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Net primary productivity (NPP) is an important index to evaluate the carbon absorption capacity in agricultural ecosystems, timely and accurate spatial–temporal variations of NPP play a significant role in guiding agricultural production. Currently, NPP observation at the canopy scale is primarily based on the chamber method. However, upscaling the spatial–temporal estimates of NPP at the canopy scale is still challenging. In this study, maize daytime NPP was measured by the chamber, and multispectral images of maize canopies were obtained via unmanned aerial vehicle (UAV) multispectral system. We explored the potential of multispectral images for estimating maize daytime NPP at the canopy scale. Four machine learning algorithms were employed to estimate daytime NPP using ground factors and vegetation indices (VIs) × photosynthetic active radiation (PAR) independently. NPP estimation models based on the gradient boosting regression (GBR), random forest (RF), and support vector regression (SVR) using ground factors outperformed the VIs × PAR-based model, barring the ridge regression (RR) model. Among them, the GBR-based models performed better, and the model using ground factors (R2 = 0.958) outperformed those using VIs × PAR (R2 = 0.899). However, the ground factor-based GBR model was complex and contained more input parameters, thus making it highly-time and labor-intensive, and highly destructive. Moreover, ground factor-based GBR model could not reflect the variation of NPP in maize canopy. The VIs × PAR-based GBR model could explain 89.9% of the daytime NPP, and it only required two parameters, including VIs and PAR. Therefore, the VIs × PAR-based GBR model can easily obtain high resolution and long time-series NPP information nondestructively for a large range. This study reveals that maize daytime NPP can be estimated using high-resolution UAV multispectral images and provides a good reference for promoting the monitoring and upscaling of NPP observations. [ABSTRACT FROM AUTHOR]

Different irrigation treatments in plants significantly influence CO 2 emissions. This study investigated the net ecosystem CO 2 exchange (NEE) and yield of maize cropland under different irrigation treatments to determine the optimal irrigation treatment at different maize growth stages. Different degrees of water deficit irrigation were carried out in four fields: full irrigation, 100% evapotranspiration (ET); moderate deficit irrigation, 65% ET; and severe deficit irrigation, 40% ET. Automated static and dynamic chambers were used to measure CO 2 fluxes. We found that NEE showed a "V" -shaped trend during the day and slowly declined at night. The light response parameters were calculated using the rectangular hyperbola model, and the leaf area index accounted for 85% of the light use efficiency. In the late vegetative stage, severe deficit irrigation led to a reduction in CO 2 emissions but significantly reduced the crop growth rate (p < 0.05). In the reproductive stage, full irrigation not only reduced CO 2 emissions but also significantly increased the crop growth rate. In the maturation stage, moderate deficit irrigation reduced CO 2 emissions, and the crop growth rate decreased compared with full irrigation, but the decrease was not significant (p > 0.05). Compared with full irrigation throughout the growth period, full irrigation in the reproductive stage and moderate deficit irrigation in the late vegetative and reproductive stages both increased the maize carbon sink capacity and light use efficiency at higher leaf area index, and the harvest index did not decrease significantly. However, inadequate irrigation in the reproductive stage or severe deficit irrigation in the late vegetative and maturation stages significantly reduced the maize harvest index. Thus, full irrigation during the reproductive stage, and moderate deficit irrigation during the late vegetative and reproductive stages were the optimal irrigation strategies for saving water, reducing CO 2 emissions and ensuring a satisfactory maize yield. • NEE in maize cropland under different irrigation treatments in semiarid Inner Mongolia. • Automated static and dynamic chambers were used to measure night and day maize NEE. • Full irrigation in reproductive stage can decrease CO 2 emission. • Moderate deficit irrigation in late vegetative and maturation stages reduced CO 2 emission. • Reasonable irrigation can reduce CO 2 emission and maintain high harvest index. [ABSTRACT FROM AUTHOR]

Rapid and accurate acquisition of crop coefficient (K c) values is essential for estimating field crop evapotranspiration (ET). The lack of rapid access to the high-resolution spatial and temporal distribution of K c values hinders obtaining a crop K c value for application in precision irrigation agriculture. This study aimed to explore the potential of leaf area index (LAI) and multispectral vegetation indices (VIs) obtained by an unmanned aerial vehicle (UAV) for estimating the K c value for a maize crop on a field scale and to obtain a high-resolution spatial-temporal map of K c values. Hence, the performance of the estimation model for daily maize K c derived by two machine learning algorithms (random forest regression-RFR and multiple linear regression-MLR) based on the ground-based LAI and six types of UAV-based multispectral VIs (normalized difference vegetation index, NDVI; soil adjusted vegetation index, SAVI; enhanced vegetation index, EVI; transformed chlorophyll absorption in reflectance index, TCARI; green normalized vegetation index, GNDVI; and visual atmospheric resistance index, VARI), was evaluated under multiple irrigation conditions during the entire cropping cycle. Maize RFR with VIs-LAI-based ET was compared to soil water balance (SWB) and FAO-56-based ET. The results showed that the RFR algorithm effectively (R2 = 0.65) estimated maize K c values based on ground-based LAI and UAV-based VIs. The UAV-based VIs based on Red-edge-Red and Green-Red spectral bands and ground-based LAI were suitable predictors in the K c prediction model under different irrigation conditions. Further, we successfully obtained a high resolution (pixel size of centimeter) spatial distribution of maize K c values based on EVI-based LAI and UAV-based VIs. Furthermore, the results indicated that the combination of UAV multispectral remote sensing technology and the RFR algorithm provides a potential solution for the distribution of water use and precision irrigation on a field scale. • The performance of a novel random forest regression model for estimating maize K c was evaluated. • The contributions of ground-based LAI and UAV-based VIs to the maize K c estimation model were tested. • A high-resolution (centimeter pixel) K c map on a field scale was generated. • The applicability of the maize K c estimation model to derive daily ET and cumulative ET was discussed. [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]

In this study, we develop a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. We found that leaf area index (LAI) inversion based on unmanned aerial vehicle (UAV) vegetation index has a high accuracy, with R2 and root mean square error (RMSE) values of 0.877 and 0.609, respectively. Maize yield estimation based on UAV remote sensing data and simple algorithm for yield (SAFY) crop model data assimilation has different yield estimation accuracy under different water treatments. This method can be used to estimate corn yield, where R2 is 0.855 and RMSE is 692.8kg/ha. Generally, the higher the water stress, the lower the estimation accuracy. Furthermore, we perform the yield estimate mapping at 2 m spatial resolution, which has a higher spatial resolution and accuracy than satellite remote sensing. The great potential of incorporating UAV observations with crop data to monitor crop yield, and improve agricultural management is therefore indicated. [ABSTRACT FROM AUTHOR]

The measurement of net ecosystem exchange (NEE) of field maize at a plot-sized scale is of great significance for assessing carbon emissions. Chamber methods remain the sole approach for measuring NEE at a plot-sized scale. However, traditional chamber methods are disadvantaged by their high labor intensity, significant resultant changes in microclimate, and significant impact on the physiology of crops. Therefore, an automated portable chamber with an air humidity control system to determinate the nighttime variation of NEE in field maize was developed. The chamber system can automatically open and close the chamber, and regularly collect gas in the chamber for laboratory analysis. Furthermore, a humidity control system was created to control the air humidity of the chamber. Chamber performance test results show that the maximum difference between the temperature and humidity outside and inside the chamber was 0.457 °C and 5.6%, respectively, during the NEE measuring period. Inside the chamber, the leaf temperature fluctuation range and the maximum relative change of the maize leaf respiration rate were − 0.3 to 0.3 °C and 23.2015%, respectively. We verified a series of measurements of NEE using the dynamic and static closed chamber methods. The results show a good common point between the two measurement methods (N = 10, R2 = 0.986; and mean difference: ΔCO2 = 0.079   μ mol   m − 2 s − 1 ). This automated chamber was found to be useful for reducing the labor requirement and improving the time resolution of NEE monitoring. In the future, the relationship between the humidity control system and chamber volume can be studied to control the microclimate change more accurately. [ABSTRACT FROM AUTHOR]

The rapid, accurate, and real-time estimation of crop coefficients at the farm scale is one of the key prerequisites in precision agricultural water management. This study aimed to map the maize crop coefficient (Kc) with improved accuracy under different levels of deficit irrigation. The proposed method for estimating the Kc is based on multispectral images of high spatial resolution taken using an unmanned aerial vehicle (UAV). The analysis was performed on five experimental plots using Kc values measured from the daily soil water balance in Ordos, Inner Mongolia, China. To accurately estimate the Kc, the fraction of vegetation cover (fc) derived from the normalized difference vegetation index (NDVI) was used to compare with field measurements, and the stress coefficients (Ks) calculated from two vegetation index (VI) regression models were compared. The results showed that the NDVI values under different levels of deficit irrigation had no significant difference in the reproductive stage but changed significantly in the maturation stage, with a decrease of 0.09 with 72% water applied difference. The fc calculated from the NDVI had a high correlation with field measurement data, with a coefficient of determination (R2) of 0.93. The ratios of transformed chlorophyll absorption in reflectance index (TCARI) to renormalized difference vegetation index (RDVI) and TCARI to soil-adjusted vegetation index (SAVI) were used, respectively, to establish two types of Ks regression models to retrieve Kc. Compared to the TCARI/SAVI model, the TCARI/RDVI model under different levels of deficit irrigation had better correlation with Kc, with R2 and root-mean-square error (RMSE) values ranging from 0.68 to 0.80 and from 0.140 to 0.232, respectively. Compared to Kc calculated from on-site measurements, the Kc values retrieved from the VI regression models established in this study had greater ability to assess the field variability of soil and crops. Overall, use of the UAV-measured multispectral vegetation index approach could improve water management at the farm scale. [ABSTRACT FROM AUTHOR]

Mapping maize water stress status and monitoring its spatial variability at a farm scale are a prerequisite for precision irrigation. High-resolution multispectral images acquired from an unmanned aerial vehicle (UAV) were used to evaluate the applicability of the data in mapping water stress status of maize under different levels of deficit irrigation at the late vegetative, reproductive and maturation growth stages. Canopy temperature, field air temperature and relative humidity obtained by a handheld infrared thermometer and a portable air temperature/relative humidity meter were used to establish a crop water stress index (CWSI) empirical model under the weather conditions in Ordos, Inner Mongolia, China. Nine vegetation indices (VIs) related to crop water stress were derived from the UAV multispectral imagery and used to establish CWSI inversion models. The results showed that non-water-stressed baseline had significant difference in the reproductive and maturation stages with an increase of 2.1 °C, however, the non-transpiring baseline did not change significantly with an increase of 0.1 °C. The ratio of transformed chlorophyll absorption in reflectance index (TCARI) and renormalized difference vegetation index (RDVI), and the TCARI and soil-adjusted vegetation index (SAVI) had the best correlations with CWSI. R2 values were 0.47 and 0.50 for TCARI/RDVI and TCARI/SAVI at the reproductive and maturation stages, respectively; and 0.81 and 0.80 for TCARI/RDVI and TCARI/SAVI at the late reproductive and maturation stages, respectively. Compared to CWSI calculated by on-site measurements, CWSI values retrieved by VI-CWSI regression models established in this study had more abilities to assess the field variability of crop and soil. This study demonstrates the potentiality of using high-resolution UAV multispectral imagery to map maize water stress. [ABSTRACT FROM AUTHOR]