A novel total phosphorus concentration retrieval method based on two-line classification in lakes and reservoirs across China

Phosphorus is widely recognized as a nutrient that restricts growth and is the primary contributor to eutrophication in 80 % of water bodies. Consequently, the Chinese government has consistently prioritized monitoring and controlling total phosphorus (TP) levels. The remote estimation of TP in lakes and reservoirs at a national scale is a challenging task due to TP being a non-optically active parameter. Currently, there is a lack of developed TP inversion models specifically designed for lakes and reservoirs in China. For solving this problem, a novel two-line classification method drawn on scatter plots based on the natural logarithm of TP (Ln(TP)) and B33/B9 was proposed and used to classify 1211 measured samples obtained from field cruises in 105 lakes and reservoirs across China from 2012 to 2022 into three categories, Class 1, Class 2, and Class 3. Results demonstrate that the proposed classification method has the ability to enhance the correlation between Ln(TP) and 43 basic potential single band and band combinations. Specifically, the correlation range improved from (-0.31,0.15) to (-0.77,0.24) in Class 1, (-0.81, 0.36) in Class 2, and (-0.74, 0.52) in Class 3. Additionally, the classification method also improved the correlation range between Ln(TP) and 820 band ratios, from (-0.32, 0.32) to (-0.83, 0.82) in Class 1, (-0.86, 0.86) in Class 2, and (-0.86, 0.86) in Class 3. These datasets were subsequently utilized as input for eXtreme Gradient Boosting (XGBoost) models. Finally, well performing XGBoost models in Class 1 (R2 = 0.76, RMSE = 0.3, MAPE = 12 %), Class 2 (R2 = 0.84, RMSE = 0.49, MAPE = 38 %), and Class 3 (R2 = 0.74, RMSE = 0.46, MAPE = 14 %) were used to map TP of 563 large lakes and reservoirs (≥20 km2) across China using MODIS images from 2005, 2010, 2015, and 2020. This study presents a novel approach for estimating non-optically active parameters through remote sensing on a national scale.

Impact of climate change on net primary production (NPP) in south Iran

Climate change is a natural hazard which threatens the sustainable development of human health, food security, economic well-being, and natural resources. It also affects photosynthesis, plant respiration, and decomposition of organic matter that contribute to atmospheric carbon flow. The net primary production (NPP) is one of the main components of carbon balance. This study investigated the impact of climatic change on the net production in the Hormozgan county in south Iran. To obtain NPP, MODIS NPP product (MOD17A3) was used and future temperature and precipitation values were obtained using the HadGEM2-ES model under the RCP4.5 scenario. These values were downscaled using the LARSWG 6 statistical model, and precipitation and temperature were simulated for the RCP4.5 scenario. For further analysis, NPP was simulated based on the BIOME-BGC model and compared with the NPP data obtained from the MODIS images. Comparison of the climatic parameters of the basic (2001-2015) and future (2016-2030) periods indicated an increase in precipitation, minimum temperature, and maximum temperature of the study area and subsequently an increase in the NPP value in all biomes (averagely 17.73%) in the future. The highest NPP values were observed in the central and western parts of the region in biomes 4 (mangrove forest cover), 10 (broadleaf forest vegetation), and 6, 5, and 1 (rangeland vegetation), respectively, and the lowest values were observed in the eastern parts. Results showed that the increase in future NPP could be due to the increase in precipitation.

[Spatio-temporal Variations in Aquatic Vegetation Cover and the Potential Influencing Factors in Lake Hongze Based on MODIS Images]

Aquatic vegetation is an important part of lake ecosystems and plays a vital role in improving water quality and maintaining biodiversity. At present, China's lakes are facing eutrophication and the degradation of aquatic vegetation. The monitoring of temporal and spatial variations in aquatic vegetation and elucidating the main influencing factors are of great significance for protecting aquatic vegetation and restoring eutrophic lake ecosystems. Therefore, we introduced the Vegetation Present Frequency (VPF) method to extract data on aquatic vegetation and combined this with meteorological factors and human activities to analyze the temporal and spatial in Lake Hongze based on MODIS data from 2007 to 2017. The VPF of aquatic vegetation in Lake Hongze showed clear seasonal and interannual variations. The VPF was significantly higher in spring and summer than in autumn and winter (P<0.05, one way-ANOVA). The maximum VPF of 0.43 occurred in June but the minimum VPF of 0.21 was recorded in January. The VPF from April to October, during the growing season of aquatic vegetation, was significantly higher than in other months. The annual mean VPF of the northern lake area (Z1) decreased significantly (R²=0.56, P<0.01), ranging from the highest value of 0.50 in 2008 to the lowest value of 0.27 in 2016 (a decrease of 45.8%), indicating a significant loss of aquatic vegetation. Spatially, the VPF of Lake Hongze decreases from the coastal zone to the open water, and the VPF values of the northern (Z1) and western sub-lakes (Z2) are higher than that of other lakes segments (Z3-Z5). The interannual variation in VPF for the entire lake was not significantly affected by annual mean temperature, annual precipitation, annual mean wind speed, or annual sunshine duration (P>0.05), indicating that meteorological factors have little influence on interannual variation of aquatic vegetation in this lake. However, total suspended matter concentration was significantly negatively correlated with VPF in Z1 area (R²=0.48, P<0.01), with strong sand-mining activities occurring in this area. These results indicate that the increase of total suspended matter concentrations caused by sand mining is an important driving factor in the decline of aquatic vegetation in the Z1 segment.

Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data

Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture has expanded rapidly in northeastern China in the last decade, but there are no updated maps of paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the unique physical features of paddy rice during the flooding and transplanting phases and use vegetation indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine the temporal window of flooding and rice transplantation over a year to improve the existing phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies, and sparse vegetation) with potential influences on paddy rice identification were removed (masked out) due to their different temporal profiles. The accuracy assessment using high-resolution images showed that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice fields in temperate and cold temperate zones, the northern frontier of rice planting.