Soybean production both in Brazil and globally has regularly been threatened by drought periods. The use of infrared thermography to evaluate the canopy’s temperature and its relationship with plant water status constitutes an important tool for agricultural monitoring. However, studies regarding the water status of soybean plants through unmanned aerial vehicle (UAV)-based thermal imaging are yet to be reported. Thus, the present study aimed to evaluate the water status of soybean plants submitted to different water conditions via thermal images obtained through an UAV thermal infrared camera. The field experiment was undertaken at the National Soybean Research Centre (Embrapa Soja, a branch of the Brazilian Agricultural Research Corporation) in a randomized complete block design, with four replicates. The following water conditions were evaluated: irrigated (IRR, receiving rainfall and irrigation when necessary, and with a soil water matric potential between −0.03 MPa and −0.05 MPa), non-irrigated (NIRR, receiving only rainfall), and water deficit (or drought stress) induced at the vegetative (DSV) and reproductive (DSR) stages. Water deficit was induced using rainout shelters. Soil moisture and weather data were monitored daily. Thermal images were obtained on twelve dates, half in 2016–2017 and half during the 2017–2018 crop seasons, through a thermal infrared camera (DIY-Thermocam) sensitive to temperatures ranging from – 40°C to 200°C, with 0.5 °C accuracy and 14-bit radiometric resolution. Images in RAW format (160 pixels x120 pixels) were obtained at 125 m above ground level. They were then processed and calibrated by acquiring a correction factor resulting from the effect of atmospheric attenuation. The canopy temperature was evaluated in relation to that of air temperature and through the Normalized Relative Canopy Temperature (NRCT). Atmospheric attenuation was positively correlated to flight altitude, so that image correction eliminated such an effect. The thermal behaviour of soybean plants was directly correlated to soil water availability and atmospheric vapour pressure deficit, with differences ranging from 0.2 °C to 7.2°C. Plants grown at lower soil moisture conditions had higher temperatures, which were observed under DSV and DSR conditions. However, plants submitted to DSV and then rehydrated under normal field conditions demonstrated lower temperatures than those presented by plants under IRR or NIRR. Water deficit was more damaging to yields when induced at the reproductive stage. We emphasize the potential use of UAV thermal infrared cameras for monitoring the water status of soybean plants, and for collecting non-destructive information in support of better crop management decisions and new studies.

Leaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In this study, we tested the hypothesis that technology using two hyperspectral sensors for both reflectance and absorbance data would result in more accurate predictions of absorbance spectra. Our findings indicated that the green/yellow regions (500–600 nm) had a greater impact on photosynthetic pigment predictions, while the blue (440–485 nm) and red (626–700 nm) regions had a minor impact. Strong correlations were found between absorbance (R² = 0.87 and 0.91) and reflectance (R² = 0.80 and 0.78) for chlorophyll and carotenoids, respectively. Carotenoids showed particularly high and significant correlation coefficients using the partial least squares regression (PLSR) method (R²_C = 0.91, R²cv = 0.85, and R²_P = 0.90) when associated with hyperspectral absorbance data. Our hypothesis was supported, and these results demonstrate the effectiveness of using two hyperspectral sensors for optical leaf profile analysis and predicting the concentration of photosynthetic pigments using multivariate statistical methods. This method for two sensors is more efficient and shows better results compared to traditional single sensor techniques for measuring chloroplast changes and pigment phenotyping in plants.

The characterization of physical and chemical soils attributes is a pressing necessity for the agricultural land management optimization in many countries. Currently, soil analyses are performed by chemical treatments in a laboratory, generating environmental and time‐consuming problems. Remote sensing techniques can be faster and cheaper than conventional methods, do not generate chemical residues and are non‐destructive to the samples. The objective of the present work was to determine a remote sensing technique to estimate soil physical and chemical attributes in the regions of Paraguaçu Paulista and Rio Brilhante, in the States of São Paulo and Mato Grosso do Sul, respectively, Brazil, using reflectance data obtained by a sensor located in orbit. Fieldwork was performed to validate orbital data. A total of 110 soil samples were collected representing 43,000 h for the development of spectral models. Landsat‐7 ETM+images were atmospherically corrected and transformed to reflectance. The soil samples observed in the field were located by GPS and evaluated in the orbital image. The method used consists in a detailed investigation of the spectral data, in which the spectral curve, the position of the data in a graphic dispersion, the colour compositions and the pixel cursor values are evaluated. Spectral models were determined to quantify soil attributes. Soil samples from a different area had their attribute contents determined by the models. The attributes studied were sand, silt, clay, pH (CaCl₂), organic matter, phosphorus, potassium, calcium, magnesium, aluminium, hydrogen, cation exchange capacity (CEC), sum of cations (SC) base saturation (BS) and aluminium saturation (AS). The results showed weak correlations with some soil attributes, such as SC, K, Ca, Mg, Al and P. High correlations (reaching 0.86) were obtained with sand, clay, silt, OM, CEC and H. On the other hand, validation procedure indicated that the best attributes for quantification were clearly clay (0.61) and sand (0.5). Therefore, there is strong evidence that these attributes can be predicted in similar landscapes, using the multiple‐linear equations developed in this study. This knowledge can be useful in many ways in agriculture, such as soil mapping, determining soil attributes, determining soil information in difficult access regions, and diminishing traditional soil analyses with environmental protection.