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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.

Abstract Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF760) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.

The National Park of Hainan Tropical Rainforest (NPHTR) plays the crucial role of an ecological barrier for Hainan Island. It also serves as a valuable repository of tropical biological heritage for the world. The scientific establishment of an ecological security pattern (ESP) for the NPHTR is therefore essential for ensuring the robustness and stability of this regional ecosystem, safeguarding tropical biodiversity, and promoting sustainable development. This study integrates ecosystem service functions (ESFs) and ecological sensitivity to identify ecological sources (ESs) so that regional integrity is promoted and ecological security is ensured. Ecological corridors (ECs) are established on the basis of a minimum cumulative resistance (MCR) model and circuit theory. We integrate ESs, nodes, and corridors to construct the ESP of the NPHTR. The areas of extreme importance and sensitivity in the NPHTR account for 25.17% and 25.47% of its overall area, respectively, and are predominantly situated in the higher elevations of its eastern and central regions. Further, the ESs are mainly distributed in its western region, specifically Ba Wangling (BWL), Jian Fengling (JFL), and Ying Geling (YGL), covering an area of 1624.67 km2 (38.06% of the total area) of the NPHTR. After correction, 47 ECs with a distance of 870.9 km have been established, with BWL and YGL serving as the core areas. The ecological pinch- and barrier points in the NPHTR cover areas of 11.49 km2 and 16.35 km2, respectively, primarily consisting of man-made landscapes such as farmlands and buildings. These areas are significantly disturbed by human activities. The NPHTR has an ESP of “one screen, one district, three belts, multiple points, and multiple corridors”. BWL and YGL, which contain numerous sources and corridors, are crucial ecological functional zones. To ensure the horizontal connectivity of rare wildlife, such as of Nomascus hainanus and Cervus eldii hainanus, the NPHTR should establish buffer or development zones. These findings offer valuable insights for the development and planning of ecological civilization on Hainan Island, as well as for the establishment and management of the ESP of other national parks and nature reserves.

IntroductionThe plain marsh wetland ecosystems are sensitive to changes in the natural environment and the intensity of human activities. The Sanjiang Plain is China’s largest area of concentrated marsh wetland, the Small Sanjiang Plain is the most important component of the Sanjiang Plain. However, with the acceleration of the urbanization and development of large-scale agricultural reclamation activities in the Small Sanjiang Plain in Northeast China, the wetland has been seriously damaged. In light of this degradation this study examines the Small Sanjiang Plain.MethodsFrom the four aspects of area, structure, function, and human activities, we try to construct a wetland degradation comprehensive index (WDCI) in cold region with expert scoring methods and analytic hierarchy process (AHP), coupled with network and administrative unit. The objective was to reveal the degradation of wetlands in Northeast China over three decades at a regional scale.ResultsThe results showed that (1) the overall wetland area decreased between 1990 and 2020 by 39.26×103 hm2. Within this period a significant decrease of 336.56×103 hm2 occurred between 1990 and 200 and a significant increase of 214.62×103 hm2 occurred between 2010 and 2020. (2) In terms of structural changes, the fractal dimension (FRAC) has the same trend as the Landscape Fragmentation Index (LFI) with little change. (3) In terms of functional changes, the average above-ground biomass (AGB) increased from 1029.73 kg/hm2 to 1405.38 kg/hm2 between 1990 and 2020 in the study area. (4) In terms of human activities, the average human disturbance was 0.52, 0.46, 0.57 and 0.53 in 1990, 2000, 2010 and 2020, with the highest in 2010. (5) The composite wetland degradation index shows that the most severe wetland degradation was 49.61% in 2010 occurred between 1990 and 2020. (6) Among the severely deteriorated trajectory types in 2010–2020, mild degradation → serious degradation accounted for the largest area of 240.23×103 hm2, and the significant improvement trajectory type in 1990–2000 accounted for the largest area of 238.50×103 hm2.DiscussionIn brief, we conclude that the degradation of the Small Sanjiang Plain wetland was caused mainly by construction, overgrazing, deforestation, and farmland reclamation. This study can also provide new views for monitoring and managing wetland degradation by remote sensing in cold regions.

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.