Chlorophyll-a plays an essential biochemical role in the eutrophication process, and is widely considered an important water quality indicator for assessing human activity’s effects on aquatic ecosystems. Herein, 20 years of moderate resolution imaging spectroradiometer (MODIS) data were applied to investigate the spatiotemporal patterns and trends of chlorophyll-a concentration (Chla) in the eutrophic Lake Taihu, based on a new empirical model. The validated results suggested that our developed model presented appreciable performance in estimating Chla, with a root mean square error (MAPE) of 12.95 μg/L and mean absolute percentage error (RMSE) of 29.98%. Long-term MODIS observations suggested that the Chla of Lake Taihu experienced an overall increasing trend and significant spatiotemporal heterogeneity during 2002–2021. A driving factor analysis indicated that precipitation and air temperature had a significant impact on the monthly dynamics of Chla, while chemical fertilizer consumption, municipal wastewater, industrial sewage, precipitation, and air temperature were important driving factors and together explained more than 81% of the long-term dynamics of Chla. This study provides a 20 year recorded dataset of Chla for inland waters, offering new insights for future precise eutrophication control and efficient water resource management.

Satellite-retrieved land surface phenology (LSP) is a first-order control on terrestrial ecosystem productivity, which is critical for monitoring the ecological environment and human and social sustainable development. However, mapping large-scale LSP at a 30 m resolution remains challenging due to the lack of dense time series images with a fine resolution and the difficulty in processing large volumes of data. In this paper, we proposed a framework to extract fine-resolution LSP across the conterminous United States using the supercomputer Tianhe-2. The proposed framework comprised two steps: (1) generation of the dense two-band enhanced vegetation index (EVI2) time series with a fine resolution via the spatiotemporal fusion of MODIS and Landsat images using ESTARFM, and (2) extraction of the long-term and fine-resolution LSP using the fused EVI2 dataset. We obtained six methods (i.e., AT, FOD, SOD, RCR, TOD and CCR) of fine-resolution LSP with the proposed framework, and evaluated its performance at both the site and regional scales. Comparing with PhenoCam-observed phenology, the start of season (SOS) derived from the fusion data using six methods of AT, FOD, SOD, RCR, TOD and CCR obtained r values of 0.43, 0.44, 0.41, 0.29, 0.46 and 0.52, respectively, and RMSE values of 30.9, 28.9, 32.2, 37.9, 37.8 and 33.2, respectively. The satellite-retrieved end of season (EOS) using six methods of AT, FOD, SOD, RCR, TOD and CCR obtained r values of 0.68, 0.58, 0.68, 0.73, 0.65 and 0.56, respectively, and RMSE values of 51.1, 53.6, 50.5, 44.9, 51.8 and 54.6, respectively. Comparing with the MCD12Q2 phenology, the satellite-retrieved 30 m fine-resolution LSP of the proposed framework can obtain more information on the land surface, such as rivers, ridges and valleys, which is valuable for phenology-related studies. The proposed framework can yield robust fine-resolution LSP at a large-scale, and the results have great potential for application into studies addressing problems in the ecological environmental at a large scale.

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Satellite-retrieved vegetation phenology has great potential for application in characterizing seasonal and annual land surface dynamics. However, obtaining regional-scale vegetation phenology from satellite remote sensing data often requires extensive data processing and computation, which makes the accurate and rapid retrieval of regional-scale phenology a challenge. To retrieve vegetation phenology from satellite remote sensing data, we developed an open-source tool called phenoC++, which uses parallel technology in C++. phenoC++ includes six common algorithms: amplitude threshold (AT), first-order derivative (FOD), second-order derivative (SOD), third-order derivative (TOD), relative change rate (RCR), and curvature change rate (CCR). We implemented the proposed phenoC++ and evaluated its performance on a site scale with PhenoCam-observed phenology metrics. The result shows that SOS derived from MODIS images by phenoC++ with six methods (i.e., AT, FOD, SOD, RCR, TOD, and CCR) obtained r-values of 0.75, 0.76, 0.75, 0.76, 0.64, and 0.67, and RMSE values of 21.36, 20.41, 22.38, 19.11, 33.56, and 32.14, respectively. Satellite-retrieved EOS by phenoC++ with six methods obtained r-values of 0.58, 0.59, 0.57, 0.56, 0.36, and 0.40, and RMSE values of 52.43, 46.68, 55.13, 49.46, 71.13, and 69.34, respectively. Using PhenoCam-observed phenology as a baseline, SOS retrieved by phenoC++ was superior to MCD12Q2, while EOS retrieved by phenoC++ was slightly inferior to that of MCD12Q2. Moreover, compared with MCD12Q2 on a regional scale, phenoC++-retrieved vegetation phenology yields more effective pixels. The innovative features of phenoC++ are 1) integrating six algorithms for retrieving SOS and EOS; 2) quickly processing data on a large scale with simple input startup parameters; 3) outputting phenology metrics in GeoTIFF format image, which is more convenient to use with other geospatial data. phenoC++ could aid in investigating and addressing large-scale phenology problems of the ecological environment.