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Soil salinization is one of the important factors limiting the sustainable development of agriculture and causing land degradation. In order to explore the effect of growth stage division on the estimation accuracy of soil salt content (SSC) in fields cropped with sunflower, multiple time‐series unmanned aerial vehicle (UAV) were used to monitor SSC in the Hetao Irrigation District, Inner Mongolia, China. Aerial and field campaigns for four growth stages of sunflowers in six study areas were conducted from July to September in 2021. The ground samplings of electrical conductivity (EC), leaf area index (LAI), plant height (H), and leaf chlorophyll content (CHL) were taken simultaneously with (UAV) multispectral images. The correlation between six vegetation indices (VIs), four salinity indices (SIs), three crop parameters (LAI, CHL, H) and SSC was investigated. The optimal parameters were determined and used as input variables to establish SSC estimation model using artificial neural network (ANN), random Forest (RF), multiple linear regression (MLR) algorithm, respectively. The results show that the division of growth stages could improve the correlation between spectral index, growth parameters and SSC, and the estimation model by each growth stage was more accurate than that of the whole growth period. Among the spectral indices, the VIs showed a higher correlation with SSC than the SIs. Among the crop parameters, the LAI was the most sensitive to the degree of soil salinization. The nonlinear regression algorithm (ANN, RF) performed better than the linear regression model (MLR) in the application of SSC estimation, and the best estimation models for the four growth stages of sunflower were the ANN_SSC models. This study proposed a fast and low‐cost method to monitor the soil salinization of sunflower‐cropped fields in time‐series and provided a reference for the quick perception and prevention of soil salinization. [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of crop evapotranspiration (ET c) is essential for efficient irrigation management at the farmland scale. However, effective decision-making for irrigation scheduling requires high spatiotemporal-resolution data to provide within-field heterogeneity information. The objective of this study was to evaluate the use of optical and thermal information obtained from an unmanned aerial vehicle (UAV) to quantify maize (Zea mays L.) ET c within the framework of the FAO-56 dual crop coefficient approach. Canopy temperature-based crop water stress index (CWSI) and number of degrees above canopy threshold (DACT) were used to determine the stress coefficient (K s). Three types of normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) were adopted to determine basal crop coefficient (K cb). Then the two forms of stress coefficient in combination with three vegetation indices (VIs) were evaluated to estimate daily crop ET c under different irrigation treatments over two years in the southwest region of Inner Mongolia, China. The results demonstrated that the combination of NDVI and CWSI produced the best estimates of maize ET c , with R2 of 0.84 and root mean square error (RMSE) of 0.50 mm/day. The DACT-based model also performed well, with R2 of 0.77–0.80 and RMSE of 0.53 mm/day. Although the results varied with irrigation levels, the daily mean bias error of ET c predictions over different years indicated acceptable accuracy, with a mean bias error (MBE) of 0.48 mm/day. Sensitivity analyses indicated that ET c models were most sensitive to the slope of the linear regression between K cb and VIs, followed by soil evaporation constant and influential factor in K s_DACT. From this study, the combinations of vegetation index and CWSI, as well as DACT, were recommended as alternative approaches for estimating ET c due to intrinsic simplicity and easy interpretation. These ET c models relying on UAV-based multi-sensor data thus show promising potential in farmland-scale applications. • The potential of low-cost UAV-based image data for maize ET c prediction was evaluated. • Three vegetation indices NDVI, SAVI, and EVI were used to determine K cb. • Two different forms of K s methods (CWSI and DACT) were compared. • The robustness and adaptability of UAV-based ET c estimation model across different irrigation treatments was discussed. [ABSTRACT FROM AUTHOR]

• The effect of imagery collected platform on the fixed-threshold method was assessed. • Machine learning regression algorithms were adopted to relate UAV VIs to FVC. • The established FVC estimation model was tested for different growing season. • RF model performed well for different growing seasons, stages, and water status. Crop water stress is an inevitable and increasing challenge for agriculture. To improve crop water use efficiency, management of water stress and accurate estimation of crop traits were required. Among crop traits used to detect crop growth status and predict yield, fractional vegetation cover (FVC) is of great significance. We conducted studies in a maize field located in Inner Mongolia, China with different irrigation levels during 2018 and 2019 growing seasons. UAV RGB imagery was captured to investigate the effect of image sensors on thresholds obtained by the fixed-threshold method (proposed in our recent study), and to provide reference FVC (FVC UAV_R) for FVC models based on UAV multispectral imagery. Five vegetation indices (VIs), calculated from UAV multispectral imagery, and three regression algorithms (RF: random forest, ANN: artificial neural network, and MLR: multivariate linear regression) were used to build the FVC model suitable for different growing seasons, growth stages, and crop water stress. The results showed that there was a change in thresholds obtained using the fixed-threshold method based on different image sensors, but this change did not make a big difference on the accuracy of FVC UAV_R , with the R2 difference of 0.01 and the RMSE difference of 0.01. As for the three FVC regression models, RF model was the most suitable model when these models established in 2018 were used to estimate maize FVC in 2019 for different growth stages and water stress. The low estimation accuracy for high FVC levels was the reason why MLR model could not be used in the other maize growing season. This study provides a low cost and easy way to estimate maize FVC and its inter-field variability under various water status in different maize growing seasons or growth stages. [ABSTRACT FROM AUTHOR]

• Redundant information of the imagery was removed by the feature selection method. • U-Net algorithm is more suitable for crop mapping in the irrigation district. • The key early window period of major crop mapping was determined. • The crop mapping models with strong versatility in the early period were obtained. The information on spatial distribution pattern and area of different crops is particularly useful for monitoring and managing the sustainability of agricultural resources. However, there are still challenges in timely mapping of crop types and planting areas to support production during the present growing season. Here, we proposed to use time-series Sentinel-2 imagery and a deep learning method for major crop mapping (wheat, maize, sunflower, and squash) in the agricultural production areas in the Hetao Irrigation District (HID), Inner Mongolia Autonomous Region, China. A feature selection method that combines the global separability index and feature recursive elimination was proposed to optimize the band features of Sentinel-2 imagery. The Random Forest, Extreme Gradient Boosting, U-Net and deeplabv3+ algorithms were then used for image classification with all spectral bands and the selected band features, respectively. The selected bands and best method were used to determine the key time window for the early identification of four major crops. Integrate all image datasets in the early window period, and build mapping models suitable for images at different times in the window period by combining the selected bands and best method. The results showed that the proposed feature selection method effectively removed redundant features and improved the classification efficiency. The U-Net algorithm was more suitable for the classification of major crops in HID with a higher accuracy whether using all band features or the selected features. The key periods for early recognition of wheat, maize, sunflower, and squash in HID were mid-May, late June, early July, and late June, respectively. The constructed models for crop type mapping in the early period were tested in 2020 and 2021 in the HID, and the F1-score, the mean intersection-over-union and overall accuracy were 86.71 %, 84.66 %, and 98.89 %, respectively. The proposed feature selection method, U-Net classification algorithm, and screening of the early mapping window period in this study provide an efficient, accurate and timely crop mapping tool in HID. Future research can further verify the applicability of the early identification time window proposed in this study to support other agricultural management activities in the areas. [ABSTRACT FROM AUTHOR]

• Ground measurements of canopy structure and chlorophyll content were adopted. • The annual change of relationships between UAV VIs and Gs was explored. • Non-linear regression algorithms were adopted to relate UAV VIs to Gs. • Multiple linear regression approach performed well and was considered very robust. To further assess the sensitivity of crop chlorophyll and structure based on UAV vegetation indices (VIs) to maize water stress, a study was carried out in a maize field located in Inner Mongolia, China, with various levels of deficit irrigation over the entire 2018 and 2019 growing seasons. Ground measurements of stomatal conductance (Gs), leaf area index and leaf chlorophyll were used as references for maize water status, canopy structure and chlorophyll content, respectively. Four structure VIs and two chlorophyll VIs, and three regression algorithms (multiple linear, random forest and artificial neural networks regression) were adopted. The results showed that canopy structure derived from VIs had a significant correlation (p < 0.001) with Gs with the highest r value of 0.64 (n = 270) in 2018 and 2019. The transformed chlorophyll absorption in reflectance index, chlorophyll VI, could only estimate severe maize water stress with an r value of −0.47 (p < 0.001, n = 270) for the drier 2019. The water stress sensitivity of chlorophyll and structure VIs maybe significantly influenced by different responses of canopy structure and chlorophyll concentration to water stress, and the different spectral resolution of UAV multispectral cameras. Compared to non-linear machine learning regression algorithms, the multiple linear regression was robust enough to relate UAV-based multispectral VIs to Gs with coefficients of determination of 0.48 and 0.45 (n = 270) for 2018 and 2019, respectively. Although stable significant correlations were found between UAV multispectral VIs and Gs, annual changes in these specific expressions were also observed. Overall, our results demonstrated the potential of using structure VIs derived from UAV multispectral images and multiple linear regression approach to estimate maize water status in field scale. [ABSTRACT FROM AUTHOR]