TY - JOUR
T1 - Dynamics of soil surface temperature with unmanned aerial systems
AU - Basurto-Lozada, Daniela
AU - Hillier, Adeline
AU - Medina, David
AU - Pulido, Dagoberto
AU - Karaman, Sertac
AU - Salas, Joaquin
N1 - Publisher Copyright:
© 2020
PY - 2020/10
Y1 - 2020/10
N2 - Thermographies are a source of rich information, valuable in precision agriculture tasks such as crop stress assessment, plant disease analysis, and soil moisture evaluation. Traditionally, practitioners obtain soil temperature from the ground or using satellites and other airborne methods, which are costly and offer limited spatial and temporal resolution. In this paper, we introduce a method to measure soil surface temperature dynamics with the use of an unmanned aerial system (UAS). In our approach, we fuse information from thermal and multispectral cameras with ambient variables to generate estimates for soil temperature using computational intelligence models. Using the images, we produce a spatial reconstruction using structure from motion (SfM). After the multimodal registration of the resulting geo-referenced orthomosaics, we characterize the dynamics of the soil surface temperature using the differences between consecutively captured temperature orthomosaics. In our results, we are capable of estimating soil surface temperature from a UAS flying at 30 m AGL with a RMSE of 3.24∘C ± 0.3 and 1.77∘C ± 0.2, at one standard deviation, for two test fields with average ground sampling distances below 6.0 cm/pixel, using a Random Forest regressor.
AB - Thermographies are a source of rich information, valuable in precision agriculture tasks such as crop stress assessment, plant disease analysis, and soil moisture evaluation. Traditionally, practitioners obtain soil temperature from the ground or using satellites and other airborne methods, which are costly and offer limited spatial and temporal resolution. In this paper, we introduce a method to measure soil surface temperature dynamics with the use of an unmanned aerial system (UAS). In our approach, we fuse information from thermal and multispectral cameras with ambient variables to generate estimates for soil temperature using computational intelligence models. Using the images, we produce a spatial reconstruction using structure from motion (SfM). After the multimodal registration of the resulting geo-referenced orthomosaics, we characterize the dynamics of the soil surface temperature using the differences between consecutively captured temperature orthomosaics. In our results, we are capable of estimating soil surface temperature from a UAS flying at 30 m AGL with a RMSE of 3.24∘C ± 0.3 and 1.77∘C ± 0.2, at one standard deviation, for two test fields with average ground sampling distances below 6.0 cm/pixel, using a Random Forest regressor.
KW - Remote sensing
KW - Soil surface temperature
KW - Thermographic imaging
KW - Unmanned aerial systems
UR - http://www.scopus.com/inward/record.url?scp=85087646636&partnerID=8YFLogxK
U2 - 10.1016/j.patrec.2020.07.003
DO - 10.1016/j.patrec.2020.07.003
M3 - Artículo
AN - SCOPUS:85087646636
SN - 0167-8655
VL - 138
SP - 68
EP - 74
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
ER -