Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale.

Rapid acquisition of the spatial distribution of soil nutrients holds great implications for farmland soil productivity safety, food security and agricultural management. To this end, we collected 1297 soil samples and measured the content of soil total nitrogen (TN), soil available phosphorus (AP) and soil available potassium (AK) in Zengcheng, north of the Pearl River Delta, China. Hyperspectral remote sensing images (115 bands) of the Chinese Environmental 1A satellite were used as auxiliary variables and dimensionality reduction was performed using Pearson correlation analysis and principal component analysis. The TN, AP and AK of soil were predicted in the study area based on auxiliary variables after dimensionality reduction, along with stepwise linear regression (SLR), support vector machine (SVM), random forest (RF) and back-propagation neural network (BPNN) models; 324 independent points were used to verify the predictive performance. The BPNN model, which demonstrated the best predictive accuracy among all methods, combined ordinary kriging (OK) with mapping the spatial variations of soil nutrients. Results show that the BPNN model with double hidden layers had better predictive accuracy for soil TN (root mean square error (RMSE) = 0.409 mg kg-1, R² = 44.24%), soil AP (RMSE = 40.808 mg kg-1, R² = 42.91%) and soil AK (RMSE = 67.464 mg kg-1, R² = 48.53%) compared with the SLR, SVM and RF models. The back propagation neural network-ordinary kriging (BPNNOK) model showed the best predictive results of soil TN (RMSE = 0.292 mg kg-1, R² = 68.51%), soil AP (RMSE = 29.62 mg kg-1, R² = 69.30%) and soil AK (RMSE = 49.67 mg kg-1 and R² = 70.55%), indicating the best fitting ability between hyperspectral remote sensing bands and soil nutrients. According to the spatial mapping results of the BPNNOK model, concentrations of soil TN (north-central), soil AP (central and southwest) and soil AK (central and southeast) were respectively higher in the study area. The most important bands (464⁻517 nm) for soil TN (b10, b14, b20 and b21), soil AP (b3, b19 and b22) and soil AK (b4, b11, b12 and b25) exhibited the best response and sensitivity according to the SLR, SVM, RF and BPNN models. It was concluded that the application of hyperspectral images (visible-near-infrared data) with BPNNOK model was found to be an efficient method for mapping and monitoring soil nutrients at the regional scale.

Analysis of multi-scale effects and spatial heterogeneity of environmental factors influencing purse seine tuna fishing activities in the Western and Central Pacific Ocean.

Understanding the spatial fishing activity distribution characteristics is important for the sustainable development of fisheries. Spatial nonstationarity is always present, especially in marine ecosystems. To explore how marine environmental factors affect the fishing effort of tuna purse seine vessels, data from 2015 to 2020 on the fishing activities of these fleets and environmental variables in the Western and Central Pacific Ocean (WCPO) were analyzed. A Generalized Additive Model (GAM), Geographically Weighted Regression model (GWR), and Multi-Scale Geographically Weighted Regression (MGWR) model were applied to explore the drivers of fishing activity and the impacts of environmental factors on spatial heterogeneity. The results indicate that: (1) The MGWR models has the highest prediction accuracy and effectively reflects the spatial heterogeneity and multi-scale effects of environmental factors in a year. (2) Environmental factors exhibit significant multi-scale effects and spatial heterogeneity on the fishing activities of purse seine tuna vessels. Sea floor depth, salinity at 200 m depth and sea surface temperature show the greatest spatial heterogeneity in their impact on fishing activities. (3) Sea surface temperature, distance to port, and primary productivity and salinity at 200 m depth are key variables influencing the fishing activities of purse seine tuna vessels. These findings are expected to provide scientific and effective guidance for fishery management and sustainable development by assessing the spatial variations in fishing activities at multiple scales.

[Impact of trawl fishing on fisheries population components in East China Sea].

Based on the recent 50 years two-boats trawl fishing data of some state-owned fisheries companies and recent 7 years single-boat fishing data of some areas in East China Sea, the trophic index(TI) and composition indexs of fishes of two-boats trawl and single-boat trawl were calculated and analyzed, which indicated that TI increased before 1974 and decreased rapidly thereafter, while composition index decreased from the end of 1970s, but SD increased. The results of single-boat trawl also indicated that TI decreased gradually. Long-term over-fishing of trawl was one of the main reasons of the change of fisheries population structure in East China Sea. All of these showed that there was an over-fishing in East China Sea, and the robust of fisheries population was lower.

[Effects of environmental factors on catch variation of main species of stow net fisheries in East China Sea].

Stow net fishery is one of the important fishing methods in the East China Sea. This paper used the generalized additive models (GAMs) to quantitatively describe the relationships between stow net catch and environmental factors (sea surface temperature SST, water depth, fishing position and tide) in the East China Sea. The results indicated that each factor had its own nonlinear effect on the catch per unit effort (CPUE) of haitail (Trichiurus japonicus), small yellow croaker (Larimichthys polyactis) and butter fish (Pampus spp.), and SST and water depth were the key factors. The GAMs' fitting results showed that SST had the strongest effect on the catch per unit effort of haitail, and water depth had the second one. The effects of fishing location and tide were very small. Water depth was the most influential variable when adjusted for the effects on small yellow croaker. SST, fishing location and tide had similar effects. Meanwhile, water depth and SST were the key factors affecting the catch per unit effort of butter fish. They had similar intensity.