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Soil salinization is an important factor that jeopardizes agricultural production and ecological environment. Rapid and accurate acquisition of soil salinity information in farmland is instructive for sustainable agricultural development and land resource management. In order to improve the accuracy of soil salinity prediction under vegetation cover conditions by satellite remote sensing, the eighth Agricultural Division of Xinjiang Production and Construction Corps was taken as the study area in this study. The soil surface (0-20 cm) samples were collected under high fractional vegetation cover conditions in July and August 2023, respectively, and synchronized satellite images were acquired. Sentinel-1, Sentinel-2 and environment variables provide 3 different types of explanatory variables. The dataset A (polarization indices, spectral indices), dataset B (polarization indices, environment variables), dataset C (spectral indices, environment variables), and dataset D (polarization indices, spectral indices, environment variables) were constructed separately from different combinations of Sentinel-1 radar information, Sentinel-2 multispectral information and environment variables. Then, three integrated machine learning algorithms, namely adaptive boosting (AdaBoost), gradient boost regression Tree (GBRT) and eXtreme gradient boosting tree (XGBoost), were applied to construct soil salinity inversion models based on different datasets. The results showed that Models constructed from dataset B (polarization indices and environmental variables) and C (spectral indices and environmental variables) achieved higher prediction accuracies compared to dataset A (polarization indices and spectral indices). It is shown that when environmental variables are involved in the prediction of soil salinity, the model effect is more effective than the model constructed by polarization and spectral indices suggesting that the model effects are more effective than those constructed from polarization and spectral indices. When environmental variables were applied to dataset D together with polarization indices and spectral indices, the prediction accuracy of all models constructed based on dataset D are generally higher than those constructed on dataset A, B, and C, and that the synergy of environmental variables with radar data and multispectral data can effectively improve the model accuracy. Radar information, spectral information and environmental variables are complementary in soil salinity prediction. Based on the correlation analysis, it can be seen that radar information, spectral information and environmental variables can be used as effective characteristic variables for soil salinity prediction in the study area. It was worth noting that the correlation between topographic factors and land surface temperature with soil salinity is relatively high, with the highest correlation between elevation and surface soil salinity (r = 0.52). Considering the spatial characteristics of soil salinity distribution in the study area can provide effective characteristic variables for soil salinity prediction under vegetation cover condition. In all datasets, the XGBoost had the best performance, followed by GBRT, and the AdaBoost had a large validation error. The D-XGBoost model having the highest accuracy with a validation set R² of 0.72, an RMSE of 2.40 g/kg, and an MAE of 1.29 g/kg. The integrated learning algorithms based on the combination of multiple source variables has a strong nonlinear fitting ability. XGBoost can better model the complex nonlinear relationship between soil salinity content and remote sensing information, environmental factors, and obtain ideal fitting results. The joint application of multi-source remote sensing data and integrated learning algorithms can obtain the ideal soil salinity inversion accuracy under vegetation cover conditions. This study provides an effective technical means for real-time dynamic monitoring of soil salinity by satellite remote sensing in farmland to optimize irrigation strategies and manage saline soils comprehensively in Xinjiang. [ABSTRACT FROM AUTHOR]

Water stress has been one of the most serious threat to the crop growth, development and yield quality in agricultural fields. Timely and accurate diagnosis of crop water stress can greatly contribute to the precision irrigation for the crop resilience and yield. In this study, the research object was taken from the summer maize in the typical dryland agricultural area of northwest China. A six-channel multispectral sensor was mounted on a drone to obtain the remote sensing image data of summer maize at the nodulatione and staminate pulling stage in 2022. At the same time, the stomatal conductance and phenotypic parameters of summer maize were also collected. The background was removed by supervised classification. The canopy vegetation index and image texture were obtained using the gray-scale covariance matrix. The sensitive vegetation index, image texture and phenotypic parameters and their combinations were screened out by the Bayesian information criterion and full subset filtering. The summer maize stomatal conductance estimation model was constructed to combine the three types of machine learnings: the extreme learning machine, the random forest, and the back-propagation neural network. The optimal model was mapped to estimate the stomatal conductance. The Pearson correlation coefficient of vegetation index and stomatal conductance were significantly positively correlated, whereas, the canopy reflectance of summer maize was weakly negatively correlated. Different types of image textures at different wavelengths were correlated with the stomatal conductance, and the highest correlation was found in the 550 nm band. The Pearson correlation coefficients between morphological structure phenotypes (plant height, stem thickness and leaf area) and stomatal conductance of summer maize were 0.72, 0.58 and 0.69, respectively, where the three types of phenotypic parameters data were correlated well with stomatal conductance. Vegetation indices with spectral reflectance data were used to assess the overall health and moisture status of the vegetation. Image texture was used to capture the spatial distribution, texture and structural features of crops. Crop phenotypic parameters were then used to reflect the physiological and morphological responses of the crop in a three-dimensional manner, providing visual information about the growth and moisture of the vegetation. The decision coefficients of the crop water stress diagnostic models that constructed from the three information sources increased from 0.728 and 0.750 to 0.841, respectively, compared with the single or two combinations, indicating the great potential to stomatal conductance prediction. The optimal combination of indicators was screened by Bayesian information criterion and full subset screening: DWSI, NDVI, MEA, ENT, plant height and leaf area. The back-propagation neural network model with the three complementary information sources was the optimal model for the water stress diagnosis of summer maize (coefficient of determination of 0.841, root mean square error of 0.043 mol/(m2 ·s), and mean absolute error of 0.034 mol/(m² ·s)). The underestimation of stomatal conductance was significantly improved, compared with the rest models. The inverse map with the optimal model was widely applied to easily and accurately diagnose the crop water stress for the purpose of irrigation strategies and resource allocation. The finding can provide a feasible and accurate diagnosis of water stress in summer maize. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Thermal infrared imaging can be expected to rapidly and cost-saving monitor the soil moisture content in the largescale farmland. But it is still unclear on the impact of lighting conditions on thermal infrared images. This study aims to explore the impact of direct sunlight or shadow occlusion of the crop canopy and soil on the UAV thermal infrared remote sensing, in order to diagnose the crop water stress and monitor soil moisture content. Summer corn with different irrigation treatments was taken as the research object. The thermal infrared images were divided into four parts: illuminated canopy, shaded canopy, illuminated soil, and shaded soil. The light temperature was extracted from the higher temperature, whereas, the shadow temperature was assumed as the lower temperature. Temperature extraction was used to calculate the 11:00, 13:00, and 15:00 canopy temperature difference (difference between canopy temperature and atmospheric temperature, ΔT), crop water stress index (CWSI), and evaporative fraction (ratio of latent heat flux to effective energy, EF). A comparison was made on three changes in the monitoring effect of the index on soil moisture content after using light temperature (ΔTL, CWSIL, EFL) and shadow temperature (ΔTS, CWSIS, EFS ) at different times. The results show that: 1) There was the variation in the monitoring effects of the three indices over time. The EF monitoring effect was better at 11:00 and 15:00, the crop water stress index monitoring effect was better at 13:00. There was the less change in the ΔT monitoring over time. The temperature index should be selected for monitoring soil moisture content using field conditions and the time of flying the drone; 2) The monitoring effect was improved the most at 11:00 in the jointing period, after distinguishing light temperature and shadow temperature. The R² of EF, EFS, and EFL were 0.54, 0.65, and 0.78, respectively. The R² of CWSI, CWSIS, and CWSIL were 0.47, 0.64, and 0.70, respectively. There was no significant light temperature in the period of tasseling and filling, whereas, the index monitoring effect was significantly reduced using shadow temperature. There was the largest decrease in the CWSIS at 13:00, compared with the CWSI, where the R² decreases were 0.11 and 0.06, respectively. Therefore, it was very necessary to choose the clear and cloudless weather and avoid cloudy days, when shooting thermal infrared images. Furthermore, the impact of lighting conditions on thermal infrared images was also change with the growth period; 3) The best monitoring soil moisture content was obtained using EFL at 11:00 in the jointing and tasseling period, and CWSI at 13:00 in the filling period, where the R² of predicting soil moisture content were 0.75, 0.75, and 0.89, respectively. In addition, the soil moisture content was also dominated the monitoring effect. When the soil volumetric moisture content was around 0.23, both EFL and CWSI shared the more accurate prediction. Different time points, light temperature and shade temperature were utilized to analyze the monitoring effect of three temperature indexes on soil moisture content. The better monitoring time and index were determined in the three growth periods of summer corn. The finding can provide a strong reference for the UAV thermal infrared monitoring of soil moisture content. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soil water and salt content have been two of the most important influencing factors on crop growth and agricultural productivity all over the world nowadays. Inappropriate soil water and content can pose a potential threat to crop quality and yield. Fortunately, satellite remote sensing can be expected to monitor the soil salt content and water content, due to the largescale coverage, time- and labor-saving. Among them, the optical satellite has been the major data source for the estimation of the soil water and salt content, particularly with the high temporal return cycle, high spatial resolution, and rich spectral range. However, the soil water and salt can affect each other on the response to the spectral reflectance in the areas with their large variations, similar to their regular change in optical satellite band reflectance. There is a high demand to improve the inversion accuracy of soil water and salt content when the soil salt or water content cannot be maintained consistently. This study aims to improve the estimation accuracy of soil salt and water, and then expand the application scope of optical satellite remote sensing. A semi-analytical reflectance model-RVS model was proposed to simulate the response of vegetation spectral reflectance to the soil water and salt content in the vegetation soil. The influence of soil salt-water interaction was also considered when estimating the soil water and salt content. The RVS model was then used to simultaneously estimate the soil water and salt content in the vegetation-covered soil. The results show that the RVS model performed the better estimation accuracy in the study area in both soil water content (coefficient of determination: R 2 =0.63-0.74, root mean square error: RMSE=0.017-0.028) and soil salt content (coefficient of determination: R ² =0.68-0.75, root mean square error: RMSE=0.0525- 0.0617). There was a more outstanding response of vegetation spectral reflectance to the soil water and salt content in the deep soil during the whole stage of crop growth, compared with the shallow soil. The dominant influencing factors on the vegetation spectral reflectance were slowly shifting from the soil water content to the soil salt content and soil water-salt interactions, as the crop grew. The RVS model was then used to continuously estimate the soil water and salt content in the period of crop growth and irrigation. As such, the salt accumulation was attributed to the insufficient water that was caused by both crop growth depletion and excessive irrigation. The estimation revealed the interference of soil water content, soil salt content, and water-salt interaction on the crop spectral reflectance, considering the influence of soil water (soil salt) when estimating soil salt content (soil water content) under the concept of soil salt-water interaction. The simultaneous monitoring of soil water content and soil salt content was realized to construct the estimation models using Sentinel-2 and machine learning. The finding can provide a strong reference for the accurate monitoring of soil salt content and soil water content on the regional scale. [ABSTRACT FROM AUTHOR]

Canopy temperature is one of the most important parameters to calculate the crop water stress index（CWSI）.Among them,the unmanned aerial vehicles（UAVs） with thermal infrared remote sensing function have been widely used to extract the crop canopy temperature in recent years.The monitoring accuracy of crop water can be improved to accurately remove the soil background in the thermal infrared images.In this study,an improved method was proposed to monitor the CWSI using UVA remote sensing.The research object was firstly selected as the summer maize at the jointing stage.Four water treatments were then carried out:T1（50% of the field capacity）,T2（65% of the field capacity）,T3（80% of the field capacity）,and T4（95%-100% of the field capacity）.The spatial distribution of surface maize canopy temperature in the study area was extracted by the red green ratio index（RGRI） with the visible and thermal infrared images from UAVs.Specifically,the UAV images were selected as 5 days in 2020 and 7 days in 2021.The cumulative frequency was then analyzed for the canopy temperature on each thermal infrared image.Soil water content data at a depth of 40cm was also collected at the same time.Subsequently,two methods were proposed to improve the performance of CWSI.The accuracy was evaluated using the correlation between the CWSI and crop physiological indexes（Stomatal conductance,Gs;Net photosynthetic rate,Pn;and Transpiration rate,Tr） with the soil moisture content.Method 1,the canopy temperature was segmented using different statistical quantiles in the normal distribution.Some unreasonable values were then eliminated for the low and high temperature in the histogram of canopy temperature frequency.The commonly-used statistical quantiles were 93.32%,84.13%,69.15%,61.79%,50%,38.21%,30.85%,and15.87%,respectively.The intervals 0.62% and 2.28% were used to eliminate the high temperature values on the right side of the frequency histogram,and then the CWSI(denoted as CWSITcF%) was calculated from the average canopy temperature over different statistical quantiles.The statistical quantiles with the best correlation with the crop physiological indexes were selected as the most sensitive canopy temperature interval.In Method 2,maize canopy data was divided into four parts:Interval Ⅰ,Tc ＜40,Var＜10;Interval Ⅱ,Tc ＜40,10＜Var＜20;Interval Ⅲ,35＜Tc ＜45,Var＞20;and Interval Ⅳ,40＜Tc＜50,0＜Var＜20,according to the variance of canopy temperature.The most sensitive statistical quantile on the respective interval was then selected to calculate the CWSI（denoted as CWSIn）The results showed that:1) The coefficients R² of CWSIn and crop physiological indexes（Gs,Pn,Tr） calculated from 2020 and 2021 were 0.72,0.52 and 0.62,and normalized root mean square error（nRMSE） were 23.96%,24.06% and 25.60%.The accuracy of model fitting was higher than that of original CWSI（R2 were 0.73,0.34,0.46,nRMSE were 23.69%,28.27%,30.21%）,but little different from CWSITcF%（R² were 0.74,0.54,0.61,nRMSE were 22.87%,23.74% and 25.61%2) The most sensitive canopy temperature range varied greatly between years（2020,61.17%;2021,49.38%;and Two-year data,83.51%）,although the CWSITcF% was provided a higher accuracy of crop water stress.There was also some influence on the application of CWSITcF% in practice.The CWSIn presented a higher stability for the crop physiological indexes（The nRMSE between physiological indicators were 16.60%,27.37% and 28.49% in 2020 and 21.60%,18.95%,22.64% in2021）Therefore,the improved CWSIn can be expected to more accurately monitor the crop water stress under UAVs remote sensing,compared with the original. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A canopy-air temperature difference has been one of the most commonly used indicators to monitor soil moisture content. Among them, the time delay effect between canopy and atmospheric temperature can be critical to improving the monitoring accuracy of soil moisture content. Taking the summer maize at the filling stage (Mid-August to early September) as the research object, this study aims to clarify the time delay effect of canopy temperature on the soil moisture content under three water treatments, including I1 (85%-100% of field capacity), I2 (70%-85% of field capacity), and I3 (50%-65% of field capacity). Three high-precision infrared temperature sensors (Apogee SI-411) were also selected to monitor the canopy temperature under three water treatments. An automatic weather station (AWS-CR1000) was used to record the data of the environmental factors, such as the net surface radiation (W/m²), atmospheric temperature (℃), and air relative humidity (%) of the test site. The soil moisture content was measured at the depths of 0-10, 0-20, 0-30, 0-40, and 0-60 cm on August 24, 27, 31, September 3, and 6, 2020 under different water treatments. The interval of data recording was all set at 10 min. Firstly, the diurnal variation of canopy temperature and environmental factors was fitted by Gaussian function, and then the peak time of the fitting curve was determined to calculate the peak time difference as the lag time between canopy temperature and environmental factors. Secondly, a multiple linear regression was selected to determine the main influencing factors of canopy temperature. Finally, the time lag effect on the soil moisture content was analyzed using the lag time between canopy temperature and the main influencing factors. The results showed that: 1) There was a significant difference in the peak of canopy temperature under different water treatments, where the I3 water treatment presented the highest peak value, followed by I2, and I1 water treatment presented the lowest. 2) The peak time of canopy temperature of I1, I2, and I3 water treatment lagged behind the net surface radiation by about 70, 70, and 100 min, respectively, and ahead of the atmospheric temperature and relative humidity by about 60, 60, and 30 min, respectively. 3) The main influencing factor of canopy temperature was the atmospheric temperature, followed by the net surface radiation, and finally the relative humidity. 4) The mean values of canopy-air temperature difference from 11:00 to 13:00 with or without time lag were calculated to consider the correlation between canopy-air temperature difference and soil moisture content at different depths. It was found that the correlation with the time lag was generally higher than that without time lag, where the determination coefficient R² values with the time lag were 0.684, 0.699, 0.726, 0.615, and 0.516, respectively, the R² values without the time lag were 0.710, 0.698, 0.713, 0.584, and 0.474, respectively. The R² values increased by 0.14%, 1.82%, 5.31%, and 8.86% in 0-20, 0-30, 0-40, and 0-60 cm depth, respectively. On the whole, the monitoring accuracy of soil moisture content was improved to consider the time lag between canopy and atmospheric temperature. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) with thermal infrared remote sensing have been used to rapidly extract the temperature of the crop canopy, further to monitor the water stress condition of the crop. The removal of soil background from the thermal infrared images can be an effective way to improve the monitoring accuracy of crop moisture. But there is also a great challenge on the thermal infrared image processing. In this study, an accurate and rapid temperature extraction was implemented to monitor the water stress using the UAVs with remote sensing images. The summer maize at the jointing stage was selected as the research object. There were also four completely irrigation treatments with three replicates and a total of 12 experimental plots. Specifically, the four water treatments were: T1 (50% of the field capacity), T2 (65% of the field capacity), T3 (80% of the field capacity), and T4 (95%-100% of the field capacity). The thermal infrared and visible images were captured by the UAVs on July 26, July 27, July 29, July 31, and August 2, 2020. The data of canopy temperature and soil water content at 20cm depth were also collected at the same time. After that, the Otsu algorithm, EXG-Kmeans, and Otsu-EXG-Kmeans were selected to determine the canopy area in the images. The accuracy of the extraction was evaluated from four aspects, including the schematics of canopy area, classification accuracy, temperature histogram, and correlation with the measured canopy temperature. An optimal combination of parameters was achieved to extract the thermal infrared images of canopy temperature. Subsequently, a crop water stress index (CWSI) was also achieved during this time. The relationship between CWSI and soil water content was then established to monitor the water deficit of maize, according to the daily average trend of CWSI. The results showed that the Otsu-EXG-Kmeans presented a higher extraction accuracy for the canopy temperature (User's accuracy: 95.9%>83.2%>66.6%), and the extracted canopy temperature was closer to the measured temperature (r:0.788>0.762>0.750>0.737), indicating that the Otsu-EXG-Kmeans was an effective way to accurately extract the canopy temperature. The CWSI presented a higher correlation with the soil water content (r:-0.738<-0.666), compared with the canopy temperature. The daily average variation trend of CWSI was more consistent with the actual situation. Consequently, the improved extraction can be widely expected to extract the canopy temperature, thereby accurately monitoring the water shortage situation of maize. This finding can provide a strong reference for the UAVs to accurately monitor the crop water stress under remote sensing. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rapid acquisition of soil water content (SWC) in field crop root zone is significant for drought supervision and precision irrigation. The UAV multispectral remote sensing system has the advantages of obtaining high spatial-temporal resolution of crop phenotype data, and has a wide application prospect in soil moisture monitoring. In order to obtain SWC accurately and timely at a farm scale, in this paper, the field maize with different water treatments is taken as the research object, and the multispectral remote sensing monitoring of summer maize is carried out by using the UAV remote sensing platform, and the soil water content of different soil depth in maize root zone is collected synchronously. Based on the UAV multispectral remote sensing image data sets of jointing stage, tasseling-silking stage and milky-maturity stage of summer maize in 2018, the soil background is removed by support vector machine classification, the spectral reflection of maize canopy is extracted, and the 10 vegetation indices are calculated, then the sensitivity analysis of soil water content in different depth is carried out by using full subset screening method for different wave bands and vegetation indices, and the soil water content in different depth is analyzed respectively, ridge regression and extreme learning machine are used to construct quantitative estimation models of soil water content at 0-20, 20-45 and 45-60 cm soil depth after full subset selection. The test area is located in Zhaojun Town, Dalate Banner, Ordos, Inner Mongolia, China(40°26'0.29" N, 109°36'25.99" E, elevation 1 010 m). The sowing time of maize is on May 11, 2018, the emergence time is on May 18, and the harvest time is on September 8, 2018. The total growth period is 114 days. The UAV multispectral remote sensing images and ground data collection dates are July 8, July 12, July 17, July 21, July 26, August 2, August 28 and September 7, 2018. It is collected once a day and tested 8 times in the whole growth period. July 8-21 is the jointing stage, July 26-August 2 is the tasseling-silking stage, August 28-september 7 is the milk-maturity stage. The flight altitude of the UAV is 70 m, and the flight time is 11:00-13:00 local time (11:44-13:44 Beijing time). Firstly, the UAV multispectral canopy images of field maize with 5 different irrigation treatments (TRTs) are acquired through the six-rotor UAV equipped with a RedEdge multispectral camera ( MicaSense, USA), and the multispectral images of diffuse reflector (reflectivity 58%, size 3×3 m) are collected at the same height to perform radiometric correction in the meantime, and then the spectral reflectances of the field maize are acquired. Secondly, the support vector machine (SVM) is used to eliminate the multispectral image of soil background in ENVI and ArcGIS software, then the maize canopy spectral reflectance is extracted and 10 vegetation indices (VIs), such as Normalized Difference Vegetation Index (NDVI), Normalized Green Difference Vegetation Index (GNDVI) and Transformed Chlorophyll Absorption In Reflectance Index(TCARI), etc, are calculated. Finally, the full subset selection method based on Bayesian Information Criterion (BIC) is used to analyze the sensitivity of SWC at different depths for different spectra bands and vegetation indices in R3.5.1 software, and then Ridge Regression (RR) as well as Extreme Learning Machine (ELM) are used to construct a quantitative estimation model of SWC at soil depths of 0-20, 20-45 and 45-60 cm at different growth stages, respectively. The results show that the full subset selection method based on BIC can effectively select the optimal spectral subset, and the selected variables generally pass the significance test and the independent variables number is small; the effects of the ELM model outperformed the RR model almost under all the same conditions; the optimal monitoring soil depth of maize at jointing stage, tasseling-silking stage is 0-20 cm, and the optimal monitoring soil depth of milk-maturity stage is 20-45 cm; the ELM inversion model at 20-45 cm soil depth at milk-maturity stage has the best effect, the decision coefficients of modeling set and verification set are 0.825 and 0.750, respectively, the root mean square error are 1.00% and 1.32%, respectively, and the normalized root mean square error are 10.85% and 13.55%, respectively. The combination of full subset selection method and machine learning can improve the inversion accuracy and robustness of SWC. This study provides a new way for rapid and accurate monitoring of SWC in farmland and precise irrigation. [ABSTRACT FROM AUTHOR]

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The canopy temperature can indicate crop water stress condition, but the traditional method of canopy temperature measurement is based on ground point measurement, time-consuming and laborious. The calculation of crop water stress index (CWSI) requires more ground data monitoring and the crop water deficit physiological indicators are also difficult to measure in specific practical application, so it is difficult to popularize widely in agriculture. With the rapid development of unmanned aerial vehicle (UAV) platforms and thermal infrared sensors, the thermal infrared technology of UAV can monitor crop canopy temperature quickly and effectively and obtain a large area of crop canopy temperature with fast, dynamic monitoring, all-weather operation advantages, and further diagnose the water stress condition of agricultural crops. To solve the problem of low precision of the current UAV thermal infrared remote sensing for the diagnosis of crop water stress, in this paper, the 4 water treatment plots I1, I2, I3 and I4 with 50%, 65%, 80% and 95%-100% of field holding water as the upper limit were set up, each treatment plot had 3 repeated tests, a total of 12 treatment plots, and the cotton at flower boll period under 4 kinds of water treatments was selected as the test object. A six-rotor unmanned aerial vehicle was used to carry the thermal infrared sensor, and the high resolution thermal infrared images of cotton canopy at 13 o’clock at noon were collected in 5 consecutive days. First, the custom coordinates for thermal infrared images are defined in order to make the image have the same operating position, and Canny edge detection algorithm is used to obtain the edge feature raster image of cotton canopy. Then, the edge feature raster images are processed in ArcGIS and ENVI (environment for visualizing images) software to obtain a polygon vector layer, including the cotton canopy edge feature raster image converting to polyline vector layers and the polyline vector layers converting to polygon vector layers. Finally, use the polygon vector layer to clip, mask statistics and draw the canopy temperature histogram. Through the Canny edge detection algorithm, the thermal infrared image of the soil background is effectively removed, and the application of canopy temperature histogram verifies the elimination effect. The number, position and size of the original temperature histogram’s peaks correspond to different matter pixels. If the temperature histogram is a single peak histogram for the high coverage crop canopy, the crop canopy with low coverage will be characterized by double or triple peaks. According to the canopy temperature histogram, the temperature characteristics of cotton canopy are calculated, including standard deviation of canopy temperature (CTSD) and canopy temperature coefficient of variation (CTCV). The relationships between cotton canopy temperature characteristics and cotton leaf stomatal conductance (Gs), transpiration rate (Tr), crop water stress index (CWSI) and soil water content (SWC) were studied, and the applicability of canopy temperature characteristics to diagnose cotton water stress was analyzed. The results showed that the cotton canopy temperature characteristics were correlated with the physiological and physical indices representing cotton water stress, and the maximum determining coefficient (R2) was 0.88. The determining coefficients of CTSD and CTCV with Gs, Tr, CWSI and SWC were 0.884, 0.625, 0.673, 0.550 and 0.853, 0.583, 0.620, 0.520, respectively. The CTSD was more sensitive to crop water stress condition, which could be used as a new index to diagnose crop water stress. However, the correlation between CTCV and physiological and physical indicators of crop water deficit is not ideal. Compared with CWSI, Gs, Tr and SWC which are traditional diagnostic indicators of crop water stress, this method greatly simplifies the calculation of crop diagnostic indicators. In this research, it suggests that the calculation method of canopy temperature characteristics only needs the thermal infrared image data of UAV and does not require any meteorological factors, so the canopy temperature characteristics are more easily promoted for use in agricultural water-saving irrigation. It has great potential to be used in the diagnosis of crop water stress compared with other water stress indices. [ABSTRACT FROM AUTHOR]

Organic matter content in soil is one of the most significant indicators evaluating the soil fertility, and its dynamic monitoring is good for further development of accurate agriculture. In recent years, obtaining Vis-NIR (visible - near infrared) continuous spectrum data of soil through hyperspectral technique and realizing accurate inversion prediction according to organic matter spectrum reflection characteristics have become a hot topic in current remote sensing field. However, in the hyperspectral inversion process of desert soil organic matter, there exists the problem of “low organic matter content, weak spectrum response and low model precision”. The research collected different soil samples in Seder Boker region, south of Israel, divided the experimental soil samples into sandy soil and clay loam after particle size analysis in the lab, and applied potassium dichromate external heating method to measure the organic matter content in the soil. The raw hyperspectral reflectance of soil samples was measured by the ASD FieldSpec 3 instrument. After data preprocessing and different mathematical manipulation, 6 spectral indicators were obtained, i.e. reflectivity (REF), inverse-log reflectance (LR), continuum removal reflectance (CR), standard normal variable reflectance (SNV), first-order differential reflectance (FDR) and second-order differential reflectance (SDR). Then, gray correlation degree (GCD) between different spectral indicators and organic matter content was calculated, and SNV, FDR and SDR through gray correlation (GC) test (GCD>0.90) were chosen as the sensitive spectral indicators. Moreover, hyperspectral inversion model of soil organic matter was built based on sensitive spectral indicator using partial least squares regression (PLSR) method and ridge regression (RR) method, and the precision of inversion result was verified and compared. And then, the performances of these models were evaluated by the determination coefficient for calibration set (Rc 2), determination coefficient for prediction set (Rp 2), root mean squared error (RMSE) and relative percent deviation (RPD). The results indicated that: Soil particle size has a certain impact on the spectral response of organic matter, and the inversion effect of hyperspectral model on the organic matter content in sandy soil is superior to clay loam; after comparing and analyzing the models built according to different spectral indicators, Rc 2, Rp 2 and RPD of SNV-PLSR soil model and SNV-RR soil model built according to SNV are the highest and RMSE is the lowest, so SNV is the optimal spectral inversion indicator of soil organic matter; SNV-RR model has the most ideal inversion effect on organic matter content of these 2 kinds of soil: For sandy soil, Rc 2 is 0.887, Rp 2 is 0.866, RMSE is 0.610 g/kg and RPD is 2.72; for clay loam, Rc 2 is 0.889, Rp 2 is 0.863, RMSE is 0.898g/kg and RPD is 2.37. After analysis, it is known that SNV-RR model has extremely strong forecast ability for organic matter of sandy soil, and very good quantitative forecast ability for organic matter of clay loam. In addition, compared with PLSR model, Rc 2 and Rp 2 of RR model decline slightly. However, on the premise of ensuring precision, the number of band section used in modeling only accounts for about 4% of total spectrum. Not only does it simplify the model greatly, but also realizes “dimensionality reduction” and “optimization” of hyperspectral data. Through band selection function effect of RR method, the significant band section of soil organic matter is analyzed: The sensitive band of organic matter of sandy soil is mainly concentrated at 820-860 and 940-970 nm, but the sensitive band of organic matter of clay loam is concentrated at 730-790 and 800-820 nm. The united application of gray correlation analysis and RR method in the modeling analysis of soil organic matter provides a new approach to optimize the hyperspectral model and quickly measure the organic matter content in soil. GC-SNV-RR organic matter inversion model of 2 kinds of soil is simple and has good prediction. It provides support for remote sensing analysis on desert soil organic matter, which realizes the speedability and accuracy in monitoring the desert soil organic matter. [ABSTRACT FROM AUTHOR]