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The accurate estimation and timely diagnosis of crop nitrogen (N) status can facilitate in-season fertilizer management. In order to evaluate the performance of three leaf and canopy optical sensors in non-destructively diagnosing winter wheat N status, three experiments using seven wheat cultivars and multi-N-treatments (0–360 kg N ha−1) were conducted in the Jiangsu province of China from 2015 to 2018. Two leaf sensors (SPAD 502, Dualex 4 Scientific+) and one canopy sensor (RapidSCAN CS-45) were used to obtain leaf and canopy spectral data, respectively, during the main growth period. Five N indicators (leaf N concentration (LNC), leaf N accumulation (LNA), plant N concentration (PNC), plant N accumulation (PNA), and N nutrition index (NNI)) were measured synchronously. The relationships between the six sensor-based indices (leaf level: SPAD, Chl, Flav, NBI, canopy level: NDRE, NDVI) and five N parameters were established at each growth stages. The results showed that the Dualex-based NBI performed relatively well among four leaf-sensor indices, while NDRE of RS sensor achieved a best performance due to larger sampling area of canopy sensor for five N indicators estimation across different growth stages. The areal agreement of the NNI diagnosis models ranged from 0.54 to 0.71 for SPAD, 0.66 to 0.84 for NBI, and 0.72 to 0.86 for NDRE, and the kappa coefficient ranged from 0.30 to 0.52 for SPAD, 0.42 to 0.72 for NBI, and 0.53 to 0.75 for NDRE across all growth stages. Overall, these results reveal the potential of sensor-based diagnosis models for the rapid and non-destructive diagnosis of N status.
Jie Jiang; Cuicun Wang; Hui Wang; Zhaopeng Fu; Qiang Cao; Yongchao Tian; Yan Zhu; Weixing Cao; Xiaojun Liu. Evaluation of Three Portable Optical Sensors for Non-Destructive Diagnosis of Nitrogen Status in Winter Wheat. Sensors 2021, 21, 5579 .
AMA StyleJie Jiang, Cuicun Wang, Hui Wang, Zhaopeng Fu, Qiang Cao, Yongchao Tian, Yan Zhu, Weixing Cao, Xiaojun Liu. Evaluation of Three Portable Optical Sensors for Non-Destructive Diagnosis of Nitrogen Status in Winter Wheat. Sensors. 2021; 21 (16):5579.
Chicago/Turabian StyleJie Jiang; Cuicun Wang; Hui Wang; Zhaopeng Fu; Qiang Cao; Yongchao Tian; Yan Zhu; Weixing Cao; Xiaojun Liu. 2021. "Evaluation of Three Portable Optical Sensors for Non-Destructive Diagnosis of Nitrogen Status in Winter Wheat." Sensors 21, no. 16: 5579.
Deep-learning-based object detection algorithms have significantly improved the performance of wheat spike detection. However, UAV images crowned with small-sized, highly dense, and overlapping spikes cause the accuracy to decrease for detection. This paper proposes an improved YOLOv5 (You Look Only Once)-based method to detect wheat spikes accurately in UAV images and solve spike error detection and miss detection caused by occlusion conditions. The proposed method introduces data cleaning and data augmentation to improve the generalization ability of the detection network. The network is rebuilt by adding a microscale detection layer, setting prior anchor boxes, and adapting the confidence loss function of the detection layer based on the IoU (Intersection over Union). These refinements improve the feature extraction for small-sized wheat spikes and lead to better detection accuracy. With the confidence weights, the detection boxes in multiresolution images are fused to increase the accuracy under occlusion conditions. The result shows that the proposed method is better than the existing object detection algorithms, such as Faster RCNN, Single Shot MultiBox Detector (SSD), RetinaNet, and standard YOLOv5. The average accuracy (AP) of wheat spike detection in UAV images is 94.1%, which is 10.8% higher than the standard YOLOv5. Thus, the proposed method is a practical way to handle the spike detection in complex field scenarios and provide technical references for field-level wheat phenotype monitoring.
Jianqing Zhao; Xiaohu Zhang; Jiawei Yan; Xiaolei Qiu; Xia Yao; Yongchao Tian; Yan Zhu; Weixing Cao. A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5. Remote Sensing 2021, 13, 3095 .
AMA StyleJianqing Zhao, Xiaohu Zhang, Jiawei Yan, Xiaolei Qiu, Xia Yao, Yongchao Tian, Yan Zhu, Weixing Cao. A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5. Remote Sensing. 2021; 13 (16):3095.
Chicago/Turabian StyleJianqing Zhao; Xiaohu Zhang; Jiawei Yan; Xiaolei Qiu; Xia Yao; Yongchao Tian; Yan Zhu; Weixing Cao. 2021. "A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5." Remote Sensing 13, no. 16: 3095.
There is unprecedented increase in low-temperature stress (LTS) during post-heading stages in rice as a consequence of the recent climate changes. Quantifying the effect of LTS on yields is key to unraveling the impact of climatic changes on crop production, and therefore developing corresponding mitigation strategies. The present research was conducted to analyze and quantify the effect of post-heading LTS on rice yields as well as yield and grain filling related parameters. A two-year experiment was conducted during rice growing season of 2018 and 2019 using two Japonica cultivars (Huaidao 5 and Nanjing 46) with different low-temperature sensitivities, at four daily minimum/maximum temperature regimes of 21/27 °C (T1), 17/23 °C (T2), 13/19 °C (T3) and 9/15 °C (T4). These temperature treatments were performed for 3 (D1), 6 (D2) or 9 days (D3), at both flowering and grain filling stages. We found LTS for 3 days had no significant effect on grain yield, even when the daily mean temperature was as low as 12 °C. However, LTS of between 6 and 9 days at flowering but not at filling stage significantly reduced grain yield of both cultivars. Comparatively, Huaidao 5 was more cold tolerant than Nanjing 46. LTS at flowering and grain filling stages significantly reduced both maximum and mean grain filling rates. Moreover, LTS prolonged the grain filling duration of both cultivars. Additionally, there was a strong correlation between yield loss and spikelet fertility, spikelet weight at maturity, grain filling duration as well as mean and maximum grain filling rates under post-heading LTS (p< 0.001). Moreover, the effect of post-heading LTS on rice yield can be well quantified by integrating the canopy temperature (CT) based accumulated cold degree days (ACDDCT) with the response surface model. The findings of this research are useful in modeling rice productivity under LTS and for predicting rice productivity under future climates.
Iftikhar Ali; Liang Tang; Junjie Dai; Min Kang; Aqib Mahmood; Wei Wang; Bing Liu; Leilei Liu; Weixing Cao; Yan Zhu. Responses of Grain Yield and Yield Related Parameters to Post-Heading Low-Temperature Stress in Japonica Rice. Plants 2021, 10, 1425 .
AMA StyleIftikhar Ali, Liang Tang, Junjie Dai, Min Kang, Aqib Mahmood, Wei Wang, Bing Liu, Leilei Liu, Weixing Cao, Yan Zhu. Responses of Grain Yield and Yield Related Parameters to Post-Heading Low-Temperature Stress in Japonica Rice. Plants. 2021; 10 (7):1425.
Chicago/Turabian StyleIftikhar Ali; Liang Tang; Junjie Dai; Min Kang; Aqib Mahmood; Wei Wang; Bing Liu; Leilei Liu; Weixing Cao; Yan Zhu. 2021. "Responses of Grain Yield and Yield Related Parameters to Post-Heading Low-Temperature Stress in Japonica Rice." Plants 10, no. 7: 1425.
This work explored potassium nutrient retrieval in wheat blades using reflectance spectra. Spectral data were collected from wheat blades at different growth stages, in different cultivars, and following different fertilisation treatments from 2016 to 2019 using a leaf clip and halogen bulb with an ASD spectrometer. Reflectance data from 350 to 2500 nm were collected, and data of 400 to 2400 nm were used in the retrieval. Using a leaf clip to measure the reflectance of a narrow blade can cause bias, which can be corrected using a normalisation method, i.e. the reflectance of each band was divided by the average reflectance of all bands. Three such methods were employed: vegetation index (VI), partial least squares (PLS), and random forest (RF). The approach yielded leaf potassium content (LKC, %) and leaf potassium per area (LKA, g/m2). The results showed that newly developed VIs outperformed previously published indices. The model using a modified ratio spectral index, mRSI(2275, 1875), yielded LKC with a coefficient of determination (R2) of 0.61 and a root mean square error (RMSE) of 0.57%. Normalisation methods can eliminate multiplicative error in blade spectra, thereby correcting the underestimated reflectance of narrow blades, and improving the accuracy of potassium retrieval models. Among the three methods, PLS achieved the highest accuracy. The retrieval of LKC and LKA based on normalised spectra and the PLS method yielded R2 values of 0.74 and 0.65, respectively, and their corresponding RMSE values were 0.46% and 0.21 g/m2. LKC retrieval models had higher R2 values than LKA models. This comprehensive analysis of different methods revealed the importance of reflectance at 1883 nm and 2305 nm. In conclusion, it is feasible to retrieve wheat leaf potassium levels using spectral data.
Tiancheng Yang; Jingshan Lu; Feng Liao; Hao Qi; Xia Yao; Tao Cheng; Yan Zhu; Weixing Cao; Yongchao Tian. Retrieving potassium levels in wheat blades using normalised spectra. International Journal of Applied Earth Observation and Geoinformation 2021, 102, 102412 .
AMA StyleTiancheng Yang, Jingshan Lu, Feng Liao, Hao Qi, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao, Yongchao Tian. Retrieving potassium levels in wheat blades using normalised spectra. International Journal of Applied Earth Observation and Geoinformation. 2021; 102 ():102412.
Chicago/Turabian StyleTiancheng Yang; Jingshan Lu; Feng Liao; Hao Qi; Xia Yao; Tao Cheng; Yan Zhu; Weixing Cao; Yongchao Tian. 2021. "Retrieving potassium levels in wheat blades using normalised spectra." International Journal of Applied Earth Observation and Geoinformation 102, no. : 102412.
Accurate and timely monitoring of leaf nitrogen concentration (LNC) in rice is crucial to optimize nitrogen fertilizer management and reduce environmental pollution. Existing vegetation indices (VIs) often perform well for high canopy cover conditions, but their performance becomes poor at early growth stages due to the significant exposure of background materials and the induced spectral mixing effect. This study proposed a novel approach to estimate the LNC at early and middle growth stages of paddy rice by using abundance adjusted vegetation indices (AAVIs) from unmanned aerial vehicle (UAV) multispectral imagery. An AAVI was constructed by combining the traditional VI and the rice abundant from linear spectral mixture analysis (LSMA) of UAV imagery. Subsequently, the performance of AAVIs was evaluated in comparison with traditional VIs derived from all pixels or green pixels for individual growth stages or multiple stages. The results demonstrated that AAVIs exhibited better performance in LNC estimation, regardless of individual stages or across the entire early season. Specially, AACIred-edge showed the best performance among the AAVIs evaluated for LNC estimation. For universal modeling across early stages, the combination of AACIred-edge and AAEVI yielded the highest accuracy (R2=0.78, RMSE=0.26%, rRMSE=10.4%) performed remarkably better than the traditional VIs from all pixels or green pixels (R2<0.40). These findings illustrated that the AAVIs have great potential in monitoring nitrogen status at early growth stages with high-resolution aerial or satellite images in the context of precision crop management.
Wenhui Wang; Yapeng Wu; Qiaofeng Zhang; Hengbiao Zheng; Xia Yao; Yan Zhu; Weixing Cao; Tao Cheng. AAVI: A Novel Approach to Estimating Leaf Nitrogen Concentration in Rice From Unmanned Aerial Vehicle Multispectral Imagery at Early and Middle Growth Stages. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 6716 -6728.
AMA StyleWenhui Wang, Yapeng Wu, Qiaofeng Zhang, Hengbiao Zheng, Xia Yao, Yan Zhu, Weixing Cao, Tao Cheng. AAVI: A Novel Approach to Estimating Leaf Nitrogen Concentration in Rice From Unmanned Aerial Vehicle Multispectral Imagery at Early and Middle Growth Stages. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):6716-6728.
Chicago/Turabian StyleWenhui Wang; Yapeng Wu; Qiaofeng Zhang; Hengbiao Zheng; Xia Yao; Yan Zhu; Weixing Cao; Tao Cheng. 2021. "AAVI: A Novel Approach to Estimating Leaf Nitrogen Concentration in Rice From Unmanned Aerial Vehicle Multispectral Imagery at Early and Middle Growth Stages." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 6716-6728.
Extreme temperature events as a consequence of global climate change result in a significant decline in rice production. A two-year phytotron experiment was conducted using three temperature levels and two heating durations to compare the effects of heat stress at booting, flowering, and combined (booting + flowering) stages on the production of photosynthates and yield formation. The results showed that high temperature had a significant negative effect on mean net assimilation rate (MNAR), harvest index (HI), and grain yield per plant (YPP), and a significant positive effect under treatment T3 on mean leaf area index (MLAI) and duration of photosynthesis (DOP), and no significant effect on biomass per plant at maturity (BPPM), except at the flowering stage. Negative linear relationships between heat degree days (HDD) and MNAR, HI, and YPP were observed. Conversely, HDD showed positive linear relationships with MLAI and DOP. In addition, BPPM also showed a positive relationship with HDD, except at flowering, for both cultivars and Wuyunjing-24 at combined stages. The variation of YPP in both cultivars was mainly attributed to HI compared to BPPM. However, for biomass, from the first day of high-temperature treatment to maturity (BPPT-M), the main change was caused by MNAR followed by DOP and then MLAI. The projected alleviation effects of multiple heat stress at combined stages compared to single-stage heat stress would help to understand and evaluate rice yield formation and screening of heat-tolerant rice cultivars under current scenarios of high temperature during the rice-growing season.
Aqib Mahmood; Wei Wang; Iftikhar Ali; Fengxian Zhen; Raheel Osman; Bing Liu; Leilei Liu; Yan Zhu; Weixing Cao; Liang Tang. Individual and Combined Effects of Booting and Flowering High-Temperature Stress on Rice Biomass Accumulation. Plants 2021, 10, 1021 .
AMA StyleAqib Mahmood, Wei Wang, Iftikhar Ali, Fengxian Zhen, Raheel Osman, Bing Liu, Leilei Liu, Yan Zhu, Weixing Cao, Liang Tang. Individual and Combined Effects of Booting and Flowering High-Temperature Stress on Rice Biomass Accumulation. Plants. 2021; 10 (5):1021.
Chicago/Turabian StyleAqib Mahmood; Wei Wang; Iftikhar Ali; Fengxian Zhen; Raheel Osman; Bing Liu; Leilei Liu; Yan Zhu; Weixing Cao; Liang Tang. 2021. "Individual and Combined Effects of Booting and Flowering High-Temperature Stress on Rice Biomass Accumulation." Plants 10, no. 5: 1021.
Accurately predicting crop development stage is key to simulating growth and yield formation in crop models. Low temperature stress is a major limitation to global wheat production and can significantly slow down wheat development rate. In a four-year environment-controlled phytotron experiments, detailed phenology datasets were obtained for low temperature stress treatments with different temperature levels and durations. Six widely-used temperature response functions (Linear, Bilinear Triangular, Trapezoidal, Bell-shaped, and Sin) for wheat phenology estimation were combined with WheatGrow model to simulate the low temperature stress effects on elongation-anthesis durations in order to test whether the effects of low temperature stress can be captured by current temperature response functions. In addition, a new algorithm for quantifying daily thermal sensitivity (DTS) was proposed and applied in the six temperature response functions to improve the prediction ability of phenology submodel under low temperature stress. The result indicates that anthesis stage was significantly delayed under low temperature stress at elongation and booting stages. All six original temperature response routines underestimated the delay of wheat development rate caused by low temperature stress, and the Linear and Triangular temperature response routines showed better performance than other four functions. A new improved DTS algorithm (DTSimproved) was proposed which can better quantify the delay of wheat development rate during low temperature stress and the recovery of development rate after low temperature stress. Model validation results show that compared with the DTSoriginal routine, all six improved phenology submodels (DTSimproved) significantly reduced the simulation error under low temperature stress, with the RMSE of elongation-anthesis duration under extreme low temperature stress (Tmin<0) decreased by 53.6% and 36.7% for cv.Yangmai16 and cv.Xumai30, respectively. Therefore, the newly improved routine for wheat phenology under low temperature stress can significantly reduce the uncertainties in model-based impact assessments under low temperature stress.
Liujun Xiao; Bing Liu; Huxin Zhang; Junyan Gu; Tianyu Fu; Senthold Asseng; Leilei Liu; Liang Tang; Weixing Cao; Yan Zhu. Modeling the response of winter wheat phenology to low temperature stress at elongation and booting stages. Agricultural and Forest Meteorology 2021, 303, 108376 .
AMA StyleLiujun Xiao, Bing Liu, Huxin Zhang, Junyan Gu, Tianyu Fu, Senthold Asseng, Leilei Liu, Liang Tang, Weixing Cao, Yan Zhu. Modeling the response of winter wheat phenology to low temperature stress at elongation and booting stages. Agricultural and Forest Meteorology. 2021; 303 ():108376.
Chicago/Turabian StyleLiujun Xiao; Bing Liu; Huxin Zhang; Junyan Gu; Tianyu Fu; Senthold Asseng; Leilei Liu; Liang Tang; Weixing Cao; Yan Zhu. 2021. "Modeling the response of winter wheat phenology to low temperature stress at elongation and booting stages." Agricultural and Forest Meteorology 303, no. : 108376.
Real-time and accurate monitoring of nitrogen content in crops is crucial for precision agriculture. Proximal sensing is the most common technique for monitoring crop traits, but it is often influenced by soil background and shadow effects. However, few studies have investigated the classification of different components of crop canopy, and the performance of spectral and textural indices from different components on estimating leaf nitrogen content (LNC) of wheat remains unexplored. This study aims to investigate a new feature extracted from near-ground hyperspectral imaging data to estimate precisely the LNC of wheat. In field experiments conducted over two years, we collected hyperspectral images at different rates of nitrogen and planting densities for several varieties of wheat throughout the growing season. We used traditional methods of classification (one unsupervised and one supervised method), spectral analysis (SA), textural analysis (TA), and integrated spectral and textural analysis (S-TA) to classify the images obtained as those of soil, panicles, sunlit leaves (SL), and shadowed leaves (SHL). The results show that the S-TA can provide a reasonable compromise between accuracy and efficiency (overall accuracy = 97.8%, Kappa coefficient = 0.971, and run time = 14 min), so the comparative results from S-TA were used to generate four target objects: the whole image (WI), all leaves (AL), SL, and SHL. Then, those objects were used to determine the relationships between the LNC and three types of indices: spectral indices (SIs), textural indices (TIs), and spectral and textural indices (STIs). All AL-derived indices achieved more stable relationships with the LNC than the WI-, SL-, and SHL-derived indices, and the AL-derived STI was the best index for estimating the LNC in terms of both calibration (R2 c = 0.78, relative root mean-squared error (RRMSEc) = 13.5%) and validation (R2 v = 0.83, RRMSEv = 10.9%). It suggests that extracting the spectral and textural features of all leaves from near-ground hyperspectral images can precisely estimate the LNC of wheat throughout the growing season. The workflow is promising for the LNC estimation of other crops and could be helpful for precision agriculture.
Jiale Jiang; Jie Zhu; Xue Wang; Tao Cheng; Yongchao Tian; Yan Zhu; Weixing Cao; Xia Yao. Estimating the Leaf Nitrogen Content with a New Feature Extracted from the Ultra-High Spectral and Spatial Resolution Images in Wheat. Remote Sensing 2021, 13, 739 .
AMA StyleJiale Jiang, Jie Zhu, Xue Wang, Tao Cheng, Yongchao Tian, Yan Zhu, Weixing Cao, Xia Yao. Estimating the Leaf Nitrogen Content with a New Feature Extracted from the Ultra-High Spectral and Spatial Resolution Images in Wheat. Remote Sensing. 2021; 13 (4):739.
Chicago/Turabian StyleJiale Jiang; Jie Zhu; Xue Wang; Tao Cheng; Yongchao Tian; Yan Zhu; Weixing Cao; Xia Yao. 2021. "Estimating the Leaf Nitrogen Content with a New Feature Extracted from the Ultra-High Spectral and Spatial Resolution Images in Wheat." Remote Sensing 13, no. 4: 739.
Nitrogen is an important indicator for monitoring wheat growth. The rapid development and wide application of non-destructive detection provide many approaches for estimating leaf nitrogen content (LNC) in wheat. Previous studies have shown that better results have been obtained in the estimation of LNC in wheat based on spectral features. However, the lack of automatically extracted features leads to poor universality of the estimation model. Therefore, a feature fusion method for estimating LNC in wheat by combining spectral features with deep features (spatial features) was proposed. The deep features were automatically obtained with a convolutional neural network model based on the PyTorch framework. The spectral features were obtained using spectral information including position features (PFs) and vegetation indices (VIs). Different models based on feature combination for evaluating LNC in wheat were constructed: partial least squares regression (PLS), gradient boosting decision tree (GBDT), and support vector regression (SVR). The results indicate that the model based on the fusion feature from near-ground hyperspectral imagery has good estimation effect. In particular, the estimation accuracy of the GBDT model is the best (R2 = 0.975 for calibration set, R2 = 0.861 for validation set). These findings demonstrate that the approach proposed in this study improved the estimation performance of LNC in wheat, which could provide technical support in wheat growth monitoring.
Baohua Yang; Jifeng Ma; Xia Yao; Weixing Cao; Yan Zhu. Estimation of Leaf Nitrogen Content in Wheat Based on Fusion of Spectral Features and Deep Features from Near Infrared Hyperspectral Imagery. Sensors 2021, 21, 613 .
AMA StyleBaohua Yang, Jifeng Ma, Xia Yao, Weixing Cao, Yan Zhu. Estimation of Leaf Nitrogen Content in Wheat Based on Fusion of Spectral Features and Deep Features from Near Infrared Hyperspectral Imagery. Sensors. 2021; 21 (2):613.
Chicago/Turabian StyleBaohua Yang; Jifeng Ma; Xia Yao; Weixing Cao; Yan Zhu. 2021. "Estimation of Leaf Nitrogen Content in Wheat Based on Fusion of Spectral Features and Deep Features from Near Infrared Hyperspectral Imagery." Sensors 21, no. 2: 613.
The accurate estimation of nitrogen accumulation is of great significance to nitrogen fertilizer management in wheat production. To overcome the shortcomings of spectral technology, which ignores the anisotropy of canopy structure when predicting the nitrogen accumulation in wheat, resulting in low accuracy and unstable prediction results, we propose a method for predicting wheat nitrogen accumulation based on the fusion of spectral and canopy structure features. After depth images are repaired using a hole-filling algorithm, RGB images and depth images are fused through IHS transformation, and textural features of the fused images are then extracted in order to express the three-dimensional structural information of the canopy. The fused images contain depth information of the canopy, which breaks through the limitation of extracting canopy structure features from a two-dimensional image. By comparing the experimental results of multiple regression analyses and BP neural networks, we found that the characteristics of the canopy structure effectively compensated for the model prediction of nitrogen accumulation based only on spectral characteristics. Our prediction model displayed better accuracy and stability, with prediction accuracy values (R2) based on BP neural network for the leaf layer nitrogen accumulation (LNA) and shoot nitrogen accumulation (SNA) during a full growth period of 0.74 and 0.73, respectively, and corresponding relative root mean square errors (RRMSEs) of 40.13% and 35.73%.
Ke Xu; Jingchao Zhang; Huaimin Li; Weixing Cao; Yan Zhu; Xiaoping Jiang; Jun Ni. Spectrum- and RGB-D-Based Image Fusion for the Prediction of Nitrogen Accumulation in Wheat. Remote Sensing 2020, 12, 4040 .
AMA StyleKe Xu, Jingchao Zhang, Huaimin Li, Weixing Cao, Yan Zhu, Xiaoping Jiang, Jun Ni. Spectrum- and RGB-D-Based Image Fusion for the Prediction of Nitrogen Accumulation in Wheat. Remote Sensing. 2020; 12 (24):4040.
Chicago/Turabian StyleKe Xu; Jingchao Zhang; Huaimin Li; Weixing Cao; Yan Zhu; Xiaoping Jiang; Jun Ni. 2020. "Spectrum- and RGB-D-Based Image Fusion for the Prediction of Nitrogen Accumulation in Wheat." Remote Sensing 12, no. 24: 4040.
Extreme high-temperature stress (HTS) associated with climate change poses potential threats to wheat grain yield and quality. Wheat grain protein concentration (GPC) is a determinant of wheat quality for human nutrition and is often neglected in attempts to assess climate change impacts on wheat production. Crop models are useful tools for quantification of temperature impacts on grain yield and quality. Current crop models either cannot simulate or can simulate only partially the effects of HTS on crop N dynamics and grain N accumulation. There is a paucity of observational data on crop N and grain quality collected under systematic HTS scenarios to develop algorithms for model improvement as well as evaluate crop models. Two-year phytotron experiments were conducted with two wheat cultivars under HTS at anthesis, grain filling, and both stages. HTS significantly reduced total aboveground N and increased the rate of grain N accumulation, while total aboveground N and the rate of grain N accumulation were more sensitive to HTS at anthesis than at grain filling. The observed relationships between total aboveground N, rate of grain N accumulation, and HTS were quantified and incorporated into the WheatGrow model. The new HTS routines improved simulation of the dynamics of total aboveground N, grain N accumulation, and GPC by the model. The improved model provided better estimates of total aboveground N, grain N accumulation, and GPC under HTS (the normalized root mean square error was reduced by 40%, 85%, and 80%, respectively) than the original WheatGrow model. The improvements in the model enhance its applicability to the assessment of climate change effects on wheat grain quality by reducing the uncertainties of simulating N dynamics and grain quality under HTS.
Raheel Osman; Yan Zhu; Weixing Cao; Zhifeng Ding; Meng Wang; Leilei Liu; Liang Tang; Bing Liu. Modeling the effects of extreme high-temperature stress at anthesis and grain filling on grain protein in winter wheat. The Crop Journal 2020, 9, 889 -900.
AMA StyleRaheel Osman, Yan Zhu, Weixing Cao, Zhifeng Ding, Meng Wang, Leilei Liu, Liang Tang, Bing Liu. Modeling the effects of extreme high-temperature stress at anthesis and grain filling on grain protein in winter wheat. The Crop Journal. 2020; 9 (4):889-900.
Chicago/Turabian StyleRaheel Osman; Yan Zhu; Weixing Cao; Zhifeng Ding; Meng Wang; Leilei Liu; Liang Tang; Bing Liu. 2020. "Modeling the effects of extreme high-temperature stress at anthesis and grain filling on grain protein in winter wheat." The Crop Journal 9, no. 4: 889-900.
Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops.
Jie Jiang; Zeyu Zhang; Qiang Cao; Yan Liang; Brian Krienke; Yongchao Tian; Yan Zhu; Weixing Cao; Xiaojun Liu. Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat. Remote Sensing 2020, 12, 3684 .
AMA StyleJie Jiang, Zeyu Zhang, Qiang Cao, Yan Liang, Brian Krienke, Yongchao Tian, Yan Zhu, Weixing Cao, Xiaojun Liu. Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat. Remote Sensing. 2020; 12 (22):3684.
Chicago/Turabian StyleJie Jiang; Zeyu Zhang; Qiang Cao; Yan Liang; Brian Krienke; Yongchao Tian; Yan Zhu; Weixing Cao; Xiaojun Liu. 2020. "Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat." Remote Sensing 12, no. 22: 3684.
Leaf nitrogen content (LNC), an indicator for the amount of photosynthetic proteins, plays an important role to understand plant function and status. In previous studies, vegetation indices (VIs) have been demonstrated to monitor LNC non-destructively, but which is influenced by backgrounds, and lacks specificity for nitrogen stress. In this study, sun-induced chlorophyll fluorescence (SIF), a novel technique related to plant physiology state, was proposed to estimate area-based and mass-based LNC at both leaf and canopy scales. In addition, SIF indices were evaluated to retrieve photosynthesis nitrogen use efficiency (PNUE), an important trait of leaf economics and physiology, based on the relationships between SIF, photosynthesis, and LNC. This study was conducted on two field experiments of winter wheat with different nitrogen regimes in Rugao, Jiangsu Province, China during 2016-2017 and 2017-2018 growing seasons. We took measurements of SIF, reflectance, biochemical and growth structural parameters at the leaf and canopy scales. The SIF signal was collected using ASD (Analytical Spectral Devices, Boulder, CO, USA) and QEpro (Ocean Optics, Dunedin, FL, USA) spectrometers at the two observational scales, with a full width at half maximum (FWHM) of 1.4 nm and 0.13 nm, respectively. SIF indices were calculated based on the SIF signal extracted at two oxygen absorption bands. Our results demonstrated that area-based LNC was better related to SIF indices and VIs than mass-based LNC. SIF ratio index (SIFR) and normalized SIF index (SIFN), defined as SIF761/SIF687 and (SIF761-SIF687)/(SIF761+SIF687) separately, performed better in monitoring area-based LNC at the two observation scales than CIred edge, which performed best in VIs group. Compared with CIred edge, the best estimation accuracy of SIF indices for area-based LNC increased by 0.08 and 0.02 at the leaf and canopy scales, separately. And when using SIFR and SIFN to monitor area-based LNC, there is no saturation phenomenon, which occurs using traditional VIs. From the whole range of data, area-based LNC was closely related to several plant traits (leaf: area-based leaf chlorophyll content (LCC) (LCCarea), leaf mass per area (LMA); canopy: area-based canopy LCC (CCCarea), leaf area index (LAI), leaf dry weight (LDW) per unit soil area, and LMA), which was consistent with previous studies. However, in specific group with fixed area-based LCC value, although area-based LNC almost wasn’t significantly correlated with these traits, SIFR and SIFN were instead always highly correlated with area-based LNC in each small datasets on two observation scales (leaf scale: R2>0.50, R2>0.46; canopy scale: R2>0.41, R2>0.42). Thus, the contribution of SIFR and SIFN to estimate area-based LNC wasn’t only the plant traits listed, but also other internal characters, like nitrogen allocation and proportion. Moreover, SIFR and SIFN were proved to be potential detectors to retrieve PNUE. These findings would provide us a new perspective for understanding plant nitrogen status from remote sensing observations, detecting plant function and managing precise agriculture.
Min Jia; Roberto Colombo; Micol Rossini; Marco Celesti; Jie Zhu; Sergio Cogliati; Tao Cheng; Yongchao Tian; Yan Zhu; Weixing Cao; Xia Yao. Estimation of leaf nitrogen content and photosynthetic nitrogen use efficiency in wheat using sun-induced chlorophyll fluorescence at the leaf and canopy scales. European Journal of Agronomy 2020, 122, 126192 .
AMA StyleMin Jia, Roberto Colombo, Micol Rossini, Marco Celesti, Jie Zhu, Sergio Cogliati, Tao Cheng, Yongchao Tian, Yan Zhu, Weixing Cao, Xia Yao. Estimation of leaf nitrogen content and photosynthetic nitrogen use efficiency in wheat using sun-induced chlorophyll fluorescence at the leaf and canopy scales. European Journal of Agronomy. 2020; 122 ():126192.
Chicago/Turabian StyleMin Jia; Roberto Colombo; Micol Rossini; Marco Celesti; Jie Zhu; Sergio Cogliati; Tao Cheng; Yongchao Tian; Yan Zhu; Weixing Cao; Xia Yao. 2020. "Estimation of leaf nitrogen content and photosynthetic nitrogen use efficiency in wheat using sun-induced chlorophyll fluorescence at the leaf and canopy scales." European Journal of Agronomy 122, no. : 126192.
Crop production will likely face enormous challenges against the occurrences of extreme climatic events projected under future climate change. Heat waves that occur at critical stages of the reproductive phase have detrimental impacts on the grain yield formation of rice (Oryza sativa). Accurate estimates of these impacts are essential to evaluate the effects of climate change on rice. However, the accuracy of these predictions by crop models has not been extensively tested. In this study, we evaluated fourteen rice growth models against four‐year phytotron experiments with four levels of heat treatments imposed at different times after flowering. We found that all models greatly underestimated the negative effects of heat on grain yield, suggesting that yield projections with these models do not reflect food shocks that may occur under short‐term extreme heat stress (SEHS). As a result, crop model ensembles do not help to provide accurate estimates of grain yield under heat stress. We examined the functions for grain‐setting response to temperature (TRF_GS) used in eight models and showed that adjusting the effective periods of TRF_GS improved the model performance, especially for models simulating accumulative daily temperature effects. For TRF_GS which uses daily maximum temperature averaged for the effective period, the models provided better grain yield estimates by using maximum temperatures averaged only when daily maximum temperatures exceeded the base temperature (Tbase). An alternative method based on heating‐degree days (HDD) and stage‐dependent heat sensitivity parameters further decreased the prediction uncertainty of grain yield under heat stress, where stage‐dependent heat sensitivity was more important than heat dose for model improvement under SEHS. These results suggest the limitation of the applicability of existing rice models to variable climatic conditions and the urgent need for an alternative grain‐setting function accounting for the stage‐dependent heat sensitivity.
Ting Sun; Toshihiro Hasegawa; Bing Liu; Liang Tang; Leilei Liu; Weixing Cao; Yan Zhu. Current rice models underestimate yield losses from short‐term heat stresses. Global Change Biology 2020, 27, 402 -416.
AMA StyleTing Sun, Toshihiro Hasegawa, Bing Liu, Liang Tang, Leilei Liu, Weixing Cao, Yan Zhu. Current rice models underestimate yield losses from short‐term heat stresses. Global Change Biology. 2020; 27 (2):402-416.
Chicago/Turabian StyleTing Sun; Toshihiro Hasegawa; Bing Liu; Liang Tang; Leilei Liu; Weixing Cao; Yan Zhu. 2020. "Current rice models underestimate yield losses from short‐term heat stresses." Global Change Biology 27, no. 2: 402-416.
Grain yield of wheat and its components are very sensitive to heat stress at the critical growth stages of anthesis and grain filling. We observed negative impacts of heat stress on biomass partitioning and grain growth in environment-controlled phytotron experiments over 4 years, and we quantified relationships between the stress and grain number and potential grain weight at anthesis and during grain filling using process-based heat stress routines. These relationships included reduced grain set under stress at anthesis and decreased potential grain weight under stress during early grain filling. Biomass partitioning to stems and spikes was modified under heat stress based on a source–sink relationship. The integration of our process-based stress routines into the original WheatGrow model significantly enhanced the predictions of the biomass dynamics of the stems and spikes, the grain yield, and the yield components under heat stress. Compared to the original model, the improved version decreased the simulation errors for grain yield, grain number, and grain weight by 73%, 48%, and 49%, respectively, in an evaluation using independent data under heat stress in the phytotron conditions. When tested with data obtained under field conditions, the improved model showed a good ability to reproduce the decreasing dynamics of grain yield and its components with increasing post-anthesis temperatures. Sensitivity analysis showed that the improved model was able to reproduce the responses to various observed heat-stress treatments. These improvements to the crop model will be of significant importance for assessing the effects on crop production of projected increases in heat-stress events under future climate scenarios.
Bing Liu; Leilei Liu; Senthold Asseng; Dongzheng Zhang; Wei Ma; Liang Tang; Weixing Cao; Yan Zhu. Modelling the effects of post-heading heat stress on biomass partitioning, and grain number and weight of wheat. Journal of Experimental Botany 2020, 71, 1 .
AMA StyleBing Liu, Leilei Liu, Senthold Asseng, Dongzheng Zhang, Wei Ma, Liang Tang, Weixing Cao, Yan Zhu. Modelling the effects of post-heading heat stress on biomass partitioning, and grain number and weight of wheat. Journal of Experimental Botany. 2020; 71 (19):1.
Chicago/Turabian StyleBing Liu; Leilei Liu; Senthold Asseng; Dongzheng Zhang; Wei Ma; Liang Tang; Weixing Cao; Yan Zhu. 2020. "Modelling the effects of post-heading heat stress on biomass partitioning, and grain number and weight of wheat." Journal of Experimental Botany 71, no. 19: 1.
Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices. Highlights We propose a new approach to deriving calibration recommendations for system models Approach is based on analyzing calibration in multi-model simulation exercises Resulting recommendations are holistic and anchored in actual practice We apply the approach to calibration of crop models used to simulate phenology Recommendations concern: objective function, parameters to estimate, software used
Daniel Wallach; Taru Palosuo; Peter Thorburn; Zvi Hochman; Emmanuelle Gourdain; Fety Andrianasolo; Senthold Asseng; Bruno Basso; Samuel Buis; Neil Crout; Camilla Dibari; Benjamin Dumont; Roberto Ferrise; Thomas Gaiser; Cecile Garcia; Sebastian Gayler; Afshin Ghahramani; Santosh Hiremath; Steven Hoek; Heidi Horan; Gerrit Hoogenboom; Mingxia Huang; Mohamed Jabloun; Per-Erik Jansson; Qi Jing; Eric Justes; Kurt Christian Kersebaum; Anne Klosterhalfen; Marie Launay; Elisabet Lewan; Qunying Luo; Bernardo Maestrini; Henrike Mielenz; Marco Moriondo; Hasti Nariman Zadeh; Gloria Padovan; Jørgen Eivind Olesen; Arne Poyda; Eckart Priesack; Johannes Wilhelmus Maria Pullens; Budong Qian; Niels Schuetze; Vakhtang Shelia; Amir Souissi; Xenia Specka; Amit Kumar Srivastava; Tommaso Stella; Thilo Streck; Giacomo Trombi; Evelyn Wallor; Jing Wang; Tobias K.D. Weber; Lutz Weihermueller; Allard de Wit; Thomas Woehling; Liujun Xiao; Chuang Zhao; Yan Zhu; Sabine J. Seidel. The chaos in calibrating crop models. 2020, 1 .
AMA StyleDaniel Wallach, Taru Palosuo, Peter Thorburn, Zvi Hochman, Emmanuelle Gourdain, Fety Andrianasolo, Senthold Asseng, Bruno Basso, Samuel Buis, Neil Crout, Camilla Dibari, Benjamin Dumont, Roberto Ferrise, Thomas Gaiser, Cecile Garcia, Sebastian Gayler, Afshin Ghahramani, Santosh Hiremath, Steven Hoek, Heidi Horan, Gerrit Hoogenboom, Mingxia Huang, Mohamed Jabloun, Per-Erik Jansson, Qi Jing, Eric Justes, Kurt Christian Kersebaum, Anne Klosterhalfen, Marie Launay, Elisabet Lewan, Qunying Luo, Bernardo Maestrini, Henrike Mielenz, Marco Moriondo, Hasti Nariman Zadeh, Gloria Padovan, Jørgen Eivind Olesen, Arne Poyda, Eckart Priesack, Johannes Wilhelmus Maria Pullens, Budong Qian, Niels Schuetze, Vakhtang Shelia, Amir Souissi, Xenia Specka, Amit Kumar Srivastava, Tommaso Stella, Thilo Streck, Giacomo Trombi, Evelyn Wallor, Jing Wang, Tobias K.D. Weber, Lutz Weihermueller, Allard de Wit, Thomas Woehling, Liujun Xiao, Chuang Zhao, Yan Zhu, Sabine J. Seidel. The chaos in calibrating crop models. . 2020; ():1.
Chicago/Turabian StyleDaniel Wallach; Taru Palosuo; Peter Thorburn; Zvi Hochman; Emmanuelle Gourdain; Fety Andrianasolo; Senthold Asseng; Bruno Basso; Samuel Buis; Neil Crout; Camilla Dibari; Benjamin Dumont; Roberto Ferrise; Thomas Gaiser; Cecile Garcia; Sebastian Gayler; Afshin Ghahramani; Santosh Hiremath; Steven Hoek; Heidi Horan; Gerrit Hoogenboom; Mingxia Huang; Mohamed Jabloun; Per-Erik Jansson; Qi Jing; Eric Justes; Kurt Christian Kersebaum; Anne Klosterhalfen; Marie Launay; Elisabet Lewan; Qunying Luo; Bernardo Maestrini; Henrike Mielenz; Marco Moriondo; Hasti Nariman Zadeh; Gloria Padovan; Jørgen Eivind Olesen; Arne Poyda; Eckart Priesack; Johannes Wilhelmus Maria Pullens; Budong Qian; Niels Schuetze; Vakhtang Shelia; Amir Souissi; Xenia Specka; Amit Kumar Srivastava; Tommaso Stella; Thilo Streck; Giacomo Trombi; Evelyn Wallor; Jing Wang; Tobias K.D. Weber; Lutz Weihermueller; Allard de Wit; Thomas Woehling; Liujun Xiao; Chuang Zhao; Yan Zhu; Sabine J. Seidel. 2020. "The chaos in calibrating crop models." , no. : 1.
Satellite-based time-series crop monitoring at the subfield level is essential to the efficient implementation of precision crop management. Existing spatiotemporal image fusion techniques can be helpful, but they were often proposed to generate medium-resolution images. This study proposed a HIgh-resolution SpatioTemporal Image Fusion method (HISTIF) consisting of filtering for cross-scale spatial matching (FCSM) and multiplicative modulation of temporal change (MMTC). In FCSM, we considered both point spread function effect and geo-registration errors between fine and coarse resolution images. Subsequently, MMTC used pixel-based multiplicative factors to estimate the temporal change between reference and prediction dates without image classification. The performance of HISTIF was evaluated using both simulated and real datasets with one from real Gaofen-1 (GF-1) and simulated Landsat-like/Sentinel-like images, and the other from real GF-1 and real Landsat/Sentinel-2 data on two sites. HISTIF was compared with the existing methods Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF) and Fit-FC. The results demonstrated that HISTIF produced substantial reduction in the fusion error from cross-scale spatial mismatch and accurate reconstruction in spatial details within fields, regardless of simulated or real data. The images predicted by STARFM exhibited pronounced blocky artifacts. While the images predicted by HISTIF and Fit-FC both showed clear within-field variability patterns, HISTIF was able to reduce the spectral distortion more significantly than Fit-FC. Furthermore, HISTIF exhibited the most stable performance across sensors. The findings suggest that HISTIF could be beneficial for the frequent and detailed monitoring of crop growth at the subfield level.
Jiale Jiang; Qiaofeng Zhang; Xia Yao; Yongchao Tian; Yan Zhu; Weixing Cao; Tao Cheng. HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 4607 -4626.
AMA StyleJiale Jiang, Qiaofeng Zhang, Xia Yao, Yongchao Tian, Yan Zhu, Weixing Cao, Tao Cheng. HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):4607-4626.
Chicago/Turabian StyleJiale Jiang; Qiaofeng Zhang; Xia Yao; Yongchao Tian; Yan Zhu; Weixing Cao; Tao Cheng. 2020. "HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 4607-4626.
Crop biomass is a critical variable to make sound decisions about field crop monitoring activities (fertilizers and irrigation) and crop productivity forecasts. More importantly, crop biomass estimations by components are essential for crop growth monitoring as the yield formation of crops results from the accumulation and transportation of substances between different organs. Retrieval of crop biomass from synthetic aperture radar SAR or optical imagery is of paramount importance for in-season monitoring of crop growth. A combination of optical and SAR imagery can compensate for their limitations and has exhibited comparative advantages in biomass estimation. Notably, the joint estimations of biophysical parameters might be more accurate than that of an individual parameter. Previous studies have attempted to use satellite imagery to estimate aboveground biomass, but the estimation of biomass for individual organs remains a challenge. Multi-target Gaussian process regressor stacking (MGPRS), as a new machine learning method, can be suitably utilized to estimate biomass components jointly from satellite imagery data, as the model does not require a large amount of data for training and can be adjusted to the required degrees of relationship exhibited by the given data. Thus, the aim of this study was to estimate the biomass of individual organs by using MGPRS in conjunction with optical (Sentinel-2A) and SAR (Sentinel-1A) imagery. Two hybrid indices, SAR and optical multiplication vegetation index (SOMVI) and SAR and optical difference vegetation index (SODVI), have been constructed to examine their estimation performance. The hybrid vegetation indices were used as input for the MGPRS and single-target Gaussian process regression (SGPR). The accuracy of the estimation methods was analyzed by in situ measurements of aboveground biomass (AGB) and organ biomass conducted in 2018 and 2019 over the paddy rice fields of Xinghua in Jiangsu Province, China. The results showed that the combined indices (SOMVI and SODVI) performed better than those derived from either the optical or SAR data only. The best predictive accuracy was achieved by the MGPRS using SODVI as input (r2 = 0.84, RMSE = 0.4 kg/m2 for stem biomass; r2 = 0.87, RMSE = 0.16 kg/m2 for AGB). This was higher than using SOMVI as input for the MGPRS (r2 = 0.71, RMSE = 1.12 kg/m2 for stem biomass; r2 = 0.71, RMSE = 0.56 kg/m2 for AGB) or SGPR (r2 = 0.63, RMSE = 1.08 kg/m2 for stem biomass; r2 = 0.67, RMSE = 1.08 kg/m2 for AGB). Relatively, higher accuracy for leaf biomass was achieved using SOMVI (r2 = 0.83) than using SODVI (r2 = 0.73) as input for MGPRS. Our results demonstrate that the combined indices are effective by integrating SAR and optical imagery and MGPRS outperformed SGPR with the same input variable for estimating rice crop biomass. The presented workflow will improve the estimation of crops biomass components from satellite data for effective crop growth monitoring.
Yeshanbele Alebele; Xue Zhang; Wenhui Wang; Gaoxiang Yang; Xia Yao; Hengbiao Zheng; Yan Zhu; Weixing Cao; Tao Cheng. Estimation of Canopy Biomass Components in Paddy Rice from Combined Optical and SAR Data Using Multi-Target Gaussian Regressor Stacking. Remote Sensing 2020, 12, 2564 .
AMA StyleYeshanbele Alebele, Xue Zhang, Wenhui Wang, Gaoxiang Yang, Xia Yao, Hengbiao Zheng, Yan Zhu, Weixing Cao, Tao Cheng. Estimation of Canopy Biomass Components in Paddy Rice from Combined Optical and SAR Data Using Multi-Target Gaussian Regressor Stacking. Remote Sensing. 2020; 12 (16):2564.
Chicago/Turabian StyleYeshanbele Alebele; Xue Zhang; Wenhui Wang; Gaoxiang Yang; Xia Yao; Hengbiao Zheng; Yan Zhu; Weixing Cao; Tao Cheng. 2020. "Estimation of Canopy Biomass Components in Paddy Rice from Combined Optical and SAR Data Using Multi-Target Gaussian Regressor Stacking." Remote Sensing 12, no. 16: 2564.
Extreme heat-stress events have become more frequent due to climate change often with devastating effects on rice production. Accumulation and translocation of nitrogen (N) in rice organs is an important process that determines rice yield and quality. To assess the effects of short-term heat stress during booting stage on the accumulation and remobilization of N in rice plant organs, a pot-experiment in phytotron chambers was conducted using two cultivars (Nanjing 41 and Wuyunjing 24) at mean temperatures of 27 °C, 31 °C, 35 °C and 39 °C for 2, 4 and 6 days at booting stage. The results showed that high temperatures of 35°C and 39°C for 4 and 6 days significantly increased N concentration in leaves, stems, panicles and grains. Severe heat stress strongly reduced N translocation from leaves and stems into grains, resulting in an increased N distribution in stems and leaves. Moreover, N translocation efficiency of the vegetative parts decreased with increasing heat degree-days (HDD), and when HDD > 21.5 °C d, N translocation efficiency was negative, indicating re-accumulation of N in vegetative organs under severe heat stress. N concentration in leaves was positively associated with photosynthetic rate. Dry matter partitioning index of leaves and stems was positively correlated with their N concentration, whereas grain dry matter partitioning index was negatively correlated with grain N concentration. Heat stress reduced the ratio of grain number to leaf area, thereby reducing the proportion of sink to source. These results suggested that the low N translocation efficiency under heat stress could be due to a decrease in sink capacity. Our findings demonstrate that projected climate warming is likely to induce a significant reduction in N accumulation in rice grains by inhibiting the translocation of N from vegetative organs to grains.
Fengxian Zhen; Yijiang Liu; Iftikhar Ali; Bing Liu; Leilei Liu; Weixing Cao; Liang Tang; Yan Zhu. Short-term heat stress at booting stage inhibited nitrogen remobilization to grain in rice. Journal of Agriculture and Food Research 2020, 2, 100066 .
AMA StyleFengxian Zhen, Yijiang Liu, Iftikhar Ali, Bing Liu, Leilei Liu, Weixing Cao, Liang Tang, Yan Zhu. Short-term heat stress at booting stage inhibited nitrogen remobilization to grain in rice. Journal of Agriculture and Food Research. 2020; 2 ():100066.
Chicago/Turabian StyleFengxian Zhen; Yijiang Liu; Iftikhar Ali; Bing Liu; Leilei Liu; Weixing Cao; Liang Tang; Yan Zhu. 2020. "Short-term heat stress at booting stage inhibited nitrogen remobilization to grain in rice." Journal of Agriculture and Food Research 2, no. : 100066.
Keeping global temperatures below 2.0 °C above pre-industrial condition and pursuing efforts toward the more ambitious 1.5 °C goal in the late 21st century was the main target from the Paris Agreement in 2015. Here we assessed the likely challenges for the China’s winter wheat production under 1.5 °C and 2.0 °C increase of global temperature, with four wheat crop models (CERES-Wheat, Nwheat, WheatGrow, and APSIM-Wheat) and the latest climate projections from the Half a degree Additional warming, Prognosis and Projected Impacts project (HAPPI). Instead of using average “winter type” wheat cultivar, and same management and soil inputs for whole region, location-specific winter wheat cultivars with local agronomic information were calibrated for each of the representative wheat growing area of China, allowing a better spatial agronomic representation of the whole wheat planting area. The mean growing season temperature (GST) during the winter wheat vegetative stage was projected to increase by 0.6 to 1.4 °C for the 1.5 °C scenario, and 0.9 to 1.8 °C for the 2.0 °C scenario, while during the reproductive stage was decreased between 0 and 0.9 °C for the 1.5 °C scenario and -0.3 and 1.1 °C for the 2.0 °C scenario. Growing season duration (GSD) for the whole period was shortened by 6 to 15 days for the 1.5 °C scenario and 8 to 18 days for the 2.0 °C scenario, as a result of higher GST under global warming. Increase in GST and decrease in GSD was more obvious in the Southwest Subregion (SWS) than subregions in the north. The shortening GSD for the whole wheat growth period was mostly from the shortening vegetative period, as no appreciable difference in number of days from anthesis to maturity was found for the whole regions. Although there is variability among models, the indication is that wheat yields were projected to increase in the North Subregion (NS), the Huang-Huai Subregion (HHS), and the Middle-lower Researches of Yangzi River Subregion (MYS), but to decrease in the SWS under two warming scenarios. The effects of elevated CO2 concentration were mostly beneficial and tended to offset the negative impacts of increasing temperature at both global warming scenarios, with a rate of 7-14% yield increase per 100-ppm, except for locations with GST of baseline higher than 11 °C. Aggregating to regional wheat production, the total winter wheat production of China was projected to increase by 2.8% (1.6% to 3.0%, 25th percentile to 75th percentile) and 8.3% (7.0% to 9.6%, 25th percentile to 75th percentile) under 1.5 °C and 2.0 °C scenarios, and most of increase was observed in the north subregions due to the largest wheat planting area. Our results will lay the foundation for developing adaptation strategies to future climate change to ensure China and global wheat supply and food security.
Zi Ye; Xiaolei Qiu; Jian Chen; Davide Cammarano; Zhonglei Ge; Alex C. Ruane; Leilei Liu; Liang Tang; Weixing Cao; Bing Liu; Yan Zhu. Impacts of 1.5 °C and 2.0 °C global warming above pre-industrial on potential winter wheat production of China. European Journal of Agronomy 2020, 120, 126149 .
AMA StyleZi Ye, Xiaolei Qiu, Jian Chen, Davide Cammarano, Zhonglei Ge, Alex C. Ruane, Leilei Liu, Liang Tang, Weixing Cao, Bing Liu, Yan Zhu. Impacts of 1.5 °C and 2.0 °C global warming above pre-industrial on potential winter wheat production of China. European Journal of Agronomy. 2020; 120 ():126149.
Chicago/Turabian StyleZi Ye; Xiaolei Qiu; Jian Chen; Davide Cammarano; Zhonglei Ge; Alex C. Ruane; Leilei Liu; Liang Tang; Weixing Cao; Bing Liu; Yan Zhu. 2020. "Impacts of 1.5 °C and 2.0 °C global warming above pre-industrial on potential winter wheat production of China." European Journal of Agronomy 120, no. : 126149.