Victoria I. S. Lenz-Wiedemann

Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice

Abstract Appropriate field management requires methods of measuring plant height with high precision, accuracy, and resolution. Studies show that terrestrial laser scanning (TLS) is suitable for capturing small objects like crops. In this contribution, the results of multitemporal TLS surveys for monitoring plant height on paddy rice fields in China are presented. Three campaigns were carried out on a field experiment and on a farmer’s conventionally managed field. The high density of measurement points allows us to establish crop surface models with a resolution of 1 cm, which can be used for deriving plant heights. For both sites, strong correlations (each R 2 = 0.91 between TLS-derived and manually measured plant heights confirm the accuracy of the scan data. A biomass regression model was established based on the correlation between plant height and biomass samples from the field experiment ( R 2 = 0.86 ). The transferability to the farmer’s field was supported with a strong correlation between simulated and measured values ( R 2 = 0.90 ). Independent biomass measurements were used for validating the temporal transferability. The study demonstrates the advantages of TLS for deriving plant height, which can be used for modeling biomass. Consequently, laser scanning methods are a promising tool for precision agriculture.

Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China

Rice farming in Northeast China is crucially important for China’s food security and sustainable development. A key challenge is how to optimize nitrogen (N) management to ensure high yield production while improving N use efficiency and protecting the environment. Handheld chlorophyll meter (CM) and active crop canopy sensors have been used to improve rice N management in this region. However, these technologies are still time consuming for large-scale applications. Satellite remote sensing provides a promising technology for large-scale crop growth monitoring and precision management. The objective of this study was to evaluate the potential of using FORMOSAT-2 satellite images to diagnose rice N status for guiding topdressing N application at the stem elongation stage in Northeast China. Five farmers’ fields (three in 2011 and two in 2012) were selected from the Qixing Farm in Heilongjiang Province of Northeast China. FORMOSAT-2 satellite images were collected in late June. Simultaneously, 92 field samples were collected and six agronomic variables, including aboveground biomass, leaf area index (LAI), plant N concentration (PNC), plant N uptake (PNU), CM readings and N nutrition index (NNI) defined as the ratio of actual PNC and critical PNC, were determined. Based on the FORMOSAT-2 imagery, a total of 50 vegetation indices (VIs) were computed and correlated with the field-based agronomic variables. Results indicated that 45% of NNI variability could be explained using Ratio Vegetation Index 3 (RVI3) directly across years. A more practical and promising approach was proposed by using satellite remote sensing to estimate aboveground biomass and PNU at the panicle initiation stage and then using these two variables to estimate NNI indirectly (R2 = 0.52 across years). Further, the difference between the estimated PNU and the critical PNU can be used to guide the topdressing N application rate adjustments.

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

Rice is a primary staple food for the world population and there is a strong need to map its cultivation area and monitor its crop status on regional scales. This study was conducted in the Qixing Farm County of the Sanjiang Plain, Northeast China. First, the rice cultivation areas were identified by integrating the remote sensing (RS) classification maps from three dates and the Geographic Information System (GIS) data obtained from a local agency. Specifically, three FORMOSAT-2 (FS-2) images captured during the growing season in 2009 and a GIS topographic map were combined using a knowledge-based classification method. A highly accurate classification map (overall accuracy = 91.6%) was generated based on this Multi-Data-Approach (MDA). Secondly, measured agronomic variables that include biomass, leaf area index (LAI), plant nitrogen (N) concentration and plant N uptake were correlated with the date-specific FS-2 image spectra using stepwise multiple linear regression models. The best model validation results with a relative error (RE) of 8.9% were found in the biomass regression model at the phenological stage of heading. The best index of agreement (IA) value of 0.85 with an RE of 13.6% was found in the LAI model, also at the heading stage. For plant N uptake estimation, the most accurate model was again achieved at the heading stage with an RE of 11% and an IA value of 0.77; however, for plant N concentration estimation, the model performance was best at the booting stage. Finally, the regression models were applied to the identified rice areas to map the within-field variability of the four agronomic variables at different growth stages for the Qixing Farm County. The results provide detailed spatial information on the within-field variability on a regional scale, which is critical for effective field management in precision agriculture.

Potential of RapidEye and WorldView-2 Satellite Data for Improving Rice Nitrogen Status Monitoring at Different Growth Stages

For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large areas within a short time frame. In recent studies, the FORMOSAT-2 satellite images with traditional blue (B), green (G), red (R), and near-infrared (NIR) wavebands have been used to estimate rice N status due to its high spatial resolution, daily revisit capability, and relatively lower cost. This study aimed to evaluate the potential improvements of RapidEye and WorldView-2 data over FORMOSAT-2 for rice N status monitoring, as the former two sensors provide additional wavelengths besides the traditional four wavebands. Ten site-year N rate experiments were conducted in Jiansanjiang, Heilongjiang Province of Northeast China from 2008 to 2011. Plant samples and field hyperspectral data were collected at three growth stages: panicle initiation (PI), stem elongation (SE), and heading (HE). The canopy-scale hyperspectral data were upscaled to simulate the satellite bands. Vegetation index (VI) analysis, stepwise multiple linear regression (SMLR), and partial least squares regression (PLSR) were performed to derive plant N status indicators. The results indicated that the best-performed VIs calculated from the simulated RapidEye and WorldView-2 bands, especially those based on the red edge (RE) bands, explained significantly more variability for above ground biomass (AGB), plant N uptake (PNU), and nitrogen nutrition index (NNI) estimations than their FORMOSAT-2-based counterparts did, especially at the PI and SE stages. The SMLR and PLSR models based on the WorldView-2 bands generally had the best performance, followed by the ones based on the RapidEye bands. The SMLR results revealed that both the NIR and RE bands were important for N status estimation. In particular, the NIR1 band (760–900 nm from RapidEye or 770–895 nm from WorldView-2) was most important for estimating all the N status indicators. The RE band (690–730 nm or 705–745 nm) improved AGB, PNU, and NNI estimations at all three stages, especially at the PI and SE stages. AGB and PNU were best estimated using data across the stages while plant N concentration (PNC) and NNI were best estimated at the HE stage. The PLSR analysis confirmed the significance of the NIR1 band for AGB, PNU, and NNI estimations at all stages except for the HE stage. It also showed the importance of including extra bands (coastal, yellow, and NIR2) from the WorldView-2 sensor for N status estimation. Overall, both the RapidEye and WorldView-2 data with RE bands improved the results relative to FORMOSAT-2 data. However, the WorldView-2 data with three extra bands in the visible and NIR regions showed the highest potential in estimating rice N status.

Proximal fluorescence sensing for in-season diagnosis of rice nitrogen status

The objective of this study was to evaluate the potential of using Multiplex 3, a hand-held canopy fluorescence sensor, to determine rice nitrogen (N) status at different growth stages. In 2013, a paddy rice field experiment with five N fertilizer treatments and two varieties was conducted in Northeast China. Field samples and fluorescence data were collected simultaneously at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. Four N status indicators, leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU) and N nutrition index (NNI), were determined. The preliminary results indicated that different N application rates significantly affected most of the fluorescence variables, especially the simple fluorescence ratios (SFR_G, SFR_R), flavonoid (FLAV), and N balance indices (NBI_G, NBI_R). These variables were highly correlated with N status indicators. More studies are needed to further evaluate the accuracy of rice N status diagnosis using fluorescence sensing at different growth stages.

CO2 Fluxes and Transpiration

Plants play a key controlling role within the hydrological cycle. For analysing global change impacts on water resources in the Upper Danube basin, coupled and process-based modelling of vegetation water and carbon fluxes is needed. The model component Biological is part of the simulation system DANUBIA and calculates the processes of carbon assimilation and transpiration for various vegetation categories (e.g. grassland, winter wheat, sugar beet and maize). To best depict the complex interplay of water, carbon and nitrogen fluxes in agroecosystems, decisions on crop management are included in the modelling. Additionally, meteorological and pedological model input data are provided by other dynamically coupled DANUBIA model components. Modelling of photosynthesis and transpiration takes into account not only the predicted increases in air temperature and atmospheric CO2 concentration but also the availability of water and nitrogen. Maps of transpiration totals for one hydrological year are presented for several agricultural land uses in the Upper Danube basin. Local conditions, characteristics of the different vegetation categories and differences in management are shown. In this way, spatial and temporal changes in plant water demand and supply under global change conditions and altered cultivation practices are assessed at the regional scale.

Plant Growth and Biomass Production

In the Upper Danube basin, plant growth and biomass production strongly influence water, carbon and nitrogen fluxes. The model component Biological is part of the simulation system DANUBIA and calculates plant growth and biomass production for various vegetation categories (e.g. grassland, winter wheat, sugar beet and maize). An ecohydrological model approach is needed to account for the interactions of water, carbon and nitrogen fluxes in the soil-plant-atmosphere system. Meteorological and pedological model input data are provided by dynamically coupled DANUBIA model components. When analysing global change effects on agroecosystems, it is crucial to consider agricultural decisions such as type of use and management. Biological uses this information as input data from the coupled Farming actor model. In turn, the modelled biomass production and yield data are needed by Farming for the selection of crops to be cultivated. For the model validation analysis, measured and modelled values were compared for several test fields covering a wide range of meteorological and pedological conditions. On the district scale, agricultural statistics were used for validation. Maps of biomass production for 1 year are shown for selected field crops and managed grassland in the Upper Danube basin. Within DANUBIA, spatial and temporal changes in plant growth and biomass production for the past, present and future are assessed at the regional scale.

Effects of Future Climate Changes on Yields, Land Use and Agricultural Incomes

Dynamically coupled model runs of the DANUBIA components Biological, SNT, NaturalEnvironment and Farming were performed to estimate the effects of climate change on crop yields, agricultural land use and income. Calculations are based on a GLOWA-Danube scenario including the climate trend REMO regional, the climate variant Baseline and the societal scenario Baseline. Results of the scenario calculation are compared with the reference period for four sample districts, which represent different site conditions within the drainage basin. In general, the scenario results show an increase in yields for the considered groups of crops. However, changes of individual crops within these groups differ between the districts. All districts have an increase in income that at the beginning of the scenario period is mainly caused by the increase in premium payments of the CAP compared to the reference period. The further income increase at the end of the scenario period, which is significantly higher in districts with a higher proportion of arable land, can be attributed to the increase in yields. With respect to land use, all districts show a decrease in forage crops and an increase in the cultivation of cereals. Overall, the results indicate that no negative impacts on the productivity of the agricultural land and the income situation of the farms are to be expected up to the middle of the century.