The monitoring of particulate organic carbon (POC) flux at the bottom of the euphotic layer in global ocean using remote sensing satellite data plays an important role in clarifying and evaluating the ocean carbon cycle. Based on the in situ POC flux data, this paper evaluated various estimation models. The global ocean POC flux from 2003 to 2018 was calculated using the optimal model, and its temporal and spatial variation characteristics were analyzed. In general, the annual average of global ocean POC flux is about 8.5–14.3 ${\mathrm{Gt}\mathrm{C}\mathrm{yr}}^{-1}$ for period of 2003–2018. In the spatial dimension, the POC flux in the mid-latitude ocean (30–60°) is higher than that in the low-latitude (0–30°). The POC flux in Continental Margins with water depth less than 2000 m accounted for 30% of global ocean, which should receive more attention in global carbon cycle research. In the time dimension, the global POC flux decreases year by year generally, but the POC flux abnormally decreases during El Niño and increases during La Niña. In addition, due to global warming, sea ice melting, and bipolar sea area expansion, POC flux in high-latitude oceans (60–90°) is increasing year by year.

Remote sensing (RS)-derived vegetation indices (VIs) with medium and high spatial resolution have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring at local or global scales. Before they can be used as reliable inputs for other research, conducting in situ measurements for validation is very critical. However, the spatial heterogeneity due to the diversity of land cover and its spatial organization in the landscape increases the uncertainty of validation, so design of optimal sampling is an important basis for the reliability of the validation. In this paper, we propose an integrative stratified sampling strategy (INTEG-STRAT) based on normalized difference vegetation index (NDVI) data as prior knowledge. The basic idea is to realize a sampling optimization by determining the optimal combination of the spatial sampling method (e.g., simple random sampling (SRS), spatial system sampling (SYS), stratified sampling, generalized random tessellation stratified (GRTS), balanced acceptance sampling (BAS)) and spatial stratification scheme with an objective rule. The objective rule in this paper is to minimize the root mean square error (RMSE) of 10-fold cross validation between estimated values (sample are not included) and the corresponding values on prior knowledge. Relative precision, correlation coefficient, and RMSE are used to compare the effectiveness of the proposed sampling strategy with each sampling method without considering sampling optimization. After comparing, we find that the INTEG-STRAT requires fewer samples to become stable and has higher accuracy. At site 1, when the correlation coefficient between NDVI image and the simulated NDVI surface reached 80%, INTEG-STRAT needed only 70 sampling points while other methods require more sampling points. At the same time, INTEG-STRAT strategy has a smaller RMSE between the estimated values and the corresponding values on prior knowledge image. In general, INTEG-STRAT is an effective method in the selection of representative samples to support the validation of vegetation indices products with medium and high spatial resolution.

The phytoplankton pigment absorption $(a_{{\rm{ph}}})$-based productivity model (AbPM) is commonly used to estimate marine net primary production (NPP). In the AbPM, $a_{{\rm{ph}}}$ is used as the key predictor of phytoplankton photosynthetic efficiency. Previous studies have shown that the size of phytoplankton could affect the photosynthetic efficiency. Thus, a size-fractionated AbPM was proposed by considering how phytoplankton size influenced the global quantum yield of phytoplankton photosynthesis. The sea surface temperature (SST) was also used to further improve AbPM performance in cold waters (SST < 10 °C). With the proposed new model, monthly averaged global ocean NPP data for the period 2003–2012 were derived, and the distributions of the global NPP differed in both space and time from previous AbPM-based estimates. The accuracy of the new estimated model was validated using in situ NPP data, which were collected from different marine monitoring sites. Compared with the original AbPM, the new model not only had an excellent performance at each marine monitoring site, but also had an improved accuracy over the Southern Ocean. Although additional studies need to be conducted for confirmation, this size-fractionated model could be a reasonable and accurate method for the estimation of global ocean NPP.

The 44 years (1979–2022) of satellite-derived sea ice extent in the Arctic and Antarctic reveals the details and new trends in the process of polar sea ice coverage changes. The speed of Arctic sea ice extent reduction and the interannual difference significantly increased after 2004. Trend analysis suggests that the Arctic Ocean may experience an ice-free period around 2060. The maximum anomaly of Arctic sea ice extent has gradually transitioned from September to October, indicating a trend of prolonged melting period. The center of gravity of sea ice in the Arctic Ocean is biased towards the Pacific side, and the spatial distribution pattern of sea ice is greatly influenced by the Atlantic warm current. The dynamism of the sea ice extent on the Atlantic side is significantly greater than in other regions. Since 2014, the Antarctic sea ice extent has shifted from slow growth to a rapid decreasing trend; the sea ice extent reached a historical minimum in 2022, decreasing by 2.02 × 106 km² compared to 2014. The Antarctic experiences seven months of ice growth each year and five months of ice melting period, the annual change patterns of sea ice extent in the Arctic and Antarctic are slightly different.

Soil moisture (SM) estimates are essential for drought monitoring, hydrological modeling, and climate resilience planning applications. While satellite and model-derived SM products effectively capture SM dynamics, their coarse spatial resolutions (~10–36 km) hinder their ability to represent SM variability in heterogeneous landscapes influenced by local factors. This study proposes a novel downscaling framework that employs an Artificial Neural Network (ANN) on a cloud-computing platform to improve the spatial resolution and representation of multi-source SM datasets. A data analysis was conducted by integrating Google Earth Engine (GEE) with the computing capabilities of the python language through Google Colab. The framework downscaled Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA5-Land), and Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) at 500 m for Kenya, East Africa. This was achieved by leveraging ten input variables comprising elevation, slope, surface albedo, vegetation, soil texture, land surface temperatures (day and night), evapotranspiration, and geolocations. The coarse SM datasets exhibited spatiotemporal consistency, with a standard deviation below 0.15 m³/m³, capturing over 95% of the variability in the original data. Validation against in situ SM data at the station confirmed the framework’s reliability, achieving an average UbRMSE of less than 0.04 m³/m³ and a correlation coefficient (r) over 0.52 for each downscaled dataset. Overall, the framework improved significantly in r values from 0.48 to 0.64 for SMAP, 0.47 to 0.63 for ERA5-Land, and 0.60 to 0.69 for FLDAS. Moreover, the performance of FLDAS and its downscaled version across all climate zone is consistent. Despite the uncertainties among the datasets, the framework effectively improved the representation of SM variability spatiotemporally. These results demonstrate the framework’s potential as a reliable tool for enhancing SM applications, particularly in regions with complex environmental conditions.

Validation is an essential process to evaluate the quality of waterbody remote sensing products, and the reliability and effective application of the in situ data of waterbody parameters are an important part of validation. Based on the in situ data of chlorophyll-a (Chl-a), total suspended solids (TSS) and other environmental variables (EVs) measured at the fixed station in Taihu Lake, we attempt to develop a prediction model to determine whether the in situ measurement has enough representativeness for validating waterbody remote sensing products. Key EVs that affect the changes of Chl-a and TSS are firstly identified by using correlation analysis, which participate in modeling as variables. In addition, three multi-parameter modeling approaches are selected to simulate the daily changes of Chl-a and TSS under different EVs configurations. The results indicate that the highest prediction accuracy can be achieved through the generalized regression neural network (GRNN) based model. In the all-valid dataset, the testing absolute average relative errors (AEs) of GRNN-based Chl-a and TSS prediction model are 11.4% and 11.3%, respectively, and in the sunny-day dataset, the testing AEs are 8.6% and 8.2%, respectively. Meanwhile, the application example proves that the prediction model in this paper can be effectively used to screen the in situ data and determine the time window for satellite-ground data matching.