Pengfei Chen

Critical Nitrogen Curve and Remote Detection of Nitrogen Nutrition Index for Corn in the Northwestern Plain of Shandong Province, China

The nitrogen nutrition index (NNI) is calculated from the measured N concentration and the critical nitrogen (N) curve. It can be used to determine the N required by a crop and is helpful for optimizing N application in the field. Our objectives were to validate the existing corn critical N curve for the northwestern plain of Shandong Province and to design a more accurate remote detection method for the NNI. For this purpose, field measurements were conducted weekly to acquire the biomass and N concentrations during the corn growing season of 2011. Additionally, nearly 60 corn canopy spectra were collected during field campaigns. First, limiting and non-limiting N points were selected from sampled data, and they were used to validate the existing critical N curve. Second, an NNI estimation model based on a Principal Component Analysis method and Back Propagation Artificial Neural Network (PCA-BP-ANN) model was established. The collected canopy spectra and corresponding NNI were used to compare the performances of the above mentioned method and other for NNI estimation. The results showed that the N curve proposed in the literature is suitable for the study region. Among the three remote detection methods, PCA-BP-ANN provided the best results with highest R value and lowest root mean square error value.

Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat

Continuous monitoring leaf area index (LAI) of field crops in a growing season has a great challenge. The development of remote sensing technology provides a good tool for timely mapping LAI regionally. In this study, hyperspectral reflectance data (405-835 nm) obtained from an airborne hyperspectral imager (Pushbroom Hyperspectral Imager) were used to model LAI of winter wheat canopy in the 2002 crop growing season. LAI was modeled based on its semi-empirical relationships with six vegetation indices (VIs), including ratio vegetation index (RVI), modified simple ratio index (MSR), normalized difference vegetation index (NDVI), a newly proposed index NDVI-like (which resembles NDVI), modified triangular vegetation index (MTVI2), and modified soil adjusted vegetation index (MSAVI). To assess the performance of these VIs, root mean square errors (RMSEs) and determination coefficient (R2) between estimated LAI and measured LAI were reported. Our result showed that NDVI-like was the most accurate predictor of LAI. The inclusion of a green band in MTVI2 trended to give a rise to a much quicker saturation with increase of LAI (e.g., over 3.5). MSAVI and MTVI2 showed comparable but lower potential than NDVI-like in estimating LAI. RVI and MSR demonstrated their lowest prediction accuracy, implying that they are more likely to be affected by environmental conditions such as atmosphere and cloud, thus cannot properly reflect the properties of winter wheat canopy. Our results support the use of VIs for a quick assessment of seasonal variations in winter wheat LAI. Among the indices we tested in this study, the newly developed NDVI-like model created the most accurate and reliable results.

A Comparison of Two Approaches for Estimating the Wheat Nitrogen Nutrition Index Using Remote Sensing

Remote predictions of the nitrogen nutrition index (NNI) are useful for precise nitrogen (N) management in the field. Several studies have recommended two methods for estimating the NNI, which are classified as mechanistic and semi-empirical methods in this study. However, no studies have been conducted to thoroughly analyze and compare these two methods. Using winter wheat as an example, this study compared the performances of these two methods for estimating the NNI to determine which method is more suitable for practical use. Field measurements were conducted to determine the above ground biomass, N concentration and canopy spectra during different wheat growth stages in 2012. Nearly 120 samples of data were collected and divided into different calibration and validation datasets (containing data from single or multi-growth stages). Based on the above datasets, the performances of the two NNI estimation methods were compared, and the influences of phenology on the methods were analyzed. All models that used the mechanistic method with different calibration datasets performed well when validated by validation datasets containing single growth or multi-growth stage data. The validation results had R2 values between 0.82 and 0.94, root mean square error (RMSE) values between 0.05 and 0.17, and RMSE% values between 5.10% and 14.41%. Phenology had no effect on this type of NNI estimation method. However, the semi-empirical method was influenced by phenology. The performances of the models established using this method were determined by the type of data used for calibration. Thus, the mechanistic method is recommended as a better method for estimating the NNI. By combining proper N management strategies, it can be used for precise N management.

Deriving Maximum Light Use Efficiency From Crop Growth Model and Satellite Data to Improve Crop Biomass Estimation

Maximum light use efficiency (LUEmax) is an important parameter in biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable crop LUEmax from satellite remote sensing data. LUEmax at the plot level was retrieved first by assimilating field measured green leaf area index and biomass into a crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. LUEmax was then mapped using the best regression model from a VI. The influence factors on LUEmax variability were also assessed. Contrary to a fixed LUEmax, our results suggest that LUEmax is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level LUEmax and VIs, particularly the two-band enhanced vegetation index for winter wheat (Triticum aestivum) and the green chlorophyll index for maize (Zea mays) at the milk stage, provided a potential to derive LUEmax from remote sensing observations. To evaluate the quality of LUEmax derived from remote sensing data, biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant LUEmax and the derived variable LUEmax. Significant improvements in biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable LUEmax . This study offers a new way to derive LUEmax for a specific PEM and to improve the accuracy of biomass estimation using remote sensing.

Effects of land use change on the spatiotemporal variability of soil organic carbon in an urban-rural ecotone of Beijing, China

Understanding the effects of land use changes on the spatiotemporal variation of soil organic carbon (SOC) can provide guidance for low carbon and sustainable agriculture. In this paper, based on the large-scale datasets of soil surveys in 1982 and 2009 for Pinggu District an urban-rural ecotone of Beijing, China, the effects of land use and land use changes on both temporal variation and spatial variation of SOC were analyzed. Results showed that from 1982 to 2009 in Pinggu District, the following land use change mainly occurred: Grain cropland converted to orchard or vegetable land, and grassland converted to forestland. The SOC content decreased in region where the land use type changed to grain cropland (e.g., vegetable land to grain cropland decreased by 0.7 g kg-1; orchard to grain cropland decreased by 0.2 g kg(-1)). In contrast, the SOC content increased in region where the land use type changed to either orchard (excluding forestland) or forestland (e.g., grain cropland to orchard and forestland increased by 2.7 and 2.4 g kg(-1), respectively; grassland to orchard and forestland increased by 4.8 and 4.9 g kg(-1), respectively). The organic carbon accumulation capacity per unit mass of the soil increased in the following order: grain cropland soil<vegetable land/grassland soil<orchard soil<forestland soil. Therefore, to both secure supply of agricultural products and develop low carbon agriculture in a modern city, orchard has proven to be a good choice for land using.

Effects of two kinds of variable‐rate nitrogen application strategies on the production of winter wheat (Triticum aestivum)

Abstract To improve nitrogen‐use efficiency (NUE) is crucial to agriculture; it benefits agricultural production and reduces the impact on the environment. In past decades, a lot of variable‐rate nitrogen (VRN) application strategies have been proposed to improve NUE. The concern of this study is whether the specific N management strategy based on using in‐season predicted grain yield (ISPGY) and in‐season N uptake (ISNU) is more efficient than the VRN strategy based on grain yield goal (GYG) and ISNU. For this purpose, 2‐year (2005–06 and 2006–07) winter wheat (Triticum aestivum) experiments with the cultivar ‘Jingdong8’ were conducted at the China National Experimental Station for Precision Agriculture, located in the Changping district of Beijing, China. Four VRN application methods, SPAD chlorophyll meter method 1 (SCM1), SPAD chlorophyll meter method 2 (SCM2), vegetation index method 1 (VI1), and vegetation index method 2 (VI2), were compared with a random block design with 10 replications. The differences between SCM1 and SCM2 and between VI1 and VI2 were used to estimate the potential grain yield for each plot. SCM1 and VI1 used ISPGY, whereas SCM2 and VI2 adopted GYG. Economic benefits and soil residual NO3‐N were analysed for the four methods. The results showed that the SCM2 and V12 performed better than the corresponding SCM1 and VI1, indicating that the GYG‐based VRN strategy is better than the ISPGY‐based VRN strategy for conducting specific N management.

Estimating Winter Wheat Leaf Area Index From Ground and Hyperspectral Observations Using Vegetation Indices

Growing numbers of studies have focused on evaluating the ability of vegetation indices (VIs) to predict biophysical parameters such as leaf area index (LAI) and chlorophyll. In this study, empirical models were used to estimate winter wheat LAI based on three spectral indices [the normalized difference vegetation index (NDVI), the modified simple ratio index (MSR), and the modified soil-adjusted vegetation index (MSAVI)], and three band-selection approaches (the conventional approach, the red edge approach, and the best correlated approach), which were used to calculate VIs. The aim was to enhance the relationships between the indices and LAI values by improving the band-selection approaches so as to produce a suitable VI for winter wheat LAI estimation. Using hyperspectral airborne data and ground-measured spectra as well as ground LAI measurements collected during two field campaigns, winter wheat LAIs were estimated and validated using different VIs calculated by different band combinations. Our results showed that the MSAVI provided the best LAI estimations when using ground measured spectra with R2 over 0.74 and RMSE less than 0.98. The NDVI provided the most robust estimation results across different sites, years, and sensors, although it was not adequate for LAI estimation of moderately dense canopies due to the saturation that occurred when LAI ) 3. The MSR demonstrated more severe scattering and lower predictive accuracy than the NDVI and, therefore, was not a perfect solution to the saturation issue. In addition, it was also shown that the best correlated approach improved the predictive power of the indices and revealed the importance of red edge bands for LAI estimation; meanwhile, the red edge approach (based on the reflectance at 705 and 750 nm) was not always superior to the conventional approach (based on the reflectance at 670 and 800 nm). The results were promising and should facilitate the use of VIs in crop LAI measurements.

The impact of climate change on summer maize phenology in the northwest plain of Shandong province under the IPCC SRES A1B scenario

Climate change will affect agricultural production. Combining a climate model and a crop growth model furnishes a good approach for analyzing this effect quantitatively. The purpose of this study is to analyze the effect of climate change on summer maize phenology in northwest Shandong province under the A1B climate scenario using a regional climate model and the CERES-Maize growth model. The results showed that the temperature would increase significantly during the maize growth season in the study region, that the increased temperature would shorten the maize growth stage and result in a potential yield loss using the current cultivar, and that it is critical to breed a heat-resistant and late-maturing cultivar to maintain the yield.

A New Regionalization Scheme for Effective Ecological Restoration on the Loess Plateau in China

To prevent potentially unsuitable activities during vegetation restoration, it is important to examine the impact of historical restoration activities on the target ecological system to inform future restoration policies. Taking the Loess Plateau of China as an example, a regionalization method and corresponding scheme were proposed to select suitable vegetation types (forested lands, woody grasslands/bushlands, grasslands, or xerophytic shrublands and semi-shrublands) for a given location using remote sensing technology in order to analyze the vegetation growth status before and after the largest ecological conservation project in the country: The Grain for Green Program (GTGP). To design the scheme, remote sensing data covering the periods before and after the implementation of the GTGP (the 1980s and 2001–2013) were collected, along with soil, meteorological, and topographic data. The net primary production (NPP) values for 2001–2013 were calculated using the Carnegie-Ames-Stanford Approach (CASA) model. Locations representing the native vegetation and the restored vegetation were first recognized using maps of vegetation cover. Then, for the restored vegetation area, the places suitable for planting the covered vegetation type were selected by comparing the NPP value of the corresponding vegetation type in the native vegetation area to the NPP value in the site under consideration. Third, half of these sites were uniformly selected based on their NPP value, and these areas and the native vegetation area were used as training regions. Based on weather, soil, and topographic data, a new regionalization scheme was designed using standardized Euclidean distances. Finally, data from the remainder of the Loess Plateau were used to validate the new regionalization scheme, which was also compared to an existing Chinese eco-geographical regionalization scheme. The results showed that the new regionalization scheme performed well, with an average potential classification accuracy of 81.81%. Compared with the eco-geographical regionalization scheme, the new scheme exhibited improved the consistency of vegetation dynamics, reflecting the potential to better guide vegetation restoration activities on the Loess Plateau.