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.
Highlights
Precision agriculture, which designates a combination of technologies and practices aiming to optimize the production by accounting for variability and uncertainties within agricultural systems [1], is emerging as a possible answer to the challenge of increasing food production in a sustainable way [2]
Temperature measured in the soil near ground surface and from unmanned aerial vehicle (UAV) flying at 100 m altitude is compared with land surface temperature (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 present paper aims at describing the UAV survey characteristics and calibration, the comparison between the UAV survey and satellite-derived temperatures over a maize field north of Ravenna (Italy), providing a cross-validation of the two approaches and evidence of their primary merits, as well as an assessment of the potential of freely available satellite imagery to estimate evapotranspiration and crop water stress in the farmer perspective of precise agriculture implementation
Summary
Precision agriculture, which designates a combination of technologies and practices aiming to optimize the production by accounting for variability and uncertainties within agricultural systems [1], is emerging as a possible answer to the challenge of increasing food production in a sustainable way [2]. A precise knowledge of crop deficiency and quality of soils is required for the delineation of management zones, the optimization of farming practices and, possibly, the prediction of crop yield within-field variability [4]. For this purpose, a near real-time monitoring is needed with cost-effective technologies. Remote sensing is certainly the most cost-effective method for large-scale monitoring [5] while, to perform analyses at the scale of a single crop, a proper combination of satellite and unmanned aerial vehicle (UAV) technologies is advisable [6]. UAV technologies provide very high-resolution capabilities with the maximum timing flexibility, even though they are suitable for surveys on smaller extent areas
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