Water productivity assessments for dwarf coconut by using Landsat 8 images and agrometeorological data

Teixeira, A. H. de C.; Miranda, F. R. de; Leivas, J. F.; Pacheco, Edson Patto; Garcon, E. A. M.

doi:10.1016/j.isprsjprs.2019.07.006

Cited by 16 publications

(14 citation statements)

References 35 publications

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“…The residual method to estimate T 0 has been successfully tested in distinct Brazilian agroecosystems (Teixeira et al, 2019(Teixeira et al, , 2020aAraujo et al, 2019;Silva et al, 2019;Rampazo et al, 2020). The suitability of retrieving the WP components, without the satellite thermal bands, has been also demonstrated in other recent studies around the word (Castelli et al, 2018;Rozenstein et al, 2018;Vanino et al, 2018;Mokhtari et al, 2019;Longo-Minnolo et al, 2020).…”

Section: Agrometeorological and Remote Sensing Data Processingmentioning

confidence: 91%

“…To obtain the regression coefficients of Eq. ( 1), the L8 Ref values from bands B 1 to B 7 for the total RPA overflown area were downloaded from the United States Geological Survey (USGS) site (https://eart hexplorer.usgs.gov/), and the weights for each band applied to the L8 albedo values (α 0_L8 ) according to Teixeira et al (2019). Although the L8 images being acquired at different days regarding the Sq image dates, the α 0_L8 relation with the accumulated degree-days (DD ac ), allowed to obtain the Sq α 0 counterpart values for the RPA flight days.…”

Section: Agrometeorological and Remote Sensing Data Processingmentioning

confidence: 99%

“…( 1) obtained (Fig. 3b): It is emphasized that if one has already the band reflectance values available from the RPA onboard camera, through control ground targets, the weights for α 0 calculations may be directly applied to Ref values (Teixeira et al, 2019). On the other hand, the coefficients from Eq.…”

Section: Agrometeorological and Remote Sensing Data Processingmentioning

confidence: 99%

“…Currently, precision agriculture research using remotely sensed data can help N fertilizing strategies (Sharma and Bali, 2018;Colaço and Bramley, 2018), plant protection managements (Mahlein et al, 2012;Sedina et al, 2017), biomass estimations (Teixeira et al, 2019;Togeiro de Alckmin et al, 2020); and to retrieve physical (Ballabio et al, 2016), biological (Yigini and Panagos, 2016), and chemical (Ballabio et al, 2019) properties of soils. However, there are still lacks of remote sensing studies for corn WP modelling at the level of precision agriculture in Brazil, with few ones done at irrigation pivot parcels with Landsat 8 images at spatial resolutions ranging from 30 to 120 m (Teixeira et al, 2014b;Venancio et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

SAFER applications for water productivity assessments with aerial camera onboard a remotely piloted aircraft (RPA). A rainfed corn study in Northeast Brazil

Teixeira

Pacheco

Silva

et al. 2021

Remote Sensing Applications: Society and Environment

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Section: Agrometeorological and Remote Sensing Data Processingmentioning

confidence: 91%

Section: Agrometeorological and Remote Sensing Data Processingmentioning

confidence: 99%

Section: Agrometeorological and Remote Sensing Data Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SAFER applications for water productivity assessments with aerial camera onboard a remotely piloted aircraft (RPA). A rainfed corn study in Northeast Brazil

Teixeira

Pacheco

Silva

et al. 2021

Remote Sensing Applications: Society and Environment

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“…Calculation is made using visible, near infrared, and thermal infrared bands in conjunction with reference evapotranspiration data (ETo) estimated with meteorological station data (Teixeira, 2010;Teixeira et al, 2017Teixeira et al, , 2013. SAFER algorithm applications involving large heterogeneous areas have shown very good performance (Coaguila et al, 2017;Dehziari and Sanaienejad, 2019;Teixeira et al, 2019Teixeira et al, , 2017Teixeira et al, , 2013. However, when applied to irrigated annual crops, which are more homogenous, SAFER provides unsatisfactory ET estimates during the early stages of crop growth (Althoff et al, 2019;Lima et al, 2019) thus requiring calibration and validation of SAFER algorithm.…”

Section: Introductionmentioning

confidence: 99%

Evapotranspiration mapping of commercial corn fields in Brazil using SAFER algorithm

Venâncio

Mantovani

Amaral

et al. 2021

Sci. agric. (Piracicaba, Braz.)

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SAFER (Simple Algorithm for Evapotranspiration Retrieving) is a relatively new algorithm applied successfully to estimate actual crop evapotranspiration (ET) at different spatial scales of different crops in Brazil. However, its use for monitoring irrigated crops is scarce and needs further investigation. This study assessed the performance of SAFER to estimate ET of irrigated corn in a Brazilian semiarid region. The study was conducted in São Desidério, Bahia State, Brazil, in corn-cropped areas in no-tillage systems and irrigated by central pivots. SAFER algorithm with original regression coefficients (a = 1.8 and b =-0.008) was initially tested during the growing seasons of 2014, 2015, and 2016. SAFER performed very poorly for estimating corn ET, with RMSD values greater than 1.18 mm d-1 for 12 fields analyzed and NSE values < 0 in most fields. To improve estimates, SAFER regression coefficients were calibrated (using 2014 and 2015 data) and validated with 2016 data, with the resulting coefficients a and b equal to 0.32 and-0.0013, respectively. SAFER performed well for ET estimation after calibration, with r 2 and NSE values equal to 0.91 and RMSD = 0.469 mm d-1. SAFER also showed good performance (r 2 = 0.86) after validation, with the lowest RMSD (0.58 mm d-1) values for the set of 14 center pivots in this growing season. The results support the use of calibrated SAFER algorithm as a tool for estimating water consumption in irrigated corn fields in semiarid conditions.

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Single and basal crop coefficients for estimation of water requirements of subtropical and tropical orchards and plantations with consideration of fraction of ground cover, height, and training system

Paredes,

Petry,

Oliveira

et al. 2024

Irrig Sci

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This paper provides an overview of the research carried out over the last 25 years on the FAO56 single and basal crop coefficients of subtropical and tropical orchards and plantations of cactus pear, dragon fruit, fig, jujube, passion fruit, pomegranate, cape gooseberry, cherimoya, guava, longan, lychee, mango, papaya, acerola, carambola, cashew, cacao, coffee, jaboticaba, jatropha, macadamia, açai palm, coconut, date palm, guayule, oil palm, peach palm, ramie and rubber tree. The main objective of this review is to update standard single crop coefficients (Kc) and basal crop coefficients (Kcb) and complete the Kc and Kcb values tabulated in FAO56. Kc is the ratio between the non-stressed crop evapotranspiration (ETc) and the grass reference evapotranspiration (ETo), and Kcb is the ratio between the crop transpiration (Tc) and the ETo. When selecting and analysing the literature, only studies that used the FAO Penman–Monteith equation, or another equation well related to the former to compute ETo were considered, while ETc or Tc were obtained from accurate field measurements on crops under pristine (non-stress cropping conditions) or eustress (“good stress”) conditions. Articles meeting these conditions were selected to provide data for updating Kc and Kcb under standard conditions. The related description of orchards and plantations refers to crop cultivar and rootstock, irrigation systems and scheduling, planting spacing, fraction of ground cover (fc) by the crops, crop height (h), crop age and training systems, as Kc and Kcb values depend on these characteristics. To define the standard Kc and Kcb values of the selected crops, the values collected in the literature were compared with previously tabulated standard Kc and Kcb values. The updated tabulated values are transferable to other locations and climates and can be used to calculate and model crop water requirements, primarily for irrigation planning and scheduling, and thereby supporting of improved water use and savings, which is the overall aim of the current review.

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Water productivity assessments for dwarf coconut by using Landsat 8 images and agrometeorological data

Cited by 16 publications

References 35 publications

SAFER applications for water productivity assessments with aerial camera onboard a remotely piloted aircraft (RPA). A rainfed corn study in Northeast Brazil

SAFER applications for water productivity assessments with aerial camera onboard a remotely piloted aircraft (RPA). A rainfed corn study in Northeast Brazil

Evapotranspiration mapping of commercial corn fields in Brazil using SAFER algorithm

Single and basal crop coefficients for estimation of water requirements of subtropical and tropical orchards and plantations with consideration of fraction of ground cover, height, and training system

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