As an important part and the core link of a terrestrial ecosystem, terrestrial vegetation is the main means for human to regulate climate and mitigate the increase in atmospheric CO₂ concentration. The Yangtze River Delta (YRD) region is an urban agglomeration with the strongest comprehensive strength among developing countries (China). In the context of global climate change, a rapid, comprehensive, and detailed understanding of the spatio-temporal characteristics and variation tendency of the net ecosystem productivity (NEP) of vegetation and its response to climate during the rapid development of the YRD region is important for protecting ecological land, strengthening land management, and optimizing urban planning. The monthly mean temperature and rainfall data from 63 meteorological stations, the MODIS net primary productivity product, and a land cover product in the YRD region were used to estimate the NEP from 2000 to 2019 based on the soil respiration model, and the correlation between NEP and meteorological factors (such as temperature and rainfall) was analyzed. The results showed that: (1) From 2000 to 2019, the carbon sink area was much larger than the carbon source area in terrestrial vegetation in the Yangtze River Delta, the mean NEP of the vegetation ecosystem in the past 20 years was 253.2 g C·m⁻²·a⁻¹, and the spatial distribution presented a trend that was higher in the south and lower in the north, higher in the east and lower in the west, and that gradually increased from northwest to southeast; moreover, the NEP of mountain areas was generally higher than that of river courses and urban surroundings. The interannual fluctuation of NEP was small, but presented a slightly increasing trend, and the interannual variation of NEP was significantly correlated with the maximum NEP in this region. (2) The carbon sink capacity of different vegetation cover types was (from strong to weak): forestlands > grasslands > wetlands ≈ croplands. (3) The area with the NEP change rate (θ_slope) > 0 accounted for 69.0%; however, there was certain spatial difference, the proportions of the areas with θ_slope < 0 were (from large to small) 14.50% (Zhejiang Province, China), 9.10% (Anhui Province, China), 6.65% (Jiangsu Province, China), and 0.79% (Shanghai, China). In terms of the individual changes of these provinces and municipalities, Shanghai > Zhejiang Province > Jiangsu Province ≈ Anhui Province. (4) There was a correlation between NEP and the annual mean temperature and annual precipitation in some regions.

Soil moisture is a key factor affecting the growth of crops, and microwave remote sensing is one of the most important methods for inverse studies of soil moisture in agricultural fields in recent years. Cotton is a typical water-demanding crop in arid zones, and accurate estimation of soil moisture information in cotton fields is extremely important for optimizing irrigation management, improving water use efficiency, and increasing cotton yield. This study focuses on extracting feature sets by combining Sentinel-1 and Gaofen-6 satellite data, constructing convolutional neural network (CNN), random forest, support vector regression, and eXtreme gradient boosting model to estimate the soil moisture in cotton fields in Shihezi area of Xinjiang, and designing eight groups of experiments according to the different input data sources. The experimental results show that the accuracy of the soil moisture estimate in cotton fields in arid areas with multi-source data is significantly better than that of a single data source. Moreover, the CNN was best estimated when using multi-source data feature sets as inputs, with a coefficient of determination of 0.789, a root mean square error of 0.0249 cm3/cm3, and an average absolute error of 0.0198 cm3/cm3 for its CNN model. This result demonstrates the effectiveness of CNN in soil moisture estimation and also provides a new method for the use of multi-source remote sensing data for accurate soil moisture estimation in cotton fields in arid areas, and also explores the application of Gaofen-6 data in soil moisture.

Forest carbon stock is an important indicator reflecting the structure of forest ecosystems and forest quality, and an important parameter for evaluating the carbon sequestration capacity and carbon balance of forests. It is of great significance to study forest carbon stock in the context of current global climate change. To explore the application ability of multi-loaded, high-resolution satellite data in the estimation of subtropical forest carbon stock, this paper takes Huangfu Mountain National Forest Park in Chuzhou City as the study area, extracts remote sensing features such as spectral features, texture features, backscattering coefficient, and other remote sensing features based on multi-loaded, high-resolution satellite data, and carries out correlation analyses with the carbon stock of different species of trees and different age groups of forests. Regression models for different tree species were established for different data sources, and the optimal modeling factors for multi-species were determined. Then, three algorithms, namely, multiple stepwise regression, random forest, and gradient-enhanced decision tree, were used to estimate carbon stocks of multi-species, and the predictive ability of different estimation models on carbon stocks was analyzed using the coefficient of determination (R²) and the root mean square error (RMSE) as indexes. The following conclusions were drawn: for the feature factors, the texture features of the GF-2 image, the new red edge index of the GF-6 image, the radar intensity coefficient sigma, and radar brightness coefficient beta of the GF-3 image have the best correlation with the carbon stock; for the algorithms, the random forest and gradient-boosting decision tree have the better effect of fitting and predicting the carbon stock of multi-tree species, among which gradient-boosting decision tree has the best effect, with an R² of 0.902 and an RMSE of 10.261 t/ha. In summary, the combination of GF-2, GF-3, and GF-6 satellite data and gradient-boosting decision tree obtains the most accurate estimation results when estimating forest carbon stocks of complex tree species; multi-load, high-resolution satellite data can be used in the inversion of subtropical forest parameters to estimate the carbon stocks of subtropical forests. The multi-loaded, high-resolution satellite data have great potential for application in the field of subtropical forest parameter inversion.

Accurately grasping the distribution and area of cotton for agricultural irrigation scheduling, intensive and efficient management of water resources, and yield estimation in arid and semiarid regions is of great significance. In this paper, taking the Xinjiang Shihezi oasis agriculture region as the study area, extracting the spectroscopic characterization (R, G, B, panchromatic), texture feature (entropy, mean, variance, contrast, homogeneity, angular second moment, correlation, and dissimilarity) and characteristics of vegetation index (normalized difference vegetation index/NDVI, ratio vegetation index/DVI, difference vegetation index/RVI) in the cotton flowering period before and after based on GF-6 image data, four models such as the random forests (RF) and deep learning approach (U-Net, DeepLabV3+ network, Deeplabv3+ model based on attention mechanism) were used to identify cotton and to compare their accuracies. The results show that the deep learning model is better than that of the random forest model. In all the deep learning models with three kinds of feature sets, the recognition accuracy and credibility of the DeepLabV3+ model based on the attention mechanism are the highest, the overall recognition accuracy of cotton is 98.23%, and the kappa coefficient is 96.11. Using the same Deeplabv3+ model based on an attention mechanism with different input feature sets (all features and only spectroscopic characterization), the identification accuracy of the former is much higher than that of the latter. GF-6 satellite image data in the field of crop type recognition has great application potential and prospects.

The aboveground biomass (AGB) of forests reflects the productivity and carbon-storage capacity of the forest ecosystem. Although AGB estimation techniques have become increasingly sophisticated, the relationships between AGB, spatial distribution, and growth stages still require further exploration. In this study, the Picea schrenkiana (Picea schrenkiana var. tianschanica) forest area in the Kashi River Basin of the Ili River Valley in the western Tianshan Mountains was selected as the research area. Based on forest resources inventory data, Gaofen-1 (GF-1), Gaofen-6 (GF-6), Gaofen-3 (GF-3) Polarimetric Synthetic Aperture Radar (PolSAR), and DEM data, we classified the Picea schrenkiana forests in the study area into three cases: the Whole Forest without vertical zonation and stand age, Vertical Zonality Classification without considering stand age, and Stand-Age Classification without considering vertical zonality. Then, for each case, we used eXtreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Residual Networks (ResNet), respectively, to estimate the AGB of forests in the study area. The results show that: (1) The integration of multi-source remote-sensing data and the ResNet can effectively improve the remote-sensing estimation accuracy of the AGB of Picea schrenkiana. (2) Furthermore, classification by vertical zonality and stand ages can reduce the problems of low-value overestimation and high-value underestimation to a certain extent.