Balance Models to Estimate Crop Evapotranspiration Rates

Remote Sensing and Aerodynamic Temperature-Based Energy Balance Models to Estimate Crop Evapotranspiration Rates

Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review

Determining maize water stress through a remote sensing-based surface energy balance approach

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