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农业生态环境-灌溉Agro-ecosystem & Environment—Irrigation

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For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails Select Improving model performance in mapping cropland soil organic matter using time-series remote sensing data Xianglin Zhang, Jie Xue, Songchao Chen, Zhiqing Zhuo, Zheng Wang, Xueyao Chen, Yi Xiao, Zhou Shi 2024, 23 (8): 2820-2841.   DOI: 10.1016/j.jia.2024.01.015 Abstract （111）      PDF in ScienceDirect       Faced with increasing global soil degradation, spatially explicit data on cropland soil organic matter (SOM) provides crucial data for soil carbon pool accounting, cropland quality assessment and the formulation of effective management policies.  As a spatial information prediction technique, digital soil mapping (DSM) has been widely used to spatially map soil information at different scales.  However, the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.  To overcome this limitation, this study systematically assessed a framework of “information extraction-feature selection-model averaging” for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou, China in 2021.  The results showed that using the framework of dynamic information extraction, feature selection and model averaging could efficiently improve the accuracy of the final predictions (R2: 0.48 to 0.53) without having obviously negative impacts on uncertainty.  Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM, which improved the R2 of random forest from 0.44 to 0.48 and the R2 of extreme gradient boosting from 0.37 to 0.43.  Forward recursive feature selection (FRFS) is recommended when there are relatively few environmental covariates (<200), whereas Boruta is recommended when there are many environmental covariates (>500).  The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.  When the structures of initial prediction models are similar, increasing in the number of averaging models did not have significantly positive effects on the final predictions.  Given the advantages of these selected strategies over information extraction, feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales, so this approach can provide more reliable references for soil conservation policy-making. Reference | Related Articles | Metrics Select Comparison of CWSI and Ts-Ta-VIs in moisture monitoring of dryland crops (sorghum and maize) based on UAV remote sensing Hui Chen, Hongxing Chen, Song Zhang, Shengxi Chen, Fulang Cen, Quanzhi Zhao, Xiaoyun Huang, Tengbing He, Zhenran Gao 2024, 23 (7): 2458-2475.   DOI: 10.1016/j.jia.2024.03.042 Abstract （89）      PDF in ScienceDirect       Monitoring agricultural drought using remote sensing data is crucial for precision irrigation in modern agriculture.  Utilizing unmanned aerial vehicle (UAV) remote sensing, we explored the applicability of an empirical crop water stress index (CWSI) based on canopy temperature and three-dimensional drought indices (TDDI) constructed from surface temperature (Ts), air temperature (Ta) and five vegetation indices (VIs) for monitoring the moisture status of dryland crops.  Three machine learning algorithms (random forest regression (RFR), support vector regression, and partial least squares regression) were used to compare the performance of the drought indices for vegetation moisture content (VMC) estimation in sorghum and maize.  The main results of the study were as follows: (1) Comparative analysis of the drought indices revealed that Ts-Ta-normalized difference vegetation index (TDDIn) and Ts-Ta-enhanced vegetation index (TDDIe) were more strongly correlated with VMC compared with the other indices.  The indices exhibited varying sensitivities to VMC under different irrigation regimes; the strongest correlation observed was for the TDDIe index with maize under the fully irrigated treatment (r=−0.93). (2) Regarding spatial and temporal characteristics, the TDDIn, TDDIe and CWSI indices showed minimal differences.  Over the experimental period, with coefficients of variation were 0.25, 0.18 and 0.24, respectively. All three indices were capable of effectively characterizing the moisture distribution in dryland maize and sorghum crops, but the TDDI indices more accurately monitored the spatial distribution of crop moisture after a rainfall or irrigation event.  (3) For prediction of the moisture content of single crops, RFR models based on TDDIn and TDDIe estimated VMC most accurately (R2>0.7), and the TDDIn-based model predicted VMC with the highest accuracy when considering multiple-crop samples, with R2 and RMSE of 0.62 and 14.26%, respectively. Thus, TDDI proved more effective than the CWSI in estimating crop water content. Reference | Related Articles | Metrics Select Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales Jie Song, Dongsheng Yu, Siwei Wang, Yanhe Zhao, Xin Wang, Lixia Ma, Jiangang Li 2024, 23 (4): 1393-1408.   DOI: 10.1016/j.jia.2023.09.017 Abstract （109）      PDF in ScienceDirect       Rapid and accurate acquisition of soil organic matter (SOM) information in cultivated land is important for sustainable agricultural development and carbon balance management.  This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.  We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine (GEE) platform, and reflectance bands and vegetation indices were extracted from these composite images.  Then the random forest (RF), support vector machine (SVM) and gradient boosting regression tree (GBRT) models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.  Results showed that firstly, all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM (P<0.05) for the months of January, March, April, October, and November.  Secondly, in terms of single-monthly composite variables, the prediction accuracy was relatively poor, with the highest R2 value of 0.36 being observed in January.  When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year, the first quarter and the fourth quarter showed good performance, and any combination of three quarters was similar in estimation accuracy.  The overall best performance was observed when all monthly synthetic variables were incorporated into the models.  Thirdly, among the three models compared, the RF model was consistently more accurate than the SVM and GBRT models, achieving an R2 value of 0.56.  Except for band 12 in December, the importance of the remaining bands did not exhibit significant differences.  This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images. Reference | Related Articles | Metrics Select Improved simulation of winter wheat yield in North China Plain by using PRYM-Wheat integrated dry matter distribution coefficient Xuan Li, Shaowen Wang, Yifan Chen, Danwen Zhang, Shanshan Yang, Jingwen Wang, Jiahua Zhang, Yun Bai, Sha Zhang 2024, 23 (4): 1381-1392.   DOI: 10.1016/j.jia.2023.09.030 Abstract （117）      PDF in ScienceDirect       The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in China.  Presently, most remote sensing process models use the “biomass×harvest index (HI)” method to simulate regional-scale winter wheat yield.  However, spatiotemporal differences in HI contribute to inaccuracies in yield simulation at the regional scale.  Time-series dry matter partition coefficients (Fr) can dynamically reflect the dry matter partition of winter wheat.  In this study, Fr equations were fitted for each organ of winter wheat using site-scale data.  These equations were then coupled into a process-based and remote sensing-driven crop yield model for wheat (PRYM-Wheat) to improve the regional simulation of winter wheat yield over the North China Plain (NCP).  The improved PRYM-Wheat model integrated with the fitted Fr equations (PRYM-Wheat-Fr) was validated using data obtained from provincial yearbooks.  A 3-year (2000–2002) averaged validation showed that PRYM-Wheat-Fr had a higher coefficient of determination (R²=0.55) and lower root mean square error (RMSE=0.94 t ha–1) than PRYM-Wheat with a stable HI (abbreviated as PRYM-Wheat-HI), which had R² and RMSE values of 0.30 and 1.62 t ha–1, respectively.  The PRYM-Wheat-Fr model also performed better than PRYM-Wheat-HI for simulating yield in verification years (2013–2015).  In conclusion, the PRYM-Wheat-Fr model exhibited a better accuracy than the original PRYM-Wheat model, making it a useful tool for the simulation of regional winter wheat yield. Reference | Related Articles | Metrics Select Combining field data and modeling to better understand maize growth response to phosphorus (P) fertilizer application and soil P dynamics in calcareous soils Weina Zhang, Zhigan Zhao, Di He, Junhe Liu, Haigang Li, Enli Wang 2024, 23 (3): 1006-1021.   DOI: 10.1016/j.jia.2023.07.034 Abstract （168）      PDF in ScienceDirect       We used field experimental data to evaluate the ability of the agricultural production system model (APSIM) to simulate soil P availability, maize biomass and grain yield in response to P fertilizer applications on a fluvo-aquic soil in the North China Plain.  Crop and soil data from a 2-year experiment with three P fertilizer application rates (0, 75 and 300 kg P2O5 ha–1) were used to calibrate the model.  Sensitivity analysis was carried out to investigate the influence of APSIM SoilP parameters on the simulated P availability in soil and maize growth.  Crop and soil P parameters were then derived by matching or relating the simulation results to observed crop biomass, yield, P uptake and Olsen-P in soil.  The re-parameterized model was further validated against 2 years of independent data at the same sites.  The re-parameterized model enabled good simulation of the maize leaf area index (LAI), biomass, grain yield, P uptake, and grain P content in response to different levels of P additions against both the calibration and validation datasets.  Our results showed that APSIM needs to be re-parameterized for simulation of maize LAI dynamics through modification of leaf size curve and a reduction in the rate of leaf senescence for modern stay-green maize cultivars in China.  The P concentration limits (maximum and minimum P concentrations in organs) at different stages also need to be adjusted.  Our results further showed a curvilinear relationship between the measured Olsen-P concentration and simulated labile P content, which could facilitate the initialization of APSIM P pools in the NCP with Olsen-P measurements in future studies.  It remains difficult to parameterize the APSIM SoilP module due to the conceptual nature of the pools and simplified conceptualization of key P transformation processes.  A fundamental understanding still needs to be developed for modelling and predicting the fate of applied P fertilizers in soils with contrasting physical and chemical characteristics. Reference | Related Articles | Metrics Select A hybrid CNN-LSTM model for diagnosing rice nutrient levels at the rice panicle initiation stage Fubing Liao, Xiangqian Feng, Ziqiu Li, Danying Wang, Chunmei Xu, Guang Chu, Hengyu Ma, Qing Yao, Song Chen 2024, 23 (2): 711-723.   DOI: 10.1016/j.jia.2023.05.032 Abstract （178）      PDF in ScienceDirect       Nitrogen (N) and potassium (K) are two key mineral nutrient elements involved in rice growth.  Accurate diagnosis of N and K status is very important for the rational application of fertilizers at a specific rice growth stage.  Therefore, we propose a hybrid model for diagnosing rice nutrient levels at the early panicle initiation stage (EPIS), which combines a convolutional neural network (CNN) with an attention mechanism and a long short-term memory network (LSTM).  The model was validated on a large set of sequential images collected by an unmanned aerial vehicle (UAV) from rice canopies at different growth stages during a two-year experiment.  Compared with VGG16, AlexNet, GoogleNet, DenseNet, and inceptionV3, ResNet101 combined with LSTM obtained the highest average accuracy of 83.81% on the dataset of Huanghuazhan (HHZ, an indica cultivar).  When tested on the datasets of HHZ and Xiushui 134 (XS134, a japonica rice variety) in 2021, the ResNet101-LSTM model enhanced with the squeeze-and-excitation (SE) block achieved the highest accuracies of 85.38 and 88.38%, respectively.  Through the cross-dataset method, the average accuracies on the HHZ and XS134 datasets tested in 2022 were 81.25 and 82.50%, respectively, showing a good generalization.  Our proposed model works with the dynamic information of different rice growth stages and can efficiently diagnose different rice nutrient status levels at EPIS, which are helpful for making practical decisions regarding rational fertilization treatments at the panicle initiation stage. Reference | Related Articles | Metrics Select Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China Jie Xue, Xianglin Zhang, Songchao Chen, Bifeng Hu, Nan Wang, Zhou Shi 2024, 23 (1): 283-297.   DOI: 10.1016/j.jia.2023.06.005 Abstract （236）      PDF in ScienceDirect       Various land use and land cover (LULC) products have been produced over the past decade with the development of remote sensing technology.  Despite the differences in LULC classification schemes, there is a lack of research on assessing the accuracy of their application to croplands in a unified framework.  Thus, this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products (i.e., MCD12Q1 V6, GlobCover2009, FROM-GLC and GlobeLand30) based on the harmonised FAO criterion, and quantified the relationships between four factors (i.e., slope, elevation, field size and crop system) and cropland classification agreement.  The validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency, with overall accuracies of 94.90 and 93.52%, respectively.  The FROM-GLC showed the worst performance, with an overall accuracy of 83.17%.  Overlaying the cropland generated by the four global LULC products, we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification, respectively.  High consistency was mainly observed in the Northeast China Plain, the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain, China.  In contrast, low consistency was detected primarily on the eastern edge of the northern and semiarid region, the Yunnan-Guizhou Plateau and southern China.  Field size was the most important factor for mapping cropland.  For area accuracy, compared with China Statistical Yearbook data at the provincial scale, the accuracies of different products in descending order were: GlobeLand30, FROM-GLC, MCD12Q1, and GlobCover2009.  The cropland classification schemes mainly caused large area deviations among the four products, and they also resulted in the different ranks of spatial accuracy and area accuracy among the four products.  Our results can provide valuable suggestions for selecting cropland products at the national or provincial scale and help cropland mapping and reconstruction, which is essential for food security and crop management, so they can also contribute to achieving the Sustainable Development Goals issued by the United Nations. Reference | Related Articles | Metrics Select Spatio-temporal variations in trends of vegetation and drought changes in relation to climate variability from 1982 to 2019 based on remote sensing data from East Asia Shahzad ALI, Abdul BASIT, Muhammad UMAIR, Tyan Alice MAKANDA, Fahim Ullah KHAN, Siqi SHI, NI Jian 2023, 22 (10): 3193-3208.   DOI: 10.1016/j.jia.2023.04.028 Abstract （116）      PDF in ScienceDirect       Studying the significant impacts on vegetation of drought due to global warming is crucial in order to understand its dynamics and interrelationships with temperature, rainfall, and normalized difference vegetation index (NDVI).  These factors are linked to excesses drought frequency and severity on the regional scale, and their effect on vegetation remains an important topic for climate change study.  East Asia is very sensitive and susceptible to climate change.  In this study, we examined the effect of drought on the seasonal variations of vegetation in relation to climate variability and determined which growing seasons are most vulnerable to drought risk; and then explored the spatio-temporal evolution of the trend in drought changes in East Asia from 1982 to 2019.  The data were studied using a series of several drought indexes, and the data were then classified using a heat map, box and whisker plot analysis, and principal component analysis.  The various drought indexes from January to August improved rapidly, except for vegetation health index (VHI) and temperature condition index (TCI).  While these indices were constant in September, they increased again in October, but in December, they showed a descending trend.  The seasonal and monthly analysis of the drought indexes and the heat map confirmed that the East Asian region suffered from extreme droughts in 1984, 1993, 2007, and 2012 among the study years.  The distribution of the trend in drought changes indicated that more severe drought occurred in the northwestern region than in the southeastern area of East Asia.  The drought tendency slope was used to describe the changes in drought events during 1982–2019 in the study region.  The correlations among monthly precipitation anomaly percentage (NAP), NDVI, TCI, vegetation condition index (VCI), temperature vegetation drought index (TVDI), and VHI indicated considerably positive correlations, while considerably negative correlations were found among the three pairs of NDVI and VHI, TVDI and VHI, and NDVI and TCI.  This ecological and climatic mechanism provides a good basis for the assessment of vegetation and drought-change variations within the East Asian region.  This study is a step forward in monitoring the seasonal variation of vegetation and variations in drought dynamics within the East Asian region, which will serve and contribute to the better management of vegetation, disaster risk, and drought in the East Asian region. Reference | Related Articles | Metrics Select Integrating a novel irrigation approximation method with a process-based remote sensing model to estimate multi-years' winter wheat yield over the North China Plain ZHANG Sha, YANG Shan-shan, WANG Jing-wen, WU Xi-fang, Malak HENCHIRI, Tehseen JAVED, ZHANG Jia-hua, BAI Yun 2023, 22 (9): 2865-2881.   DOI: 10.1016/j.jia.2023.02.036 Abstract （179）      PDF in ScienceDirect       Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.  However, using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.  Thus, we proposed a new approach to approximating irrigations of winter wheat over the North China Plain (NCP), where irrigation occurs extensively during the winter wheat growing season.  This approach used irrigation pattern parameters (IPPs) to define the irrigation frequency and timing.  Then, they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat (PRYM–Wheat), to improve the regional estimates of winter wheat over the NCP.  The IPPs were determined using statistical yield data of reference years (2010–2015) over the NCP.  Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield, with an increase and decrease in the correlation coefficient (R) and root mean square error (RMSE) of 0.15 (about 37%) and 0.90 t ha–1 (about 41%), respectively.  The data in validation years (2001–2009 and 2016–2019) were used to validate PRYM–Wheat.  In addition, our findings also showed R (RMSE) of 0.80 (0.62 t ha–1) on a site level, 0.61 (0.91 t ha–1) for Hebei Province on a county level, 0.73 (0.97 t ha–1) for Henan Province on a county level, and 0.55 (0.75 t ha–1) for Shandong Province on a city level.  Overall, PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years, providing a scientific basis for ensuring regional food security. Reference | Related Articles | Metrics Select Nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images PEI Sheng-zhao, ZENG Hua-liang, DAI Yu-long, BAI Wen-qiang, FAN Jun-liang 2023, 22 (8): 2536-2552.   DOI: 10.1016/j.jia.2023.02.027 Abstract （266）      PDF in ScienceDirect       Remote sensing has been increasingly used for precision nitrogen management to assess the plant nitrogen status in a spatial and real-time manner. The nitrogen nutrition index (NNI) can quantitatively describe the nitrogen status of crops. Nevertheless, the NNI diagnosis for cotton with unmanned aerial vehicle (UAV) multispectral images has not been evaluated yet. This study aimed to evaluate the performance of three machine learning models, i.e., support vector machine (SVM), back propagation neural network (BPNN), and extreme gradient boosting (XGB) for predicting canopy nitrogen weight and NNI of cotton over the whole growing season from UAV images. The results indicated that the models performed better when the top 15 vegetation indices were used as input variables based on their correlation ranking with nitrogen weight and NNI. The XGB model performed the best among the three models in predicting nitrogen weight. The prediction accuracy of nitrogen weight at the upper half-leaf level (R2=0.89, RMSE=0.68 g m–2, RE=14.62% for calibration and R2=0.83, RMSE=1.08 g m–2, RE=19.71% for validation) was much better than that at the all-leaf level (R2=0.73, RMSE=2.20 g m–2, RE=26.70% for calibration and R2=0.70, RMSE=2.48 g m–2, RE=31.49% for validation) and at the plant level (R2=0.66, RMSE=4.46 g m–2, RE=30.96% for calibration and R2=0.63, RMSE=3.69 g m–2, RE=24.81% for validation). Similarly, the XGB model (R2=0.65, RMSE=0.09, RE=8.59% for calibration and R2=0.63, RMSE=0.09, RE=8.87% for validation) also outperformed the SVM model (R2=0.62, RMSE=0.10, RE=7.92% for calibration and R2=0.60, RMSE=0.09, RE=8.03% for validation) and BPNN model (R2=0.64, RMSE=0.09, RE=9.24% for calibration and R2=0.62, RMSE=0.09, RE=8.38% for validation) in predicting NNI. The NNI predictive map generated from the optimal XGB model can intuitively diagnose the spatial distribution and dynamics of nitrogen nutrition in cotton fields, which can help farmers implement precise cotton nitrogen management in a timely and accurate manner Reference | Related Articles | Metrics Select A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery LIAO Zhen-qi, DAI Yu-long, WANG Han, Quirine M. KETTERINGS, LU Jun-sheng, ZHANG Fu-cang, LI Zhi-jun, FAN Jun-liang 2023, 22 (7): 2248-2270.   DOI: 10.1016/j.jia.2023.02.022 Abstract （184）      PDF in ScienceDirect       The accurate and rapid estimation of canopy nitrogen content (CNC) in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture. However, the determination of CNC from field sampling data for leaf area index (LAI), canopy photosynthetic pigments (CPP; including chlorophyll a, chlorophyll b and carotenoids) and leaf nitrogen concentration (LNC) can be time-consuming and costly. Here we evaluated the use of high-precision unmanned aerial vehicle (UAV) multispectral imagery for estimating the LAI, CPP and CNC of winter wheat over the whole growth period. A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. Six machine learning methods, i.e., multiple stepwise regression (MSR), support vector regression (SVR), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were compared for the retrieval of winter wheat LAI, CPP and CNC values, and a double-layer model was proposed for estimating CNC based on LAI and CPP. The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. The proposed double-layer models (R2=0.67–0.89, RMSE=13.63–23.71 mg g–1, MAE=10.75–17.59 mg g–1) performed better than the direct inversion models (R2=0.61– 0.80, RMSE=18.01–25.12 mg g–1, MAE=12.96–18.88 mg g–1) in estimating winter wheat CNC. The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs (R2=0.89, RMSE=13.63 mg g–1, MAE=10.75 mg g–1). The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field. Reference | Related Articles | Metrics Select Combining the critical nitrogen concentration and machine learning algorithms to estimate nitrogen deficiency in rice from UAV hyperspectral data YU Feng-hua, BAI Ju-chi, JIN Zhong-yu, GUO Zhong-hui, YANG Jia-xin, CHEN Chun-ling 2023, 22 (4): 1216-1229.   DOI: 10.1016/j.jia.2022.12.007 Abstract （220）      PDF in ScienceDirect       Rapid and large area acquisition of nitrogen (N) deficiency status is important for achieving the optimal fertilization of rice.  Most existing studies, however, focus on the use of unmanned aerial vehicle (UAV) remote sensing to diagnose N nutrition in rice, while there are fewer studies on the quantitative description of the degree of N deficiency in rice, and the effects of the critical N concentration on the spectral changes in rice have rarely been explored.  Therefore, based on the canopy spectral data obtained by remotely-sensed UAV hyperspectral images, the N content in rice was obtained through field sampling.  The construction method of the rice curve for the northeastern critical N concentration was studied, and on this basis, N deficiency was determined.  Taking the spectrum of the critical N concentration state as the standard spectrum, the spectral reflectivity data were transformed by the ratios and differences, and the feature extraction of the spectral data was carried out by the successive projections algorithm (SPA).  Finally, by taking the characteristic band as the input variable and N deficiency as the output variable, a set of multivariate linear regression (MLR), long short-term memory (LSTM) inversion models based on extreme learning machine (ELM), and the non-dominated sorting genetic algorithm III extreme learning machine (NSGA-III-ELM) were constructed.  The results showed two key aspects of this system: 1) The correlation between the N deficiency data and original spectrum was poor, but the correlation between the N deficiency data and N deficiency could be improved by a difference change and ratio transformation; 2) The inversion results based on the ratio spectrum and NSGA-III-ELM algorithm were the best, as the R2 values of the training set and validation set were 0.852 and 0.810, and the root mean square error (RMSE) values were 0.291 and 0.308, respectively.  From the perspective of the spectral data, the inversion accuracy of the ratio spectrum was better than the accuracy of the original spectrum or difference spectrum.  At the algorithm level, the model inversion results based on LSTM algorithms showed a serious overfitting phenomenon and poor inversion effect.  The inversion accuracy based on the NSGA-III-ELM algorithm was better than the accuracy of the MLR algorithm or the ELM algorithm.  Therefore, the inversion model based on the ratio spectrum and NSGA-III-ELM algorithm could effectively invert the N deficiency in rice and provide critical technical support for accurate topdressing based on the N status in the rice. Reference | Related Articles | Metrics Select Effects of land use/cover change (LUCC) on the spatiotemporal variability of precipitation and temperature in the Songnen Plain, China CHU Xiao-lei, LU Zhong, WEI Dan, LEI Guo-ping 2022, 21 (1): 235-248.   DOI: 10.1016/S2095-3119(20)63495-5 Abstract （185）      PDF in ScienceDirect       Understanding the effects of land use/cover change (LUCC) on regional climate is critical for achieving land use system sustainability and global climate change mitigation.  However, the quantitative analysis of the contribution of LUCC to the changes of climatic factors, such as precipitation & temperature (P&T), is lacking.  In this study, we combined statistical methods and the gravity center model simulation to quantify the effects of long-term LUCC on P&T in the Songnen Plain (SNP) of Northeast China from 1980–2018.  The results showed the spatiotemporal variability of LUCC. For example, paddy field had the largest increase (15 166.43 km2) in the SNP, followed by dry land, while wetland had the largest decrease (19 977.13 km2) due to the excessive agricultural utilization and development.  Annual average precipitation decreased at a rate of –9.89 mm per decade, and the warming trends were statistically significant with an increasing rate of 0.256°C per decade in this region since 1980.  The model simulation revealed that paddy field, forestland, and wetland had positive effects on precipitation, which caused their gravity centers to migrate towards the same direction accompanied by the center of precipitation gravity, while different responses were seen for building land, dry land and unused land.  These results indicated that forestland had the largest influence on the increase of precipitation compared with the other land use types.  The responses in promoting the temperature increase differed significantly, being the highest in building land, and the lowest in forestland.  In general, the analysis of regional-scale LUCC showed a significant reduction of wetland, and the increases in building land and cropland contributed to a continuous drying and rapid warming in the SNP. Reference | Related Articles | Metrics Select The water-saving potential of using micro-sprinkling irrigation for winter wheat production on the North China Plain ZHAI Li-chao, Lü Li-hua, DONG Zhi-qiang, ZHANG Li-hua, ZHANG Jing-ting, JIA Xiu-ling, ZHANG Zheng-bin 2021, 20 (6): 1687-1700.   DOI: 10.1016/S2095-3119(20)63326-3 Abstract （175）      PDF in ScienceDirect       The shortage of groundwater resources is a considerable challenge for winter wheat production on the North China Plain.  Water-saving technologies and procedures are thus urgently required.  To determine the water-saving potential of using micro-sprinkling irrigation (MSI) for winter wheat production, field experiments were conducted from 2012 to 2015.  Compared to traditional flooding irrigation (TFI), micro-sprinkling thrice with 90 mm water (MSI1) and micro-sprinkling four times with 120 mm water (MSI2) increased the water use efficiency by 22.5 and 16.2%, respectively, while reducing evapotranspiration by 17.6 and 10.8%.  Regardless of the rainfall pattern, MSI (i.e., MSI1 or MSI2) either stabilized or significantly increased the grain yield, while reducing irrigation water volumes by 20–40%, compared to TFI.  Applying the same volumes of irrigation water, MSI (i.e., MSI3, micro-sprinkling five times with 150 mm water) increased the grain yield and water use efficiency of winter wheat by 4.6 and 11.7%, respectively, compared to TFI.  Because MSI could supply irrigation water more frequently in smaller amounts each time, it reduced soil layer compaction, and may have also resulted in a soil water deficit that promoted the spread of roots into the deep soil layer, which is beneficial to photosynthetic production in the critical period.  In conclusion, MSI1 or MSI2 either stabilized or significantly increased grain yield while reducing irrigation water volumes by 20–40% compared to TFI, and should provide water-saving technological support in winter wheat production for smallholders on the North China Plain. Reference | Related Articles | Metrics Select Optimization of water and nitrogen management for surge-root irrigated apple trees in the Loess Plateau of China DAI Zhi-guang, FEI Liang-jun, ZENG Jian, HUANG De-liang, LIU Teng 2021, 20 (1): 260-273.   DOI: 10.1016/S2095-3119(20)63283-X Abstract （141）      PDF in ScienceDirect       The Loess Plateau is one of the main regions for growing apple trees in China, but a shortage of water resources and low utilization of nitrogen have restricted its agricultural development.  A 2-year field experiment was conducted which included three levels of soil water content (SWC), 90–75%, 75–60%, and 60–45% of field capacity, and five levels of nitrogen application (Napp), 0.7, 0.6, 0.5, 0.4 and 0.3 kg/plant.  The treatments were arranged in a strip-plot design with complete randomized blocks with three replications.  For both years, the water and Napp had significant (P<0.05) effects on leaf area index (LAI), yield, water use efficiency (WUE) and nitrogen partial factor productivity (NPFP) while the interaction effect of water and Napp on yield, WUE and NPFP was significant (P<0.05) in 2018, and not in 2017.  For the same SWC level, WUE first increased, then decreased as Napp increased, while NPFP tended to decrease, but the trend of LAI with different Napp was closely related to SWC.  At the same Napp, the LAI increased as SWC increased, while the WUE and NPFP first increased, then decreased, but the yield showed different trends as the SWC increased.  The dualistic and quadric regression equations of water and Napp indicate that the yield, WUE and NPFP cannot reach the maximum at the same time.  Considering the coupling effects of water and Napp on yield, WUE and NPFP in 2017 and 2018, the SWC level shall be controlled in 75–60% of field capacity and the Napp is 0.45 kg/plant, which can be as the suitable strategy of water and Napp management for the maximum comprehensive benefits of yield, WUE and NPFP for apple trees in the Loess Plateau and other regions with similar environments.    Reference | Related Articles | Metrics Select Effects of water application uniformity using a center pivot on winter wheat yield, water and nitrogen use efficiency in the North China Plain CAI Dong-yu, YAN Hai-jun, LI Lian-hao 2020, 19 (9): 2326-2339.   DOI: 10.1016/S2095-3119(19)62877-7 Abstract （136）      PDF in ScienceDirect       In recent years, the use of fertigation technology with center pivot irrigation systems has increased rapidly in the North China Plain (NCP).  The combined effects of water and nitrogen application uniformity on the grain yield, water use efficiency (WUE) and nitrogen use efficiency (NUE) have become a research hotspot.  In this study, a two-year field experiment was conducted during the winter wheat growing season in 2016–2018 to evaluate the water application uniformity of a center pivot with two low pressure sprinklers (the R3000 sprinklers were installed in the first span, the corresponding treatment was RS; the D3000 sprinklers were installed in the second span, the corresponding treatment was DS) and a P85A impact sprinkler as the end gun (the corresponding treatment was EG), and to analyze its effects on grain yield, WUE and NUE.  The results showed that the water application uniformity coefficients of R3000, D3000 and P85A along the radial direction of the pivot (CUH) were 87.5, 79.5 and 65%, respectively.  While the uniformity coefficients along the traveling direction of the pivot (CUC) were all higher than 85%.  The effects of water application uniformity of the R3000 and D3000 sprinklers on grain yield were not significant (P>0.05); however, the average grain yield of EG was significantly lower (P<0.05) than those of RS and DS, by 9.4 and 11.1% during two growing seasons, respectively.  The coefficients of variation (CV) of the grain yield had a negative correlation with the uniformity coefficient.  The CV of WUE was more strongly affected by the water application uniformity, compared with the WUE value, among the three treatments.  The NUE of RS was higher than those of DS and EG by about 6.1 and 4.8%, respectively, but there were no significant differences in NUE among the three treatments during the two growing seasons.  Although the CUH of the D3000 sprinklers was lower than that of the R3000, it had only limited effects on the grain yield, WUE and NUE.  However, the cost of D3000 sprinklers is lower than that of R3000 sprinklers.  Therefore, the D3000 sprinklers are recommended for winter wheat irrigation and fertigation in the NCP.  Reference | Related Articles | Metrics Select Canopy morphological changes and water use efficiency in winter wheat under different irrigation treatments ZHAO Hong-xiang, ZHANG Ping, WANG Yuan-yuan, NING Tang-yuan, XU Cai-long, WANG Pu 2020, 19 (4): 1105-1116.   DOI: 10.1016/S2095-3119(19)62750-4 Abstract （182）      PDF in ScienceDirect       Water is a key limiting factor in agriculture.  Water resource shortages have become a serious threat to global food security.  The development of water-saving irrigation techniques based on crop requirements is an important strategy to resolve water scarcity in arid and semi-arid regions.  In this study, field experiments with winter wheat were performed at Wuqiao Experiment Station, China Agricultural University in two growing seasons in 2013–2015 to help develop such techniques.  Three irrigation treatments were tested: no-irrigation (i.e., no water applied after sowing), limited-irrigation (i.e., 60 mm of water applied at jointing), and sufficient-irrigation (i.e., a total of 180 mm of water applied with 60 mm at turning green, jointing and anthesis stages, respectively).  Leaf area index (LAI), light transmittance (LT), leaf angle (LA), transpiration rate (Tr), specific leaf weight, water use efficiency (WUE), and grain yield of winter wheat were measured.  The highest WUE of wheat in the irrigated treatments was found under limited-irrigation and grain yield was only reduced by a small amount in this treatment compared to the sufficient irrigation treatment.  The LAI and LA of wheat plants was lower under limited irrigation than sufficient irrigation, but canopy LT was greater.  Moreover, the specific leaf weight of winter wheat was significantly lower under sufficient than limited irrigation conditions, while the leaf Tr was significantly higher.  Correlation analysis showed that the increased LAI was associated with an increase in the leaf Tr, but the specific leaf weight had the opposite relationship with transpiration.  Optimum WUE occurred over a reasonable range in leaf Tr.  In conclusion, reduced irrigation can optimize wheat canopies and regulate water consumption, with only small reductions in final yield, ultimately leading to higher wheat WUE and water saving in arid and semi-arid regions. 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