Light detection and ranging (LiDAR) has been widely used to estimate forest biomass. In this study, we aim to further explore this capability by correlating horizontal and vertical distribution of LiDAR data with components of biomass in a Picea crassifolia forest. Airborne small footprint full-waveform data were decomposed to acquire higher density point clouds. We calculated LiDAR metrics at the tree level and subplot level and correlated them to biomass components, including branch biomass (BB), leaf biomass (LB) and above-ground biomass (AGB), respectively. A new metric (Horiz_cv) describing the horizontal distribution of point clouds was proposed. This metric was found to be highly correlated with canopy biomass (BB and LB) at the tree level and subplot level. Correlation between AGB and Horiz_cv at the subplot level is much lower than that at tree level. AGB for subplot is highly correlated with the mean height metric (H_mean), canopy cover index (CCI) and the product of them. On the other hand, the relationship between the vertical distribution of LiDAR point and biomass was explored by developing two types of vertical profiles, including LiDAR distribution profiles and a biomass profile. Good relationships were found between these two types of vertical profiles and assessed by Pearson’s correlation coefficient (R) and the area of overlap index (AOI). These good correlations possess potential in predicting the vertical distribution of canopy biomass. Overall, it is concluded that not only the vertical, but also the horizontal distribution of LiDAR points should be taken into account in estimating components of biomass by LiDAR.

View angle effects present in crop canopy spectra are critical for the retrieval of the crop canopy leaf area index (LAI). In the past, the angular effects on spectral vegetation indices (VIs) for estimating LAI, especially in crops with different plant architectures, have not been carefully assessed. In this study, we assessed the effects of the view zenith angle (VZA) on relationships between the spectral VIs and LAI. We measured the multi-angular hyperspectral reflectance and LAI of two cultivars of winter wheat, erectophile (W411) and planophile (W9507), across different growing seasons. The reflectance of each angle was used to calculate a variety of VIs that have already been published in the literature as well as all possible band combinations of Normalized Difference Spectral Indices (NDSIs). The above indices, along with the raw reflectance of representative bands, were evaluated with measured LAI across the view zenith angle for each cultivar of winter wheat. Data analysis was also supported by the use of the PROSAIL (PROSPECT + SAIL) model to simulate a range of bidirectional reflectance. The study confirmed that the strength of linear relationships between different spectral VIs and LAI did express different angular responses depending on plant type. LAI–VI correlations were generally stronger in erectophile than in planophile wheat types, especially at the zenith angle where the background is expected to be more evident for erectophile type wheat. The band combinations and formulas of the indices also played a role in shaping the angular signatures of the LAI–VI correlations. Overall, off-nadir angles served better than nadir angle and narrow-band indices, especially NDSIs with combinations of a red-edge (700~720 nm) and a green band, were more useful for LAI estimation than broad-band indices for both types of winter wheat. But the optimal angles much differed between two plant types and among various VIs. High significance (R² > 0.9) could be obtained by selecting appropriate VIs and view angles on both the backward and forward scattering direction. These results from the in-situ measurements were also corroborated by the simulation analysis using the PROSAIL model. For the measured datasets, the highest coefficient was obtained by NDSI(536,720) at −35° in the backward (R² = 0.971) and NDSI(571,707) at 55° in the forward scattering direction (R² = 0.984) for the planophile and erectophile varieties, respectively. This work highlights the influence of view geometry and plant architecture. The identification of crop plant type is highly recommended before using remote sensing VIs for the large-scale mapping of vegetation biophysical variables.

Land-use change (LUC) is a significant contributor to the increase in atmospheric CO₂ concentrations, with previous studies demonstrating its profound impact on soil organic carbon (SOC). The conversion of primary forests to farmland has been recognized as the most significant type of LUC inducing CO₂ release from the soil. Therefore, it is critical to understand the impacts of forest LUC on SOC storage, with a particular focus on primary forest to farmland conversion. In this study, we conducted a meta-analysis of 411 observations from 41 published works and found that SOC storage decreased significantly following the conversion of primary forests to farmland. Factors such as soil depth and climate zone influenced the degree of SOC storage loss, with SOC loss being less severe in deeper soil following a conversion from primary forests to farmland. Moreover, the loss of SOC storage was more severe in temperate regions compared to tropical regions. The input and output of surface SOC, changes in soil structure, and increases in atmospheric CO₂ concentrations were significant reasons for the loss of SOC following primary forest to farmland LUC. However, improving tillage methods and implementing sustainable agricultural management strategies can help reduce SOC loss. These findings highlight the importance of sustainable land-use practices in mitigating the negative impacts of forest LUC on SOC storage and the global carbon cycle.

The co-contamination of arsenic (As) and cadmium (Cd) in paddy soils threatens rice safety, yet synergistic mitigation strategies using silicon (Si) and ferrous sulfate (FeSO₄) remain underexplored. This study integrated hydroponic and soil pot experiments to evaluate Si-FeSO₄ interactions on As/Cd accumulation and rice growth. Hydroponic trials employed 21-day-old rice seedlings exposed to 0.5 mg As(III)/Cd(II) L⁻¹ with/without 70 mg Si L⁻¹ and 30–70 mg Fe L⁻¹, followed by sequential harvesting at 14 and 21 days. Soil experiments utilized co-contaminated paddy soil (50 mg As kg⁻¹ and 1.2 mg Cd kg⁻¹) amended with Si (80 or 400 mg kg⁻¹) and Fe (100 or 1000 mg kg⁻¹), with pore water dynamics monitored over 120 days. Hydroponic results demonstrated that 70 mg Si L⁻¹ combined with 30 or 70 mg Fe L⁻¹ enhanced shoot biomass by 12–79% under As stress, while simultaneously reducing shoot As concentrations by 76–87% and Cd concentrations by 14–33%. Iron plaque induced by FeSO₄ exhibited contrasting adsorption behaviors: hydroponic roots immobilized both As and Cd (p < 0.01), whereas roots in soil primarily retained Cd (p < 0.05). In soil experiments, the optimal treatment of 100 mg Fe kg⁻¹ and 400 mg Si kg⁻¹ (Fe₁ + Si₂) increased grain biomass by 54%, while reducing As and Cd concentrations by 37% and 42%, respectively. However, a higher Fe dosage (Fe₂: 1000 mg kg⁻¹ Fe) paradoxically increased grain Cd concentrations. Mechanistically, Si amendment elevated soil pH (Δ + 0.72), facilitating Cd immobilization, while FeSO₄ lowered pH (Δ−0.07–0.53), increasing Cd mobility. A strong correlation between soluble Cd and plant uptake was observed (p < 0.01), while changes in As accumulation were unrelated to aqueous behavior. The optimized Si/Fe molar ratio of 7.95:1 effectively mitigated As and Cd co-accumulation, offering a dual-functional strategy for safe rice cultivation in contaminated soils.