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.

China is one the largest maize (Zea mays L.) producer worldwide. Considering water deficit as one of the most important limiting factors for crop yield stability, remote sensing technology has been successfully used to monitor water relations in the soil–plant–atmosphere system through canopy and leaf reflectance, contributing to the better management of water under precision agriculture practices and the quantification of dynamic traits. This research was aimed to evaluate the relation between maize leaf water content (LWC) and ground-based and unoccupied aerial vehicle (UAV)-based hyperspectral data using the following approaches: (I) single wavelengths, (II) broadband reflectance and vegetation indices, (III) optimum hyperspectral vegetation indices (HVIs), and (IV) partial least squares regression (PLSR). A field experiment was undertaken at the Chinese Academy of Agricultural Sciences, Beijing, China, during the 2020 cropping season following a split plot model in a randomized complete block design with three blocks. Three maize varieties were subjected to three differential irrigation schedules. Leaf-based reflectance (400–2500 nm) was measured with a FieldSpec 4 spectroradiometer, and canopy-based reflectance (400–1000 nm) was collected with a Pika-L hyperspectral camera mounted on a UAV at three assessment days. Both sensors demonstrated similar shapes in the spectral response from the leaves and canopy, with differences in reflectance intensity across near-infrared wavelengths. Ground-based hyperspectral data outperformed UAV-based data for LWC monitoring, especially when using the full spectra (Vis–NIR–SWIR). The HVI and the PLSR models were demonstrated to be more suitable for LWC monitoring, with a higher HVI accuracy. The optimal band combinations for HVI were centered between 628 and 824 nm (R² from 0.28 to 0.49) using the UAV-based sensor and were consistently located around 1431–1464 nm and 2115–2331 nm (R² from 0.59 to 0.80) using the ground-based sensor on the three assessment days. The obtained results indicate the potential for the complementary use of ground-based and UAV-based hyperspectral data for maize LWC monitoring.

Asian soybean rust (Phakopsora pachyrhizi) is the most severe disease in soybean crops production. The early detection of the disease by traditional methods involves visual inspection of the symptoms present in the leaves and is expensive and time-consuming. The limitations of visual detection have led to an interest in the development of spectroscopically based detection techniques for the rapid diagnosis of this disease. Thus, this work aimed to develop a procedure for early and accurate detection and differentiation of soybean under different levels of Asian rust disease, based on spectral analysis and linear discriminant analysis (LDA), with optimum wavelengths selection by a stepwise procedure. Reflectance spectroscopy ranging from the visible (Vis) to the near-infrared (NIR) region (350–2,500 nm) was obtained by a Fieldspec 3 Jr. hyperspectral sensor through the spectral measurement of soybean leaves with different levels of disease that had the following treatments: uninfected (T1), severity 0.6% (T2), severity 2.0% (T3), severity 7.0% (T4), severity 18.0% (T5), and severity 42.0% (T6). There were 15 spectral curves measured in each treatment, totalling 90 spectral samples. Principal component analysis (PCA) was applied as an indicator of the explained variance of the reflectance spectra among the different disease progressions. The spectral signature of the leaves showed the existence of a strong increase in reflectance in the Vis region when the levels of disease increased, associated with a lower concentration of pigments. The PCA explained over 97.00% of the spectral variance in the first and second principal components and the stepwise procedure selected from 87 spectral bands. The LDA achieved global accuracies of 100.00% and 82.51%, in the calibration and validation procedures, respectively. These results suggest the spectral reflectance technique as a promising tool for cost-effective, fast analysis and a non-destructive method for diagnosis Asian soybean rust.

The cropland in the farming-pastoral ecotone of Northern China is highly unstable owing to environmental restoration projects, poor soil fertility, poverty, and rural labor loss, and it is characterized by a large number of fallow fields. Mapping fallow fields in a farming-pastoral ecotone can help in evaluating the impact of complex cropland landscapes on the environment and food security. To map the fallow fields using the Sentinel-1 and Sentinel-2 archives, a multi-metric dataset of vertical transmit–vertical receive + vertical transmit–horizontal receive polarization was established. Moreover, spectral bands and vegetation index datasets were established using Google Earth Engine to classify cropped and fallow fields using the Random Forest classifier. The overall accuracies and Kappa coefficients of different datasets were assessed to examine the dataset with the highest overall accuracy in the main growing season. A 10 m resolution fallow map for 2020 was then generated based on the combined Sentinel-1 and Sentinel-2 datasets with the highest overall accuracy and Kappa coefficients were 95.82% and 0.92, respectively. In addition, the time-series characteristics of the entropy eigenvalues generated via dual-polarization decomposition were quantitatively evaluated to clarify the contribution of the Sentinel-1 synthetic-aperture radar archive to the fallow field mapping. The eigenvalues were more sensitive to the phenological characteristics of cropped and fallow fields than the original backscatter signal of the Sentinel-1 data. Moreover, the mapping method was tested at different time intervals by gradually aggregating the results across an increasing number of months to optimize the fallow field monitoring using the minimum number of observations possible within a short period. Data aggregated over August achieved the highest one-month accuracy; it was also very close to the observations from the whole growing season. The results further emphasize the influence of Sentinel-1 archives on fallow field mapping. Overall, this study clarifies the potential applicability of Sentinel archives for monitoring and mapping managing patterns of agricultural land in a region.