Remote Sensing Analysis of Surface Temperature from Heterogeneous Data in a Maize Field and Related Water Stress

Abstract

Precision agriculture aims at optimizing crop production by adapting management actions to real needs and requires that a reliable and extensive description of soil and crop conditions is available, that multispectral satellite images can provide. The purpose of the present study, based on activities carried out in 2019 on an agricultural area north of Ravenna (Italy) within the project LIFE AGROWETLANDS II, is to evaluate the potentials and limitations of freely available satellite thermal images for the identification of water stress conditions and the optimization of irrigation management practices, especially in agricultural areas and wetlands affected by saline soils and salt water capillary rise. Point field surveys and a very-high resolution thermal survey (5 cm) by an unmanned aerial vehicle (UAV) supported thermal camera were performed on a maize field tentatively at every Landsat-8 passage to check land surface temperature (LST) and canopy cover (CC) estimated from satellite. Temperature measured in the soil near ground surface and from UAV flying at 100 m altitude is compared with LST estimated from satellite measurements using three conversion methods: the top of atmosphere brightness temperature based on Landsat-8 band 10 (SB) corrected to account only for surface emissivity, the radiative transfer equation (RTE) for atmosphere effects correction, and the original split window method (SW) using both Thermal Infrared Sensor (TIRS) bands. The comparison shows discrepancies, due to extreme difference in resolution, the systematic hour of satellite passage (11 am solar time), and systematic differences between methods beside the unavoidable inaccuracy of UAV measurements. Satellite derived temperatures result usually lower than UAV measurements; SB produced the lowest values, SW the best (difference = −1.7 ± 1.7), and RTE the median (difference = −2.7 ± 1.6). The correlation between contemporary 30 m resolution temperature values of near pixels and corresponding tile-average temperatures was not significant, due to the purely numerical interpolation from the 100 m resolution TIRS images, whereas the time pattern along the season is consistent among methods, being correlation coefficient always greater than 0.85. Correlation coefficients among temperatures obtained from Landsat-8 by different methods are almost 1, showing that values are almost strictly related by a linear transformation. All the methods are useful to estimate water stress, since its associated Crop Water Stress Index (CWSI) is, from its definition, insensitive to linear transformation of temperatures. Actual evapotranspiration (ETa) maps are evaluated with the Surface Energy Balance Algorithm for Land (SEBAL) based on the three Landsat-8 derived LSTs; the higher is LST, the lower is ETa. Resulting ETa estimates are related with LST but not strictly, due to variation in vegetation cover and soil, therefore patterns result similar but not equivalent, whereas values are dependent on the atmosphere correction method. RTE and SW result in the best methods among the tested ones and the derived ETa values result reliable and appropriate to user needs. For real time application the Normalized Difference Moisture Index (NDMI), which can also be derived from more frequent Sentinel-2 passages, can be profitably used in combination or as a substitute of the CWSI.

Estimation of Intercepted Solar Radiation and Stem Water Potential in a Table Grape Vineyard Covered by Plastic Film Using Sentinel-2 Data: A Comparison of OLS-, MLR-, and ML-Based Methods

“…NDVI, EVI, and GNDVI are spectral indices widely used for estimating crop biomass [42], assessing plant health status [43,44], and conducting phenological analyses [45,46]. Conversely, NDWI 1-2-3 and NDMI are primarily related to water content and can be used for Ψstem prediction [47][48][49]. No soil-adjusted spectral indices (such as SAVI, OSAVI, MSAVI, or MSAVI2) were considered in this study due to the specific nature of the vineyard.…”

Unmanned Aerial Vehicles in Agriculture: A Survey

“…In those cases, a more complex flight plan should be performed, typically by defining waypoints where the sensor acquisition has to be done [36]. High overlap values near to 60 or even 80% between subsequent images or between tracks are usually applied [28,37].…”

“…A remarkable comparative carried out in Italy between free thermal images provided by Landsat-8 satellite and the very high resolution ones obtained by one-hundred-metersheight flights of an UAV is presented in [37]. The study makes use of a four-rotor UAV model Matrice 210 made by DJI that relies on a dual-gimbal configuration to handle the simultaneous use of two sensors' payloads.…”

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

“…Crop water management is essential in regions currently facing or predicted to face water scarcity. Infrared thermography has been successfully implemented to assess water use by crops ( Nhamo et al, 2020 ), and for measuring genotype performance under salinity or water deficit stress ( Raza et al, 2014 ; Kumar et al, 2017 ; Thapa et al, 2018 ; Hou et al, 2019 ; Zhang et al, 2019b ; Kumar et al, 2020 ; Masina et al, 2020 ). In cotton ( Gossypium arboreum ) monitored by infrared thermography, it was observed that yield, fiber length, and micronaire suffered reduction after canopy temperature exceeded a given threshold ( Conaty et al, 2015 ).…”