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The dominant specialised cropping system (SCS) has supported the increasing population in China, although this agricultural production paradigm could lead to environmental problems. The modern integrated crop-livestock system (ICLS) in China, designed as a recycling paradigm, can alleviate the negative environmental impacts of SCS. However, it must be better investigated, especially due to the trade-off between increased production and environmental harm. In this study, we set up a multi-criteria evaluation with eight indicators based on emergy analysis to quantify and compare the performance of ICLS and SCS and to evaluate the performance of the indicators along a gradient when a proportion of chemical nitrogen is substituted with manure fertiliser nitrogen (PCSM). We examined one experimental modern ICLS and an average SCS by conducting a household survey in Shandong province. The results showed that the ICLS puts less pressure on the environment (ELR = 1.04), has higher emergy sustainability (ESI = 1.09), and generates higher economic benefits per unit area of land (LPO = 3789.94 $‧ha−1). However, the productivity of the ICLS is lower (Tr = 1.66E+05 sej‧J−1, EOD = 1.07E+11 J‧ha−1‧yr−1, EYR = 1.13) than that of the SCS. With an increasing gradient of PCSM, for both systems, the productivity and environmental pressure decreased sharply; this trade-off was less marked for the ICLS. Considering sustainable resource utilisation, environmentally friendly production, and system stability, the ICLS could be an option for Chinese agricultural production in regions with serious issues of manure pollution and cultivated land degradation. However, the ICLS needs to optimise the crop-livestock structure, strengthen scientific management, and improve productivity.
Yang Li; Zhigang Sun; Francesco Accatino; Sheng Hang; Yun Lv; Zhu Ouyang. Comparing specialised crop and integrated crop-livestock systems in China with a multi-criteria approach using the emergy method. Journal of Cleaner Production 2021, 314, 127974 .
AMA StyleYang Li, Zhigang Sun, Francesco Accatino, Sheng Hang, Yun Lv, Zhu Ouyang. Comparing specialised crop and integrated crop-livestock systems in China with a multi-criteria approach using the emergy method. Journal of Cleaner Production. 2021; 314 ():127974.
Chicago/Turabian StyleYang Li; Zhigang Sun; Francesco Accatino; Sheng Hang; Yun Lv; Zhu Ouyang. 2021. "Comparing specialised crop and integrated crop-livestock systems in China with a multi-criteria approach using the emergy method." Journal of Cleaner Production 314, no. : 127974.
Achieving food and feed self-sufficiency is important for both China and the world. While China's food self-sufficiency has been examined at the national and provincial levels, few studies consider lower administrative levels or different food and feed items. This study quantifies self-sufficiency in the eastern regions of China and examines correlations with agronomic (arable area, yield, fertilizer input, and machinery power) and socioeconomic (population density, gross domestic product [GDP]) variables at the local level, which are related to the interactions of the Sustainable Development Goals. We calculated food and feed balances, and checked correlations across and within regions grouped by population density levels between production, balance indices, and other agronomic and socioeconomic variables. The results showed that most regions can achieve self-sufficiency in cereals, vegetables, and meat. Regarding eggs and maize, there was self-sufficiency in the north but deficiency in the south. Nearly all regions demonstrated extreme shortages of milk and soybeans. The results also showed a positive correlation between the production of some food commodities and the population in eastern regions of China, demonstrating that the aim of achieving food self-sufficiency at the local level is pursued. For cereals, vegetables, and maize, the yield and arable land per capita were positive factors for self-sufficiency, while GDP per capita was a negative factor for cereals, meat, and maize. Various factors have different impacts on the food and feed self-sufficiency of regions based on population density. Protecting arable land by rural revitalization and mitigating urban sprawl can retain food and feed self-sufficiency in large cities. This study outlines important implications for policymakers seeking to achieve food and feed self-sufficiency in China.
Yang Li; Zhigang Sun; Francesco Accatino. Spatial distribution and driving factors determining local food and feed self‐sufficiency in the eastern regions of China. Food and Energy Security 2021, 10, e296 .
AMA StyleYang Li, Zhigang Sun, Francesco Accatino. Spatial distribution and driving factors determining local food and feed self‐sufficiency in the eastern regions of China. Food and Energy Security. 2021; 10 (3):e296.
Chicago/Turabian StyleYang Li; Zhigang Sun; Francesco Accatino. 2021. "Spatial distribution and driving factors determining local food and feed self‐sufficiency in the eastern regions of China." Food and Energy Security 10, no. 3: e296.
Unmanned aerial vehicle (UAV) system is an emerging remote sensing tool for profiling crop phenotypic characteristics, as it distinctly captures crop real-time information on field scales. For optimizing UAV agro-monitoring schemes, this study investigated the performance of single-source and multi-source UAV data on maize phenotyping (leaf area index, above-ground biomass, crop height, leaf chlorophyll concentration, and plant moisture content). Four UAV systems [i.e., hyperspectral, thermal, RGB, and Light Detection and Ranging (LiDAR)] were used to conduct flight missions above two long-term experimental fields involving multi-level treatments of fertilization and irrigation. For reducing the effects of algorithm characteristics on maize parameter estimation and ensuring the reliability of estimates, multi-variable linear regression, backpropagation neural network, random forest, and support vector machine were used for modeling. Highly correlated UAV variables were filtered, and optimal UAV inputs were determined using a recursive feature elimination procedure. Major conclusions are (1) for single-source UAV data, LiDAR and RGB texture were suitable for leaf area index, above-ground biomass, and crop height estimation; hyperspectral outperformed on leaf chlorophyll concentration estimation; thermal worked for plant moisture content estimation; (2) model performance was slightly boosted via the fusion of multi-source UAV datasets regarding leaf area index, above-ground biomass, and crop height estimation, while single-source thermal and hyperspectral data outperformed multi-source data for the estimation of plant moisture and leaf chlorophyll concentration, respectively; (3) the optimal UAV scheme for leaf area index, above-ground biomass, and crop height estimation was LiDAR + RGB + hyperspectral, while considering practical agro-applications, optical Structure from Motion + customer-defined multispectral system was recommended owing to its cost-effectiveness. This study contributes to the optimization of UAV agro-monitoring schemes designed for field-scale crop phenotyping and further extends the applications of UAV technologies in precision agriculture.
Wanxue Zhu; Zhigang Sun; Yaohuan Huang; Ting Yang; Jing Li; Kangying Zhu; Junqiang Zhang; Bin Yang; Changxiu Shao; Jinbang Peng; Shiji Li; Hualang Hu; Xiaohan Liao. Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping. Precision Agriculture 2021, 1 -35.
AMA StyleWanxue Zhu, Zhigang Sun, Yaohuan Huang, Ting Yang, Jing Li, Kangying Zhu, Junqiang Zhang, Bin Yang, Changxiu Shao, Jinbang Peng, Shiji Li, Hualang Hu, Xiaohan Liao. Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping. Precision Agriculture. 2021; ():1-35.
Chicago/Turabian StyleWanxue Zhu; Zhigang Sun; Yaohuan Huang; Ting Yang; Jing Li; Kangying Zhu; Junqiang Zhang; Bin Yang; Changxiu Shao; Jinbang Peng; Shiji Li; Hualang Hu; Xiaohan Liao. 2021. "Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping." Precision Agriculture , no. : 1-35.
Abundant shallow underground brackish water resources could help in alleviating the shortage of fresh water resources and the crisis concerning agricultural water resources in the North China Plain. Improper brackish water irrigation will increase soil salinity and decrease the final yield due to salt stress affecting the crops. Therefore, it is urgent to develop a practical and low-cost method to monitor the soil salinity of brackish irrigation systems. Remotely sensed spectral vegetation indices (SVIs) of crops are promising proxies for indicating the salinity of the surface soil layer. However, there is still a challenge concerning quantitatively correlating SVIs with the salinity of deeper soil layers, in which crop roots are mainly distributed. In this study, a field experiment was conducted to investigate the relationship between SVIs and salinity measurements at four soil depths within six winter wheat plots irrigated using three salinity levels at the Yucheng Comprehensive Experimental Station of the Chinese Academy of Sciences during 2017–2019. The hyperspectral reflectance was measured during the grain-filling stage of winter wheat, since it is more sensitive to soil salinity during this period. The SVIs derived from the observed hyperspectral data of winter wheat were compared with the salinity at four soil depths. The results showed that the optimized SVIs, involving soil salt-sensitive blue, red-edge, and near-infrared wavebands, performed better when retrieving the soil salinity (R2 ≥ 0.58, root mean square error (RMSE) ≤ 0.62 g/L), especially at the 30-cm depth (R2 = 0.81, RMSE = 0.36 g/L). For practical applications, linear or quadratic models based on the screened SVIs in the form of normalized differential vegetation indices (NDVIs) could be used to retrieve soil salinity (R2 ≥ 0.63, RMSE ≤ 0.62 g/L) at all soil depths and then diagnose salt stress in winter wheat. This could provide a practical technique for evaluating regional brackish water irrigation systems.
Kangying Zhu; Zhigang Sun; Fenghua Zhao; Ting Yang; Zhenrong Tian; Jianbin Lai; Wanxue Zhu; Buju Long. Relating Hyperspectral Vegetation Indices with Soil Salinity at Different Depths for the Diagnosis of Winter Wheat Salt Stress. Remote Sensing 2021, 13, 250 .
AMA StyleKangying Zhu, Zhigang Sun, Fenghua Zhao, Ting Yang, Zhenrong Tian, Jianbin Lai, Wanxue Zhu, Buju Long. Relating Hyperspectral Vegetation Indices with Soil Salinity at Different Depths for the Diagnosis of Winter Wheat Salt Stress. Remote Sensing. 2021; 13 (2):250.
Chicago/Turabian StyleKangying Zhu; Zhigang Sun; Fenghua Zhao; Ting Yang; Zhenrong Tian; Jianbin Lai; Wanxue Zhu; Buju Long. 2021. "Relating Hyperspectral Vegetation Indices with Soil Salinity at Different Depths for the Diagnosis of Winter Wheat Salt Stress." Remote Sensing 13, no. 2: 250.
Wild animal surveys play a critical role in wild animal conservation and ecosystem management. Unmanned aircraft systems (UASs), with advantages in safety, convenience and inexpensiveness, have been increasingly used in wild animal surveys. However, manually reviewing wild animals from thousands of images generated by UASs is tedious and inefficient. To support wild animal detection in UAS images, researchers have developed various automatic and semiautomatic algorithms. Among these algorithms, deep learning techniques achieve outstanding performances in wild animal detection, but have some practical issues (e.g., limited animal pixels and sparse animal samples). Based on a typical deep learning pipeline, faster region based convolutional neural networks (Faster R-CNN), this study adopted several tactics, including feature stride shortening, anchor size optimization, and hard negative class, to overcome the practical issues in wild animal detection in UAS images. In this study, a kiang survey was conducted in UAS datasets (23,748 images) obtained by 14 flight campaigns in the eastern Tibetan Plateau. The validation experiments of our adopted tactics revealed the following: (1) feature stride shortening and anchor size optimization improved small animal detection performance in the animal patch set, increasing the F1 score from 0.84 to 0.86 and from 0.86 to 0.92, respectively; and (2) the hard negative class significantly suppressed false positives in the full UAS image set, increasing the F1 score from 0.44 to 0.86. The test results in the full UAS image set showed that the modified model with the adopted tactics can be applied to either a semiautomatic survey to accelerate manual verification by 25 times or an automatic survey with an F1 score of approximately 0.90. This study demonstrates that the combination of UAS and deep learning techniques can enable automatic/semiautomatic, accurate, inexpensive, and efficient wild animal surveys.
Jinbang Peng; Dongliang Wang; Xiaohan Liao; Quanqin Shao; Zhigang Sun; Huanyin Yue; Huping Ye. Wild animal survey using UAS imagery and deep learning: modified Faster R-CNN for kiang detection in Tibetan Plateau. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 169, 364 -376.
AMA StyleJinbang Peng, Dongliang Wang, Xiaohan Liao, Quanqin Shao, Zhigang Sun, Huanyin Yue, Huping Ye. Wild animal survey using UAS imagery and deep learning: modified Faster R-CNN for kiang detection in Tibetan Plateau. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 169 ():364-376.
Chicago/Turabian StyleJinbang Peng; Dongliang Wang; Xiaohan Liao; Quanqin Shao; Zhigang Sun; Huanyin Yue; Huping Ye. 2020. "Wild animal survey using UAS imagery and deep learning: modified Faster R-CNN for kiang detection in Tibetan Plateau." ISPRS Journal of Photogrammetry and Remote Sensing 169, no. : 364-376.
Leaf chlorophyll content (LCC) is a crucial indicator of nutrition in crop plants and can be applied to assess the adequacy of nitrogen (N) fertilizer for crops while reducing N losses to farmland. This study estimated the LCC of maize and wheat, and comprehensively examined the effects of the spectral information and spatial scale of unmanned aerial vehicle (UAV) imagery, and the effects of phenotype and phenology on LCC estimation. A Cubert S185 hyperspectral camera onboard a DJI M600 Pro was used to conduct six flight missions over a long-term experimental field with five N applications (0, 70, 140, 210, and 280 kg N ha−1) and two irrigation levels (60% and 80% field water capacity) during the growing seasons of wheat and maize in 2019. Four regression algorithms, that is, multi-variable linear regression, random forest, backpropagation neural network, and support vector machine, were used for modeling. Leaf, canopy, and hybrid scale hyperspectral variables (H-variables) were used as inputs for the statistical LCC models. Optimal H-variables for modeling were determined by Pearson correlation filtering followed by a recursive feature elimination procedure. The results showed that (1) H-variables at the canopy- and leaf-scales were appropriate for wheat and maize LCC estimation, respectively; (2) the robustness of LCC estimation was in the order of the flowering stage > heading stage > grain filling stage for wheat and early grain filling stage > flowering stage > jointing stage for maize; (3) the reflectance of the red edge, green, and blue bands were the most important inputs for LCC modeling, and the optimal vegetation indices differed for the various growth stages and crops; and (4) all four algorithms maintained an acceptable accuracy with respect to LCC estimation, although random forest and support vector machine were slightly better. This study is valuable for the design of appropriate schemes for the spectral and scale issues of UAV sensors for LCC estimation regarding specific crop phenotype and phenology periods, and further boosts the applications of UAVs in precision agriculture.
Wanxue Zhu; Zhigang Sun; Ting Yang; Jing Li; Jinbang Peng; Kangying Zhu; Shiji Li; Huarui Gong; Yun Lyu; Binbin Li; Xiaohan Liao. Estimating leaf chlorophyll content of crops via optimal unmanned aerial vehicle hyperspectral data at multi-scales. Computers and Electronics in Agriculture 2020, 178, 105786 .
AMA StyleWanxue Zhu, Zhigang Sun, Ting Yang, Jing Li, Jinbang Peng, Kangying Zhu, Shiji Li, Huarui Gong, Yun Lyu, Binbin Li, Xiaohan Liao. Estimating leaf chlorophyll content of crops via optimal unmanned aerial vehicle hyperspectral data at multi-scales. Computers and Electronics in Agriculture. 2020; 178 ():105786.
Chicago/Turabian StyleWanxue Zhu; Zhigang Sun; Ting Yang; Jing Li; Jinbang Peng; Kangying Zhu; Shiji Li; Huarui Gong; Yun Lyu; Binbin Li; Xiaohan Liao. 2020. "Estimating leaf chlorophyll content of crops via optimal unmanned aerial vehicle hyperspectral data at multi-scales." Computers and Electronics in Agriculture 178, no. : 105786.
Stomata is an important channel for the exchange of water and gas between crops and the external environment. Stomatal behavior can shed light on how environmental stressors affect crop growth. Traditional stomata investigation methods involve experimental laboratory measurements of specific plants using stoma-related instruments or a field plot. However, developing a new remote sensing canopy resistance model for analyzing the stomatal behavior of crops under typical environmental stress is essential for regional agricultural management. Canopy resistance is a vital microscale–macroscale (from kilometers to micrometers) bridge that represents the stomatal behavior of the entire crop. In this study, a remote sensing canopy resistance model was proposed based on the Penman-Monteith model and a remote sensing evapotranspiration model. The model was verified by field-observed winter wheat data at the Yucheng Comprehensive Experiment Station (YCES) with dry-hot wind (DHW) and brackish water irrigation treatments from 2017 to 2019. The model measured canopy resistance (regression coefficient = 1.25; R2 = 0.98) well. Finally, remote sensing-based canopy resistance measurements were used to investigate the effects of soil salinity and dry-hot wind on the winter wheat. The results showed excellent model performance for retrieving crop stomatal behavior under two environmental stresses and proved that the proposed remote sensing canopy resistance model is a promising tool for the investigation of stomatal behavior under environmental stress; this is particularly relevant for regional precision farming.
Kangying Zhu; Zhigang Sun; Fenghua Zhao; Ting Yang; Zhenrong Tian; Jianbin Lai; Buju Long; Shiji Li. Remotely sensed canopy resistance model for analyzing the stomatal behavior of environmentally-stressed winter wheat. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 168, 197 -207.
AMA StyleKangying Zhu, Zhigang Sun, Fenghua Zhao, Ting Yang, Zhenrong Tian, Jianbin Lai, Buju Long, Shiji Li. Remotely sensed canopy resistance model for analyzing the stomatal behavior of environmentally-stressed winter wheat. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 168 ():197-207.
Chicago/Turabian StyleKangying Zhu; Zhigang Sun; Fenghua Zhao; Ting Yang; Zhenrong Tian; Jianbin Lai; Buju Long; Shiji Li. 2020. "Remotely sensed canopy resistance model for analyzing the stomatal behavior of environmentally-stressed winter wheat." ISPRS Journal of Photogrammetry and Remote Sensing 168, no. : 197-207.
Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) provides a new opportunity for land observation. This study is the first to compare and evaluate the performance of the only two spaceborne GNSS-R satellite missions whose data are publicly available, i.e., the UK’s TechdemoSat-1 (TDS-1) and the US’s Cyclone Global Navigation Satellite System (CYGNSS), for sensitivity analysis with SMAP SM on a daily basis and soil moisture (SM) estimates on a monthly basis over Mainland China. For daily sensitivity analysis, the two data were matched up and compared for the period (i.e., May 2017 through April 2018) when they coexisted (R = 0.561 vs R = 0.613). For monthly SM estimates, a back-propagation artificial neural network (BP-ANN) was used to construct a model using data from more than two years. The model was subsequently used to derive long-term and continuous SM maps over Mainland China. The results showed that TDS-1 and CYGNSS agree and correlate very well with the SMAP SM in Mainland China (R = 0.676, MAE = 0.052 m3m-3, and ubRMSE = 0.060 m3m-3 for TDS-1; R = 0.798, MAE = 0.040 m3m-3, and ubRMSE = 0.062 m3m-3 for CYGNSS). The retrieved results were further validated using monthly in situ SM data from dense sites across Mainland China. It was found that the SM derived from the TDS-1/ CYGNSS also correlated well with in situ SM (R = 0.687, MAE = 0.066 m3m-3, and ubRMSE = 0.056 m3m-3 for TDS-1; R = 0.724, MAE = 0.052 m3m-3, and ubRMSE = 0.053 m3m-3 for CYGNSS). The results in this study suggested that TDS-1/CYGNSS and the upcoming spaceborne GNSS-R mission could be new and powerful data sources to produce SM data set at a large scale and with relatively high precision.
Ting Yang; Wei Wan; Zhigang Sun; Baojian Liu; Sen Li; Xiuwan Chen. Comprehensive Evaluation of Using TechDemoSat-1 and CYGNSS Data to Estimate Soil Moisture over Mainland China. Remote Sensing 2020, 12, 1699 .
AMA StyleTing Yang, Wei Wan, Zhigang Sun, Baojian Liu, Sen Li, Xiuwan Chen. Comprehensive Evaluation of Using TechDemoSat-1 and CYGNSS Data to Estimate Soil Moisture over Mainland China. Remote Sensing. 2020; 12 (11):1699.
Chicago/Turabian StyleTing Yang; Wei Wan; Zhigang Sun; Baojian Liu; Sen Li; Xiuwan Chen. 2020. "Comprehensive Evaluation of Using TechDemoSat-1 and CYGNSS Data to Estimate Soil Moisture over Mainland China." Remote Sensing 12, no. 11: 1699.
Climate change has caused uneven changes in hydrological processes (precipitation and evapotranspiration) on a space-temporal scale, which would influence climate types, eventually impact agricultural production. Based on data from 61 meteorological stations from 1961 to 2014 in the North China Plain (NCP), the spatiotemporal characteristics of climate variables, such as humidity index, precipitation, and potential evapotranspiration (ET0), were analyzed. The sensitivity coefficients and contribution rates were applied to ET0. The NCP has experienced a semiarid to humid climate from north to south due to the significant decline of ET0 (−13.8 mm decade−1). In the study region, 71.0% of the sites showed a “pan evaporation paradox” phenomenon. Relative humidity had the most negative influence on ET0, while wind speed, sunshine hours, and air temperature had a positive effect on ET0. Wind speed and sunshine hours contributed the most to the spatiotemporal variation of ET0, followed by relative humidity and air temperature. Overall, the key climate factor impacting ET0 was wind speed decline in the NCP, particularly in Beijing and Tianjin. The crop yield in Shandong and Henan provinces was higher than that in the other regions with a higher humidity index. The lower the humidity index in Hebei province, the lower the crop yield. Therefore, potential water shortages and water conflict should be considered in the future because of spatiotemporal humidity variations in the NCP.
Wanlin Dong; Chao Li; Qi Hu; Feifei Pan; Jyoti Bhandari; Zhigang Sun. Potential Evapotranspiration Reduction and Its Influence on Crop Yield in the North China Plain in 1961–2014. Advances in Meteorology 2020, 2020, 1 -10.
AMA StyleWanlin Dong, Chao Li, Qi Hu, Feifei Pan, Jyoti Bhandari, Zhigang Sun. Potential Evapotranspiration Reduction and Its Influence on Crop Yield in the North China Plain in 1961–2014. Advances in Meteorology. 2020; 2020 ():1-10.
Chicago/Turabian StyleWanlin Dong; Chao Li; Qi Hu; Feifei Pan; Jyoti Bhandari; Zhigang Sun. 2020. "Potential Evapotranspiration Reduction and Its Influence on Crop Yield in the North China Plain in 1961–2014." Advances in Meteorology 2020, no. : 1-10.
The structure of the pig-raising sector in China is changing towards large-scale and intensive systems or ecological pig-raising systems (EPRSs). To choose the best EPRS with high economic benefits and with low environmental consequences, this study combined economic analysis and emergy analysis methods to evaluate several EPRSs. Having a large percentage of maize silage in the feed (max 40%) to replace some maize increased the economic benefit and sustainability of the EPRS and decreased the pressure on the environment. The raising system that consisted of Tuhe black pigs fed feed containing maize silage (EPRS C) performed especially well. The yield-based economic profit and area-based economic profit of EPRS C increased by 37%–54% and 3%–17%, respectively, compared to those of the three-breed crossbred pig-raising systems with or without maize silage added to the feed (EPRS A and EPRS B). Its unit emergy value and emergy loading ratio were 9–22% and 10–15% lower, respectively, than those of EPRS A and EPRS B. Furthermore, its emergy yield ratio and emergy sustainability index were about 2% and 14%–19% higher, respectively, than those of EPRS A and EPRS B. To some extent, the results from EPRS C give some guidelines on improving the performance of the ecological pig-raising sector in China. Moreover, using a high concentration of maize silage in the feed and an optimal local pig type may be beneficial for the sustainability of the ecological pig-raising sector in China.
Lyu Yun; Jing Li; Ruixing Hou; Zhigang Sun; Peifei Cong; Rubiao Liang; Sheng Hang; Huarui Gong; Zhu Ouyang. Emergy-Based Sustainability Analysis of an Ecologically Integrated Model with Maize Planting for Silage and Pig-Raising in the North China Plain. Sustainability 2019, 11, 6485 .
AMA StyleLyu Yun, Jing Li, Ruixing Hou, Zhigang Sun, Peifei Cong, Rubiao Liang, Sheng Hang, Huarui Gong, Zhu Ouyang. Emergy-Based Sustainability Analysis of an Ecologically Integrated Model with Maize Planting for Silage and Pig-Raising in the North China Plain. Sustainability. 2019; 11 (22):6485.
Chicago/Turabian StyleLyu Yun; Jing Li; Ruixing Hou; Zhigang Sun; Peifei Cong; Rubiao Liang; Sheng Hang; Huarui Gong; Zhu Ouyang. 2019. "Emergy-Based Sustainability Analysis of an Ecologically Integrated Model with Maize Planting for Silage and Pig-Raising in the North China Plain." Sustainability 11, no. 22: 6485.
Crop above-ground biomass (AGB) is a key parameter used for monitoring crop growth and predicting yield in precision agriculture. Estimating the crop AGB at a field scale through the use of unmanned aerial vehicles (UAVs) is promising for agronomic application, but the robustness of the methods used for estimation needs to be balanced with practical application. In this study, three UAV remote sensing flight missions (using a multiSPEC-4C multispectral camera, a Micasense RedEdge-M multispectral camera, and an Alpha Series AL3-32 Light Detection and Ranging (LiDAR) sensor onboard three different UAV platforms) were conducted above three long-term experimental plots with different tillage treatments in 2018. We investigated the performances of the multi-source UAV-based 3D point clouds at multi-spatial scales using the traditional multi-variable linear regression model (OLS), random forest (RF), backpropagation neural network (BP), and support vector machine (SVM) methods for accurate AGB estimation. Results showed that crop height (CH) was a robust proxy for AGB estimation, and that high spatial resolution in CH datasets helps to improve maize AGB estimation. Furthermore, the OLS, RF, BP, and SVM methods all maintained an acceptable accuracy for AGB estimation; however, the SVM and RF methods performed slightly more robustly. This study is expected to optimize UAV systems and algorithms for specific agronomic applications.
Wanxue Zhu; Zhigang Sun; Jinbang Peng; Yaohuan Huang; Jing Li; Junqiang Zhang; Bin Yang; Xiaohan Liao. Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales. Remote Sensing 2019, 11, 2678 .
AMA StyleWanxue Zhu, Zhigang Sun, Jinbang Peng, Yaohuan Huang, Jing Li, Junqiang Zhang, Bin Yang, Xiaohan Liao. Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales. Remote Sensing. 2019; 11 (22):2678.
Chicago/Turabian StyleWanxue Zhu; Zhigang Sun; Jinbang Peng; Yaohuan Huang; Jing Li; Junqiang Zhang; Bin Yang; Xiaohan Liao. 2019. "Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales." Remote Sensing 11, no. 22: 2678.
Leaf area index (LAI) is a key biophysical parameter for monitoring crop growth status, predicting crop yield, and quantifying crop variability in agronomic applications. Mapping the LAI at the field scale using multispectral cameras onboard unmanned aerial vehicles (UAVs) is a promising precision-agriculture application with specific requirements: The LAI retrieval method should be (1) robust so that crop LAI can be estimated with similar accuracy and (2) easy to use so that it can be applied to the adjustment of field management practices. In this study, three UAV remote-sensing missions (UAVs with Micasense RedEdge-M and Cubert S185 cameras) were carried out over six experimental plots from 2018 to 2019 to investigate the performance of reflectance-based lookup tables (LUTs) and vegetation index (VI)-based LUTs generated from the PROSAIL model for wheat LAI retrieval. The effects of the central wavelengths and bandwidths for the VI calculations on the LAI retrieval were further examined. We found that the VI-LUT strategy was more robust and accurate than the reflectance-LUT strategy. The differences in the LAI retrieval accuracy among the four VI-LUTs were small, although the improved modified chlorophyll absorption ratio index-lookup table (MCARI2-LUT) and normalized difference vegetation index-lookup table (NDVI-LUT) performed slightly better. We also found that both of the central wavelengths and bandwidths of the VIs had effects on the LAI retrieval. The VI-LUTs with optimized central wavelengths (red = 612 nm, near-infrared (NIR) = 756 nm) and narrow bandwidths (~4 nm) improved the wheat LAI retrieval accuracy (R2 ≥ 0.75). The results of this study provide an alternative method for retrieving crop LAI, which is robust and easy use for precision-agriculture applications and may be helpful for designing UAV multispectral cameras for agricultural monitoring.
Wanxue Zhu; Zhigang Sun; Yaohuan Huang; Jianbin Lai; Jing Li; Junqiang Zhang; Bin Yang; Binbin Li; Shiji Li; Kangying Zhu; Yang Li; Xiaohan Liao. Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs. Remote Sensing 2019, 11, 2456 .
AMA StyleWanxue Zhu, Zhigang Sun, Yaohuan Huang, Jianbin Lai, Jing Li, Junqiang Zhang, Bin Yang, Binbin Li, Shiji Li, Kangying Zhu, Yang Li, Xiaohan Liao. Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs. Remote Sensing. 2019; 11 (20):2456.
Chicago/Turabian StyleWanxue Zhu; Zhigang Sun; Yaohuan Huang; Jianbin Lai; Jing Li; Junqiang Zhang; Bin Yang; Binbin Li; Shiji Li; Kangying Zhu; Yang Li; Xiaohan Liao. 2019. "Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs." Remote Sensing 11, no. 20: 2456.
Zhigang Sun; Zhu Ouyang; Xubo Zhang; Wei Ren. A new global dataset of phase synchronization of temperature and precipitation: Its climatology and contribution to global vegetation productivity. Geoscience Data Journal 2019, 6, 126 -136.
AMA StyleZhigang Sun, Zhu Ouyang, Xubo Zhang, Wei Ren. A new global dataset of phase synchronization of temperature and precipitation: Its climatology and contribution to global vegetation productivity. Geoscience Data Journal. 2019; 6 (2):126-136.
Chicago/Turabian StyleZhigang Sun; Zhu Ouyang; Xubo Zhang; Wei Ren. 2019. "A new global dataset of phase synchronization of temperature and precipitation: Its climatology and contribution to global vegetation productivity." Geoscience Data Journal 6, no. 2: 126-136.
Winter wheat is one of the major cereal crops in the world. Monitoring and mapping its spatial distribution has significant implications for agriculture management, water resources utilization, and food security. Generally, winter wheat has distinguished phenological stages during the growing season, which form a unique EVI (Enhanced Vegetation Index) time series curve and differ considerably from other crop types and natural vegetation. Since early 2000, the MODIS EVI product has become the primary dataset for satellite-based crop monitoring at large scales due to its high temporal resolution, huge observation scope, and timely availability. However, the intraclass variability of winter wheat caused by field conditions and agricultural practices might lower the mapping accuracy, which has received little attention in previous studies. Here, we present a winter wheat mapping approach that integrates the variables derived from the MODIS EVI time series taking into account intraclass variability. We applied this approach to two winter wheat concentration areas, the state of Kansas in the U.S. and the North China Plain region (NCP). The results were evaluated against crop-specific maps or statistical data at the state/regional level, county level, and site level. Compared with statistical data, the accuracies in Kansas and the NCP were 95.1% and 92.9% at the state/regional level with R2 (Coefficient of Determination) values of 0.96 and 0.71 at the county level, respectively. Overall accuracies in confusion matrix were evaluated by validation samples in both Kansas (90.3%) and the NCP (85.0%) at the site level. Comparisons with methods without considering intraclass variability demonstrated that winter wheat mapping accuracies were improved by 17% in Kansas and 15% in the NCP using the improved approach. Further analysis indicated that our approach performed better in areas with lower landscape fragmentation, which may partly explain the relatively higher accuracy of winter wheat mapping in Kansas. This study provides a new perspective for generating multiple subclasses as training inputs to decrease the intraclass differences for crop type detection based on the MODIS EVI time series. This approach provides a flexible framework with few variables and fewer training samples that could facilitate its application to multiple-crop-type mapping at large scales.
Yanjun Yang; Bo Tao; Wei Ren; Demetrio P. Zourarakis; Bassil El Masri; Zhigang Sun; Qingjiu Tian. An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images. Remote Sensing 2019, 11, 1191 .
AMA StyleYanjun Yang, Bo Tao, Wei Ren, Demetrio P. Zourarakis, Bassil El Masri, Zhigang Sun, Qingjiu Tian. An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images. Remote Sensing. 2019; 11 (10):1191.
Chicago/Turabian StyleYanjun Yang; Bo Tao; Wei Ren; Demetrio P. Zourarakis; Bassil El Masri; Zhigang Sun; Qingjiu Tian. 2019. "An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images." Remote Sensing 11, no. 10: 1191.
Zhigang Sun. Responses to Reviewer’s Comments. 2019, 1 .
AMA StyleZhigang Sun. Responses to Reviewer’s Comments. . 2019; ():1.
Chicago/Turabian StyleZhigang Sun. 2019. "Responses to Reviewer’s Comments." , no. : 1.
Zhigang Sun. Please see the supplement PDF file for the whole manuscript. 2018, 1 .
AMA StyleZhigang Sun. Please see the supplement PDF file for the whole manuscript. . 2018; ():1.
Chicago/Turabian StyleZhigang Sun. 2018. "Please see the supplement PDF file for the whole manuscript." , no. : 1.
Zhigang Sun; Zhu Ouyang; Xubo Zhang; Wei Ren. Supplementary material to "A new global dataset of phase synchronization of temperature and precipitation: its climatology and contribution to global vegetation productivity". 2018, 1 .
AMA StyleZhigang Sun, Zhu Ouyang, Xubo Zhang, Wei Ren. Supplementary material to "A new global dataset of phase synchronization of temperature and precipitation: its climatology and contribution to global vegetation productivity". . 2018; ():1.
Chicago/Turabian StyleZhigang Sun; Zhu Ouyang; Xubo Zhang; Wei Ren. 2018. "Supplementary material to "A new global dataset of phase synchronization of temperature and precipitation: its climatology and contribution to global vegetation productivity"." , no. : 1.
Besides cumulative temperature and precipitation, the phase synchronization of temperature and precipitation also helps to regulate vegetation distribution and productivity across global lands. However, the phase synchronization has been rarely considered in previous studies related to climate and biogeography due to a lack of a robust and quantitative approach. In this study, we proposed a synchronization index of temperature and precipitation (SI-TaP) and then investigated its global spatial distribution, interannual fluctuation, and long-term trend derived from a global 60-year dataset of meteorological forcings. Further investigation was conducted to understand the relationship between SI-TaP and the annually summed Normalized Difference Vegetation Index (NDVI), which could be a proxy of terrestrial vegetation productivity. Results show differences in both spatial patterns and temporal variations between SI-TaP and air temperature and precipitation, but SI-TaP may help to explain the distribution and productivity of terrestrial vegetation. About 60 % of regions where annually summed NDVI is greater than half of its maximum value overlap regions where SI-TaP is greater than half of its maximum value. By using SI-TaP to explain vegetation productivity along with temperature and precipitation, the maximum increase in the coefficient of determination is 0.66 across global lands. Results from this study suggest that the proposed SI-TaP index is helpful to better understand climate change and its relation to the biota. Dataset available at http://www.dx.doi.org/10.11922/sciencedb.642 or http://www.sciencedb.cn/dataSet/handle/642.
Zhigang Sun; Zhu Ouyang; Xubo Zhang; Wei Ren. A new global dataset of phase synchronization of temperature and precipitation: its climatology and contribution to global vegetation productivity. Earth System Science Data Discussions 2018, 2018, 1 -16.
AMA StyleZhigang Sun, Zhu Ouyang, Xubo Zhang, Wei Ren. A new global dataset of phase synchronization of temperature and precipitation: its climatology and contribution to global vegetation productivity. Earth System Science Data Discussions. 2018; 2018 ():1-16.
Chicago/Turabian StyleZhigang Sun; Zhu Ouyang; Xubo Zhang; Wei Ren. 2018. "A new global dataset of phase synchronization of temperature and precipitation: its climatology and contribution to global vegetation productivity." Earth System Science Data Discussions 2018, no. : 1-16.
Light is one of the most important natural resources for plant growth. Light interception (LI) and use efficiency (LUE) are often affected by the structure of canopy caused by growing pattern and agronomy managements. Agronomy practices, such as the ridge–furrow system and plastic film cover, might affect the leaf morphology and then light transmission within the canopy, thus change light extinction coefficient (k), and LI and LUE. The objective of this study is to quantify LI and LUE in rain-fed maize (Zea Mays L.), a major cropping system in Northeast China, under different combinations of ridge–furrow and film covering ratios. The tested ridge–furrow system (DRF: “double ridges and furrows”) was asymmetric and alternated with wide ridge (0.70 m in width and 0.15 m in height), narrow furrow (0.10 m), narrow ridge (0.40 m in width and 0.20 m in height), and narrow furrow (0.10 m). Field experiments were conducted in 2013 and 2014 in Jilin Province, Northeast China. Four treatments were tested: no ridges and plastic film cover (control, NRF), ridges without film cover (DRF0), ridges with 58% film cover (DRF58), and ridges with 100% film cover (DRF100). DRF0 significantly increased LI by 9% compared with NRF, while film cover showed a marginal improvement. Specific leaf area in DRF experiments with film cover was significantly lower than in NRF, and leaf angle was 16% higher than in NRF, resulting in a 4% reduction in k. LUE of maize was not increased by DRF0, but was significantly enhanced by covering film in other DRF experiments, especially by 22% in DRF100. The increase of LUE by film cover was due to a greater biomass production and a lower assimilation portioning to vegetative organs, which caused a higher harvest index. The results could help farmers to optimize maize managements, especially in the region with decreased solar radiation under climate change.
Wanlin Dong; Hang Yu; Lizhen Zhang; Ruonan Wang; Qi Wang; Qingwu Xue; Zhihua Pan; Zhigang Sun; Xuebiao Pan. Asymmetric Ridge–Furrow and Film Cover Improves Plant Morphological Traits and Light Utilization in Rain-Fed Maize. Journal of Meteorological Research 2018, 32, 829 -838.
AMA StyleWanlin Dong, Hang Yu, Lizhen Zhang, Ruonan Wang, Qi Wang, Qingwu Xue, Zhihua Pan, Zhigang Sun, Xuebiao Pan. Asymmetric Ridge–Furrow and Film Cover Improves Plant Morphological Traits and Light Utilization in Rain-Fed Maize. Journal of Meteorological Research. 2018; 32 (5):829-838.
Chicago/Turabian StyleWanlin Dong; Hang Yu; Lizhen Zhang; Ruonan Wang; Qi Wang; Qingwu Xue; Zhihua Pan; Zhigang Sun; Xuebiao Pan. 2018. "Asymmetric Ridge–Furrow and Film Cover Improves Plant Morphological Traits and Light Utilization in Rain-Fed Maize." Journal of Meteorological Research 32, no. 5: 829-838.
Pan evaporation, as a straightforward proxy of potential evapotranspiration, has consistently decreased during the last several decades in many regions across the globe mainly because of global dimming and / or decreases in wind speed. Based on a robust measurement dataset of 30-year large-pan evaporation, however, we found that recent increasing extreme climate events have reversed the decreasing trend in pan evaporation by the Lower Yellow River, with a significant increase of up to 25.2 mm/yr during 2008-2014. Further analyses show that the decrease in large-pan evaporation during 1985-2008 (-5.1 mm/yr) was mainly caused by the decreased vapor pressure deficit (VPD) and sunshine hours. While the sharp increase in large-pan evaporation after 2008 was mainly caused by more frequent heat wave and drought events in spring and summer, which resulted in concurrent increases in air temperature, VPD, and sunshine hours. Our finding of evaporation rebound due to heat wave and drought calls for improved strategies of water and crop managements for mitigating the negative effects of increasing extreme climate events.
Zhigang Sun; Zhu Ouyang; Junfang Zhao; Shiji Li; Xubo Zhang; Wei Ren. Recent rebound in observational large-pan evaporation driven by heat wave and droughts by the Lower Yellow River. Journal of Hydrology 2018, 565, 237 -247.
AMA StyleZhigang Sun, Zhu Ouyang, Junfang Zhao, Shiji Li, Xubo Zhang, Wei Ren. Recent rebound in observational large-pan evaporation driven by heat wave and droughts by the Lower Yellow River. Journal of Hydrology. 2018; 565 ():237-247.
Chicago/Turabian StyleZhigang Sun; Zhu Ouyang; Junfang Zhao; Shiji Li; Xubo Zhang; Wei Ren. 2018. "Recent rebound in observational large-pan evaporation driven by heat wave and droughts by the Lower Yellow River." Journal of Hydrology 565, no. : 237-247.