Search

Average leaf inclination angle (θ¯L) is an important canopy structure variable that influences light regime, photosynthesis, and evapotranspiration of plants. θ¯L can be measured through direct methods (e.g., protractor), which are labor-intensive and time-consuming, or through indirect optical instruments, which are more efficient than the direct methods. However, uncertainties of different indirect optical instruments for quantifying θ¯L remain largely unquantified. In this study, we evaluated and compared the performances of three major indirect optical instruments: (1) LAI-2200, (2) 30°-tilted camera, and (3) digital hemispherical photography (DHP), in different crop fields over a growing season, benchmarked with direct measurements. LAI-2200 and 30°-tilted camera showed higher agreement with direct θ¯ measurements (R2 = 0.54, RMSE = 7.37°; R2 = 0.58, RMSE = 8.08°) than DHP (R2 = 0.14, RMSE = 13.96°). Different performances of indirect optical instruments could be attributed to the accuracy of gap fraction measurement and the performance of the θ¯L quantification algorithms. When using the LAI-2200 algorithm, larger gap fraction gradients over view zenith angles led to larger θ¯L values, and smaller gap fraction gradients led to smaller θ¯L values. Such error propagation was larger in sparse canopy than in dense canopy. The Wilson G function of the LAI-2200 algorithm performed better in estimating θ¯L than the G function based on the ellipsoidal LAD function used by the CAN_EYE algorithm. We also proposed a modification of the LAI-2200 algorithm, which further improved the performance of LAI-2200 and 30°-tilted cameras in estimating θ¯L. We envision that the low-cost 30°-tilted cameras provide a promising sensor solution to continuously monitor canopy structure for various ecosystems.

This study aims to evaluate the effects of the spatiotemporal patterns of land-use and land-cover (LULC) changes on the dynamics of carbon storage in a tropical region of China by linking the trajectory analysis of LULC changes and the InVEST model. Based on remote sensing (RS), geographic information system (GIS) and change trajectories, the spatiotemporal evolution of LULC changes was explored. This evolution could be coupled with the spatiotemporal LULC change trajectories and the InVEST model for the quantitative study of the spatial distribution and temporal variation in regional carbon stocks. The results showed that during the 2000–2020 period, the built-up land continually increased to 206.05 km2 through urban expansion, and forestland became the dominant type of land, with an area of 357.39 km2. In addition to the change in land use, the carbon storage in the study region increased by 4.87 Tg C. The anaphasic trajectory had the largest area ratio at 7.05% in the total area, while the prophasic trajectory contributed to the largest increase in carbon storage, 5.87 Tg C. Moreover, the repetitive trajectories had no impact on carbon sinks and sources, whereas the anaphasic trajectory and the continual trajectory imposed passive impacts on carbon storage. These advances in research underpin scientific efforts to improve the understanding of the relationship between the optimization of land-use structure and patterns and the carbon storage service in the Nandu River Basin.

Nitrogen is an essential nutrient that directly affects plant photosynthesis, crop yield, and biomass production for bioenergy crops, but excessive application of nitrogen fertilizers can cause environmental degradation. To achieve sustainable nitrogen fertilizer management for precision agriculture, there is an urgent need for nondestructive and high spatial resolution monitoring of crop nitrogen and its allocation to photosynthetic proteins as that changes over time. Here, we used visible to shortwave infrared (400–2400 nm) airborne hyperspectral imaging with high spatial (0.5 m) and spectral (3–5 nm) resolutions to accurately estimate critical crop traits, i.e., nitrogen, chlorophyll, and photosynthetic capacity (CO2-saturated photosynthesis rate, Vmax,27), at leaf and canopy scales, and to assess nitrogen deficiency on crop yield. We conducted three airborne campaigns over a maize (Zea mays L.) field during the growing season of 2019. Physically based soil-canopy Radiative Transfer Modeling (RTM) and data-driven approaches i.e. Partial-Least Squares Regression (PLSR) were used to retrieve crop traits from hyperspectral reflectance, with ground truth of leaf nitrogen, chlorophyll, Vmax,27, Leaf Area Index (LAI), and harvested grain yield. To improve computational efficiency of RTMs, Random Forest (RF) was used to mimic RTM simulations to generate machine learning surrogate models RTM-RF. The results show that prior knowledge of soil background and leaf angle distribution can significantly reduce the ill-posed RTM retrieval. RTM-RF achieved a high accuracy to predict leaf chlorophyll content (R2 = 0.73) and LAI (R2 = 0.75). Meanwhile, PLSR exhibited better accuracy to predict leaf chlorophyll content (R2 = 0.79), nitrogen concentration (R2 = 0.83), nitrogen content (R2 = 0.77), and Vmax,27 (R2 = 0.69) but required measured traits for model training. We also found that canopy structure signals can enhance the use of spectral data to predict nitrogen related photosynthetic traits, as combining RTM-RF LAI and PLSR leaf traits well predicted canopy-level traits (leaf traits × LAI) including canopy chlorophyll (R2 = 0.80), nitrogen (R2 = 0.85) and Vmax,27 (R2 = 0.82). Compared to leaf traits, we further found that canopy-level photosynthetic traits, particularly canopy Vmax,27, have higher correlation with maize grain yield. This study highlights the potential for synergistic use of process-based and data-driven approaches of hyperspectral imaging to quantify crop traits that facilitate precision agricultural management to secure food and bioenergy production.

Sun-induced chlorophyll fluorescence (SIF) measurements have shown unique potential for quantifying plant physiological stress. However, recent investigations found canopy structure and radiation largely control SIF, and physiological relevance of SIF remains yet to be fully understood. This study aims to evaluate whether the SIF-derived physiological signal improves quantification of crop responses to environmental stresses, by analyzing data at three different spatial scales within the U.S. Corn Belt, i.e. experiment plot, field, and regional scales, where ground-based portable, stationary and space-borne hyperspectral sensing systems are used, respectively. We found that, when controlling for variations in incoming radiation and canopy structure, crop SIF signals can be decomposed into non-physiological (i.e. canopy structure and radiation, 60% ∼ 82%) and physiological information (i.e. physiological SIF yield, Φ _F , 17% ∼ 31%), which confirms the contribution of physiological variation to SIF. We further evaluated whether Φ _F indicated plant responses under high-temperature and high vapor pressure deficit (VPD) stresses. The plot-scale data showed that Φ _F responded to the proxy for physiological stress (partial correlation coefficient, r _p = 0.40, p < 0.001) while non-physiological signals of SIF did not respond ( p > 0.1). The field-scale Φ _F data showed water deficit stress from the comparison between irrigated and rainfed fields, and Φ _F was positively correlated with canopy-scale stomatal conductance, a reliable indicator of plant physiological condition (correlation coefficient r = 0.60 and 0.56 for an irrigated and rainfed sites, respectively). The regional-scale data showed Φ _F was more strongly correlated spatially with air temperature and VPD ( r = 0.23 and 0.39) than SIF ( r = 0.11 and 0.34) for the U.S. Corn Belt. The lines of evidence suggested that Φ _F reflects crop physiological responses to environmental stresses with greater sensitivity to stress factors than SIF, and the stress quantification capability of Φ _F is spatially scalable. Utilizing Φ _F for physiological investigations will contribute to improve our understanding of vegetation responses to high-temperature and high-VPD stresses.

Substantial uncertainty exists in daily and sub-daily gross primary production (GPP) estimation, which dampens accurate monitoring of the global carbon cycle. Here we find that near-infrared radiance of vegetation (NIR _v,Rad ), defined as the product of observed NIR radiance and normalized difference vegetation index, can accurately estimate corn and soybean GPP at daily and half-hourly time scales, benchmarked with multi-year tower-based GPP at three sites with different environmental and irrigation conditions. Overall, NIR _v,Rad explains 84% and 78% variations of half-hourly GPP for corn and soybean, respectively, outperforming NIR reflectance of vegetation (NIR _v,Ref ), enhanced vegetation index (EVI), and far-red solar-induced fluorescence (SIF _760 ). The strong linear relationship between NIR _v,Rad and absorbed photosynthetically active radiation by green leaves (APAR _green ), and that between APAR _green and GPP, explain the good NIR _v,Rad -GPP relationship. The NIR _v,Rad -GPP relationship is robust and consistent across sites. The scalability and simplicity of NIR _v,Rad indicate a great potential to estimate daily or sub-daily GPP from high-resolution and/or long-term satellite remote sensing data.