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Dan Cao
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

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Short Biography

Dan Cao received the B.S. and M.S. degrees from Henan Polytechnic University, in 2015 and 2018, respectively. She is currently pursuing the Ph.D. degree with the Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China. She is mainly engaged in the expertise area of agriculture remote sensing and vegetation change research.

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Journal article
Published: 28 April 2021 in Remote Sensing
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Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

ACS Style

Foyez Prodhan; Jiahua Zhang; Fengmei Yao; Lamei Shi; Til Pangali Sharma; Da Zhang; Dan Cao; Minxuan Zheng; Naveed Ahmed; Hasiba Mohana. Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data. Remote Sensing 2021, 13, 1715 .

AMA Style

Foyez Prodhan, Jiahua Zhang, Fengmei Yao, Lamei Shi, Til Pangali Sharma, Da Zhang, Dan Cao, Minxuan Zheng, Naveed Ahmed, Hasiba Mohana. Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data. Remote Sensing. 2021; 13 (9):1715.

Chicago/Turabian Style

Foyez Prodhan; Jiahua Zhang; Fengmei Yao; Lamei Shi; Til Pangali Sharma; Da Zhang; Dan Cao; Minxuan Zheng; Naveed Ahmed; Hasiba Mohana. 2021. "Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data." Remote Sensing 13, no. 9: 1715.

Journal article
Published: 10 March 2021 in Sustainability
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Understanding the response of terrestrial ecosystems to future climate changes would substantially contribute to the scientific assessment of vegetation–climate interactions. Here, the spatiotemporal distribution and dynamics of vegetation in China were projected and compared based on comprehensive sequential classification system (CSCS) model under representative concentration pathway (RCP) RCP2.6, RCP4.5, and RCP8.5 scenarios, and five sensitivity levels were proposed. The results show that the CSCS model performs well in simulating vegetation distribution. The number of vegetation types would increase from 36 to 40. Frigid–perhumid rain tundra and alpine meadow are the most distributed vegetation types, with an area of more than 78.45 × 104 km2, whereas there are no climate conditions suitable for tropical–extra-arid tropical desert in China. Some plants would benefit from climate changes to a certain extent. Warm temperate–arid warm temperate zone semidesert would expand by more than 1.82% by the 2080s. A continuous expansion of more than 18.81 × 104 km2 and northward shift of more than 124.93 km in tropical forest would occur across all three scenarios. However, some ecosystems would experience inevitable changes. More than 1.33% of cool temperate–extra-arid temperate zone desert would continuously shrink. Five sensitivity levels present an interphase distribution. More extreme scenarios would result in wider ecosystem responses. The evolutionary trend from cold–arid vegetation to warm–wet vegetation is a prominent feature despite the variability in ecosystem responses to climate changes.

ACS Style

Shuaishuai Li; Jiahua Zhang; Sha Zhang; Yun Bai; Dan Cao; Tiantian Cheng; Zhongtai Sun; Qi Liu; Til Sharma. Impacts of Future Climate Changes on Spatio-Temporal Distribution of Terrestrial Ecosystems over China. Sustainability 2021, 13, 3049 .

AMA Style

Shuaishuai Li, Jiahua Zhang, Sha Zhang, Yun Bai, Dan Cao, Tiantian Cheng, Zhongtai Sun, Qi Liu, Til Sharma. Impacts of Future Climate Changes on Spatio-Temporal Distribution of Terrestrial Ecosystems over China. Sustainability. 2021; 13 (6):3049.

Chicago/Turabian Style

Shuaishuai Li; Jiahua Zhang; Sha Zhang; Yun Bai; Dan Cao; Tiantian Cheng; Zhongtai Sun; Qi Liu; Til Sharma. 2021. "Impacts of Future Climate Changes on Spatio-Temporal Distribution of Terrestrial Ecosystems over China." Sustainability 13, no. 6: 3049.

Journal article
Published: 26 March 2020 in Remote Sensing
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With satellite observed Sea Surface Temperature (SST) accumulated for multiple decades, multi-time scale variabilities of the Indo-Pacific Warm Pool are examined and contrasted in this study by separating it into the Indian Ocean sector and the Pacific Ocean sector. Surface size, zonal center, meridional center, maximum SST and mean SST as the practical warm pool properties are chosen to investigate the warm pool variations for the period 1982–2018. On the seasonal time scale, the oscillation of the Indian Warm Pool is found much more vigorous than the Pacific Warm Pool on size and intensity, yet the interannual variabilities of the Indian Warm Pool and the Pacific Warm Pool are comparable. The Indian Warm Pool has weak interannual variations (3–5 years) and the Pacific Warm Pool has mighty interdecadal variations. The size, zonal movement and mean SST of the Indian Ocean Warm Pool (IW) are more accurate to depict the seasonal variability, and for the Pacific Ocean Warm Pool (PW), the size, zonal and meridional movements and maximum SST are more suitable. On the interannual scale, except for the meridional movements, all the other properties of the same basin have similar interannual variation signals. Following the correlation analysis, it turns out that the Indian Ocean basin-wide index (IOBW) is the most important contributor to the variabilities of both sectors. Lead-lag correlation results show that variation of the Pacific Ocean Warm Pool leads the IOBW and variation of the Indian Ocean Warm Pool is synchronous with the IOBW. This indicates that both sectors of the Indo-Pacific Warm Pool are significantly correlated with basin-wide warming or cooling.

ACS Style

Zi Yin; Qing Dong; Fanping Kong; Dan Cao; Shuang Long. Seasonal and Interannual Variability of the Indo-Pacific Warm Pool and its Associated Climate Factors Based on Remote Sensing. Remote Sensing 2020, 12, 1062 .

AMA Style

Zi Yin, Qing Dong, Fanping Kong, Dan Cao, Shuang Long. Seasonal and Interannual Variability of the Indo-Pacific Warm Pool and its Associated Climate Factors Based on Remote Sensing. Remote Sensing. 2020; 12 (7):1062.

Chicago/Turabian Style

Zi Yin; Qing Dong; Fanping Kong; Dan Cao; Shuang Long. 2020. "Seasonal and Interannual Variability of the Indo-Pacific Warm Pool and its Associated Climate Factors Based on Remote Sensing." Remote Sensing 12, no. 7: 1062.