A Three-Dimensional Index for Characterizing Crop  Water Stress

Torrion, Jessica A.; Maas, Stephan J.; Guo, Wenxuan; Bordovsky, James P.; Cranmer, Andy M.

doi:10.3390/rs6054025

Cited by 11 publications

(18 citation statements)

References 24 publications

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“…In this work, we chose the TIGR database TIGR2002_v1.1 as the atmospheric data to execute the radiative transfer simulation procedure (see Section 2.5). However, it was found that TIGR2002_v1.1 is centred at low W values of less than 1.0 g/cm 2 . To obtain robust statistics, it is necessary to select different atmospheric profiles to make W uniformly distributed from dry to moist.…”

Section: Atmospheric Profile Datamentioning

confidence: 99%

“…To obtain robust statistics, it is necessary to select different atmospheric profiles to make W uniformly distributed from dry to moist. Figure 2 shows the selected 126 atmospheric profiles in which the atmospheric temperature (T 0 ) in the lowest layer varies from 232.25 K to 311.95 K and W varies from 0.09 g/cm 2 to 6.15 g/cm 2 , which represent worldwide atmospheric conditions with a moderate sample number and ensure that there is a nearly uniform distribution for W. Ninety-eight profiles (referred to as TIGR_98) were used for development of the LST retrieval algorithm and twenty-eight (referred to as TIGR_28) were used for validation. for the selected atmospheres.…”

Section: Atmospheric Profile Datamentioning

confidence: 99%

“…For atmospheric profiles of W < 1 g/cm 2 , C ranges from approximately 5 K to 9 K and increases with W. To parameterize C, the linear dependence on W can be used. For atmospheres of W > 1 g/cm 2 , a larger range (approximately 0~9 K) was obtained, and the linear dependence on W appears to be unsatisfactory to parameterize C. To explain the regularity of C for W > 1 g/cm 2 , Figure 8 was produced. For a given surface-air temperature difference T s − T 0 , constant C displays a linear dependence on W. The slope and intercept of each linear relationship are listed in Table 1.…”

Section: Algorithm Developmentmentioning

confidence: 99%

“…As a key parameter of the surface energy budget, land surface temperature (LST) is directly related to surface energy fluxes and to the latent heat flux (evapotranspiration) and water stress in particular [1,2]. The LST is crucial for estimating the net radiation driven by the surface longwave emission [3] and for computing soil moisture [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data

et al. 2017

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Land surface temperature (LST) is a key variable in the study of the energy exchange between the land surface and the atmosphere. Among the different methods proposed to estimate LST, the quadratic split-window (SW) method has achieved considerable popularity. This method works well when the emissivities are high in both channels. Unfortunately, it performs poorly for low land surface emissivities (LSEs). To solve this problem, assuming that the LSE is known, the constant in the quadratic SW method was calculated by maintaining the other coefficients the same as those obtained for the black body condition. This procedure permits transfer of the emissivity effect to the constant. The result demonstrated that the constant was influenced by both atmospheric water vapour content (W) and atmospheric temperature (T 0 ) in the bottom layer. To parameterize the constant, an exponential approximation between W and T 0 was used. A LST retrieval algorithm was proposed. The error for the proposed algorithm was RMSE = 0.70 K. Sensitivity analysis results showed that under the consideration of NE∆T = 0.2 K, 20% uncertainty in W and 1% uncertainties in the channel mean emissivity and the channel emissivity difference, the RMSE was 1.29 K. Compared with AST 08 product, the proposed algorithm underestimated LST by about 0.8 K for both study areas when ASTER L1B data was used as a proxy of Gaofen-5 (GF-5) satellite data. The GF-5 satellite is scheduled to be launched in 2017.

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Section: Atmospheric Profile Datamentioning

confidence: 99%

Section: Atmospheric Profile Datamentioning

confidence: 99%

Section: Algorithm Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data

et al. 2017

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“…It generally occurs due to a decline in leaf turgor and atmospheric vapor pressure along with root-generated chemical signals causing a measurable reduction of leaf stomatal conductance to water vapor [4] and transpiration rate. The decrease in evaporative cooling causes an increase of vegetation temperature that can be tracked using thermal imagery [5,6]. The relationship between vegetation temperature and transpiration rates is affected by water stress experienced by the plants but also by environmental parameters, as the vapor pressure deficit of the air (VPD).…”

Section: Introductionmentioning

confidence: 99%

Discriminating Irrigated and Rainfed Maize with Diurnal Fluorescence and Canopy Temperature Airborne Maps

Rossini

Panigada

Cilia

et al. 2015

IJGI

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This study evaluates the potential of airborne remote sensing images to detect water stress in maize. Visible and near infrared CASI (Itres Research Ltd., Calgary, AL, Canada) and thermal AHS-160 (Sensytech Inc., Beverly, MA, USA) data were acquired at three different times during the day on a maize field (Zea mays L.) grown with three different irrigation treatments. An intensive field campaign was also conducted concurrently with image acquisition to measure leaf ecophysiological parameters and the leaf area index. The analysis of the field data showed that maize plants were experiencing moderate to severe water stress in rainfed plots and a weaker stress condition in the plots with a water deficit imposed between stem elongation and flowering. Vegetation indices including the normalized difference vegetation index (NDVI) and the photochemical reflectance index (PRI) computed from the CASI images, sun-induced chlorophyll fluorescence (F760) and canopy temperature (Tc) showed different performances in describing the water stress during the day. During the OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 627 morning overpass, NDVI was the index with the highest discriminant power due to the sensitivity of NDVI to maize canopy structure, affected by the water irrigation treatment. As the day progressed, processes related to heat dissipation through plant transpiration became more and more important and at midday Tc showed the best performances. Furthermore, Tc retrieved from the midday image was the only index able to distinguish all the three classes of water status. Finally, during the afternoon, PRI and F760 showed the best performances. These results demonstrate the feasibility to detect water stress using thermal and optical airborne data, pointing out the importance of careful planning of the airborne surveys as a function of the specific aims of the study.

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Water deficit detection in sugarcane using canopy temperature from satellite images

et al. 2020

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Water deficit (WD) is the main yield gap for sugarcane in Midwest Brazil. Thus, WD detection is essential to quantify yield losses, but field detection requires measurement of soil water content over large areas. In this study, we tested leaf temperature (TL) and land surface temperature (TS) to detect WD in a commercial sugarcane area. The area is located in the central region of Goiás State, Brazil. According to Köppen classification, the climate of the region is Aw (humid tropical, with rainy summer and dry winter). The soil is a Ferralsol (clayey texture). TL was measured by a portable infrared thermometer, and TS was obtained using a spectral image from Landsat 8. Both TL and TS measurements occurred between 28 Jan and 24 Aug 2014 (298-506 DAP). The water balance identified periods of water deficit (WD) and surplus (WS). The difference between TL Ta was greater than zero (7.11 °C) in WD periods and lower than zero (-2.18 °C) in WS periods. The difference between TS-Ta, in turn, ranged from -0.66 °C to 4.06 °C, but not following the tendency of WD or WS, which is associated with a relative error between TL and TS near 20% for some date. The TS Ta difference detected soil WD or WS when the relative error was low (362 and 410 DAP) and under higher WD (506 DAP) and WS (394 DAP). This way, TL was able to detect WD and WS along sugarcane growth, while TS showed limited application, requiring improvement based on surface properties to reduce the error in relation to TL. Furthermore, bands 10 and 11 are recommended for surface temperature estimation. Calibration uncertainty increases when the band 11 is used alone, being this band more affected by the absorption of radiation by the atmospheric water vapor, which implies larger errors related to the atmospheric profile in the acquisition of surface temperature.

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A Three-Dimensional Index for Characterizing Crop Water Stress

Cited by 11 publications

References 24 publications

Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data

Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data

Discriminating Irrigated and Rainfed Maize with Diurnal Fluorescence and Canopy Temperature Airborne Maps

Water deficit detection in sugarcane using canopy temperature from satellite images

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