Biome‐specific soil respiration (Rs) has important yet different roles in both the carbon cycle and climate change from regional to global scales. To date, no comparable studies related to global biome‐specific Rs have been conducted applying comprehensive global Rs databases. The goal of this study was to develop artificial neural network (ANN) models capable of spatially estimating global Rs and to evaluate the effects of interannual climate variations on 10 major biomes. We used 1976 annual Rs field records extracted from global Rs literature to train and test the ANN models. We determined that the best ANN model for predicting biome‐specific global annual Rs was the one that applied mean annual temperature (MAT), mean annual precipitation (MAP), and biome type as inputs (r² = 0.60). The ANN models reported an average global Rs of 93.3 ± 6.1 Pg C yr⁻¹ from 1960 to 2012 and an increasing trend in average global annual Rs of 0.04 Pg C yr⁻¹. Estimated annual Rs increased with increases in MAT and MAP in cropland, boreal forest, grassland, shrubland, and wetland biomes. Additionally, estimated annual Rs decreased with increases in MAT and increased with increases in MAP in desert and tundra biomes, and only significantly decreased with increases in MAT (r² = 0.87) in the savannah biome. The developed biome‐specific global Rs database for global land and soil carbon models will aid in understanding the mechanisms underlying variations in soil carbon dynamics and in quantifying uncertainty in the global soil carbon cycle.

The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of forest ecosystems. The rapid development of remote sensing satellites provides an excellent opportunity to improve the accuracy of forest soil prediction models. This study aimed to explore the utility of the Gaofen-1 (GF-1) satellite in the forest soil mapping model in Luoding City, Yunfu City, Guangdong Province, Southeast China. We used 1000 m resolution coarse-resolution soil map to represent the overall regional soil nutrient status, 12.5 m resolution terrain-hydrology variables to reflect the detailed spatial distribution of soil nutrients, and 8 m resolution remote sensing variables to reflect the surface vegetation status to build terrain-hydrology artificial neural network (ANN) models and full variable ANNs, respectively. The prediction objects were alkali-hydro-nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). The results showed that the full-variable ANN accuracy at five soil depths was better than the terrain-hydrology ANNs, indicating that remote sensing variables reflecting vegetation status can improve the prediction of forest soil nutrients. The remote sensing variables had different effectiveness for different soil nutrients and different depths. In upper soil layers (0–20 and 20–40 cm), remote sensing variables were more useful for AN, AP, and OM, and were between 10%–14% (R²), and less effective for AK at only 8% and 6% (R²). In deep soil layers (40–60, 60–80, and 80–100 cm), the improvement of all soil nutrient models was not significant, between 3 and 6% (R²). RMSE and ROA ± 5% also decreased with the depth of soil. Remote sensing ANNs (coarse resolution soil maps + remote sensing variables) further demonstrated that the predictive power of remote sensing data decreases with soil depth. Compared to terrain-hydrological variables, remote sensing variables perform better at 0–20 cm, but the predictive power decreased rapidly with depth. In conclusion, the results of the study showed that the integration of remote sensing with coarse-resolution soil maps and terrain-hydrology variables could strongly improve upper forest soil (0–40 cm) nutrients prediction and NDVI, green band, and forest types were the best remote sensing predictors. In addition, the study area is rich in AN and OM, while AP and AK are scarce. Therefore, to improve forest health, attention should be paid to monitoring and managing AN, AP, AK, and OM levels.

The soil respiration (Rs) of forests is a major component of global Rs, yet few studies have focused on it. This study aimed to estimate global forest Rs and its changes at a resolution of 30 m via an artificial neural network (ANN) model. Five input candidates representing forest type, climatic, soil, and geographical information, as well as 1472 satisfactory forest Rs records, were used to build the ANN model and evaluate the model performance via a 10‐fold cross‐validation scheme. Global forest change data sets were used to accurately define the extent of forests and their changes, which was achievable because of the dynamic information and high resolution (30 m) of the data sets. The results indicate that the average annual global forest Rs from 2000 to 2020, as estimated by the optimal ANN model with an r² value of 0.67 and a root‐mean‐square error of 252.6 g C m⁻² yr⁻¹, was 46.24 ± 5.86 Pg C yr⁻¹. From 2001 to 2019, the average theoretical annual global forest Rs loss was 0.22 ± 0.06 Pg C yr⁻¹ due to an average forest loss area of 23.4 million ha yr⁻¹. In addition, the annual Rs theoretically increased by 0.75 Pg C in 2012 due to a global forest gain area of 80.5 million from 2001 to 2012. The presented data sets of global forest Rs and its changes can provide an accurate benchmark for discussing the carbon cycle and climate change at global to regional scales, even when operating over a small forest area (i.e., dozens of ha), which is a scale that has been ignored in other global Rs studies.

Stocks and stoichiometry of carbon (C), nitrogen (N), and phosphorus (P) in ultisols are not well documented for converted forests. In this study, Ultisols were sampled in 175 plots from one type of secondary forest and four plantations of Masson pine (Pinus massoniana Lamb.), Slash pine (Pinus elliottii Engelm.), Eucalypt (Eucalyptus obliqua L’Hér.), and Litchi (Litchi chinensis Sonn., 1782) in Yunfu, Guangdong province, South China. Five layers of soil were sampled with a distance of 20 cm between two adjacent layers up to a depth of 100 cm. We did not find interactive effects between forest type and soil layer depth on soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) concentrations and storages. Storage of SOC was not different between secondary forests and Eucalypt plantations, but SOC of these two forest types were lower than that in Litchi, Masson pine, and Slash pine plantations. Soil C:P was higher in Slash pine plantations than in secondary forests. Soil CNP showed a decreasing trend with the increase of soil depth. Soil TP did not show any significant difference among soil layers. Soil bulk density had a negative contribution to soil C and P stocks, and longitude and elevation were positive drivers for soil C, N, and P stocks. Overall, Litchi plantations are the only type of plantation that obtained enhanced C storage in 0–100 cm soils and diverse N concentrations among soil layers during the conversion from secondary forests to plantations over ultisols.

Ecosites are required for stand-level forest management and can be determined within a two-dimensional edatopic grid with soil nutrient regimes (SNRs) and soil moisture regimes (SMRs) as coordinates. A new modeling method is introduced in this study to map high-resolution SNR and SMR and then to design ecosites in Nova Scotia, Canada. Using coarse-resolution soil maps and nine topo-hydrologic variables derived from high-resolution digital elevation model (DEM) data as model inputs, 511 artificial neural network (ANN) models were developed by a 10-fold cross-validation with 1507 field samples to estimate 10 m resolution SNR and SMR maps. The results showed that the optimal models for mapping SNR and SMR engaged eight and seven topo-hydrologic variables, together with three coarse-resolution soil maps, as model inputs, respectively; 82% of model-estimated SNRs were identical to field assessments, while this value was 61% for SMRs, and the produced ecosite maps had 67–68% correctness. According to the error matrix, the predicted SNR and SMR maps greatly alleviated poor prediction in the areas of extreme nutrient or moisture conditions (e.g., very poor or very rich, wet, or very dry). Thus, the new method for modeling high-resolution SNR and SMR could be used to produce ecosite maps in sites where accessibility is hard.

Zhao, Z., Ashraf, M. I., Keys, K. S. and Meng, F-R. 2013. Prediction of soil nutrient regime based on a model of DEM-generated clay content for the province of Nova Scotia, Canada. Can. J. Soil Sci. 93: 193–203. Soil nutrient regime (SNR) maps are widely required by ecological studies as well as forest growth and yield assessment. Traditionally, SNR is assessed in the field using vegetation indicators, topography and soil properties. However, field assessments are expensive, time consuming and not suitable for producing high-resolution SNR maps over a large area. The objective of this research was to develop a new model for producing high-resolution SNR maps over a large area (in this case, the province of Nova Scotia). The model used 10-m resolution clay content maps generated from digital elevation model data to capture local SNR variability (associated with topography) along with coarse-resolution soil maps to capture regional SNR variability (associated with differences in landform/parent material types). Field data from 1385 forest plots were used to calibrate the model and another 125 independent plots were used for model validation. Results showed field-identified SNRs were positively correlated with predicted clay content, with some variability associated with different landform/parent material types. Accuracy assessment showed that 63.7% of model-predicted SNRs were the same as field assessment, with 96.5% within ±1 class compared with field-identified SNRs. The predicted high-resolution SNR map was also able to capture the influence of topography on SNR which was not possible when predicting SNR from coarse-resolution soil maps alone.