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Jiahua Zhang received the Ph.D. degree in cartography and remote sensing from the Institute of Remote Sensing Applications, Chinese Academy of Sciences (CAS), Beijing, China, in 1998. Since 2002, he has been a Professor with the Chinese Academy of Meteorological Sciences. Since 2012, he has been a Full Professor with the Institute of Remote Sensing and Digital Earth, CAS. Since 2017, he also has been a Professor with Qingdao University, China. He has published more than 150 peer-review articles. His current research interests include remote sensing and geosciences, environment and disaster monitoring, and image processing and 30 international conferences articles.
Using 14 years (2007–2020) of data from passive (MODIS/Aqua) and active (CALIOP/CALIPSO) satellite measurements over China, we investigate (1) the temporal and spatial variation of aerosol properties over the Beijing–Tianjin–Hebei (BTH) region, the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) and (2) the vertical distribution of aerosol types and extinction coefficients for different aerosol optical depth (AOD) and meteorological conditions. The results show the different spatial patterns and seasonal variations of the AOD over the three regions. Annual time series reveal the occurrence of AOD maxima in 2011 over the YRD and in 2012 over the BTH and PRD; thereafter the AOD decreases steadily. Using the CALIOP vertical feature mask, the relative frequency of occurrence (rFO) of each aerosol type in the atmospheric column is analyzed: rFOs of dust and polluted dust decrease from north to south; rFOs of clean ocean, polluted continental, clean continental and elevated smoke aerosol increase from north to south. In the vertical, the peak frequency of occurrence (FO) for each aerosol type depends on region and season and varies with AOD and meteorological conditions. In general, three distinct altitude ranges are observed with the peak FO at the surface (clean continental and clean marine aerosol), at ∼1 km (polluted dust and polluted continental aerosol) and at ∼3 km (elevated smoke aerosol), whereas dust aerosol may occur over the whole altitude range considered in this study (from the surface up to 8 km). The designation of the aerosol type in different height ranges may to some extent reflect the CALIOP aerosol type classification approach. Air mass trajectories indicate the different source regions for the three study areas and for the three different altitude ranges over each area. In this study nighttime CALIOP profiles are used. The comparison with daytime profiles shows substantial differences in the FO profiles with altitude, which suggest effects of boundary layer dynamics and aerosol transport on the vertical distribution of aerosol types, although differences due to day–night CALIOP performance cannot be ruled out.
Yuqin Liu; Tao Lin; Juan Hong; Yonghong Wang; Lamei Shi; Yiyi Huang; Xian Wu; Hao Zhou; Jiahua Zhang; Gerrit de Leeuw. Multi-dimensional satellite observations of aerosol properties and aerosol types over three major urban clusters in eastern China. Atmospheric Chemistry and Physics 2021, 21, 12331 -12358.
AMA StyleYuqin Liu, Tao Lin, Juan Hong, Yonghong Wang, Lamei Shi, Yiyi Huang, Xian Wu, Hao Zhou, Jiahua Zhang, Gerrit de Leeuw. Multi-dimensional satellite observations of aerosol properties and aerosol types over three major urban clusters in eastern China. Atmospheric Chemistry and Physics. 2021; 21 (16):12331-12358.
Chicago/Turabian StyleYuqin Liu; Tao Lin; Juan Hong; Yonghong Wang; Lamei Shi; Yiyi Huang; Xian Wu; Hao Zhou; Jiahua Zhang; Gerrit de Leeuw. 2021. "Multi-dimensional satellite observations of aerosol properties and aerosol types over three major urban clusters in eastern China." Atmospheric Chemistry and Physics 21, no. 16: 12331-12358.
Urbanization is an increasing phenomenon around the world, causing many adverse effects in urban areas. Urban heat island is are of the most well-known phenomena. In the present study, surface urban heat islands (SUHI) were studied for seven megacities of the South Asian countries from 2000–2019. The urban thermal environment and relationship between land surface temperature (LST), land use landcover (LULC) and vegetation were examined. The connection was explored with remote-sensing indices such as urban thermal field variance (UTFVI), surface urban heat island intensity (SUHII) and normal difference vegetation index (NDVI). LULC maps are classified using a CART machine learning classifier, and an accuracy table was generated. The LULC change matrix shows that the vegetated areas of all the cities decreased with an increase in the urban areas during the 20 years. The average LST in the rural areas is increasing compared to the urban core, and the difference is in the range of 1–2 (°C). The SUHII linear trend is increasing in Delhi, Karachi, Kathmandu, and Thimphu, while decreasing in Colombo, Dhaka, and Kabul from 2000–2019. UTFVI has shown the poor ecological conditions in all urban buffers due to high LST and urban infrastructures. In addition, a strong negative correlation between LST and NDVI can be seen in a range of −0.1 to −0.6.
Talha Hassan; Jiahua Zhang; Foyez Ahmed Prodhan; Til Prasad Pangali Sharma; Barjeece Bashir. Surface Urban Heat Islands Dynamics in Response to LULC and Vegetation across South Asia (2000–2019). Remote Sensing 2021, 13, 3177 .
AMA StyleTalha Hassan, Jiahua Zhang, Foyez Ahmed Prodhan, Til Prasad Pangali Sharma, Barjeece Bashir. Surface Urban Heat Islands Dynamics in Response to LULC and Vegetation across South Asia (2000–2019). Remote Sensing. 2021; 13 (16):3177.
Chicago/Turabian StyleTalha Hassan; Jiahua Zhang; Foyez Ahmed Prodhan; Til Prasad Pangali Sharma; Barjeece Bashir. 2021. "Surface Urban Heat Islands Dynamics in Response to LULC and Vegetation across South Asia (2000–2019)." Remote Sensing 13, no. 16: 3177.
As climate change intensifies, surface vegetation productivity and carbon exchange between terrestrial ecosystems and the atmosphere are significantly affected by the variation of climatic factors. Due to the sensitivity of grasslands to these climatic factors, it is crucial to understand the response of vegetation greenness, or carbon exchange within grasslands, to environment factor dynamics. In this study, we used solar-induced chlorophyll fluorescence (SIF), precipitation (P), vapor pressure deficit (VPD), evaporative stress (ES), and root zone soil moisture (RSM) derived from remote sensing, reanalysis, and assimilation datasets to explore the response of vegetation greenness within Eurasian Steppe to climatic factors. Our results indicated deseasonlization based on the Seasonal-Trend decomposition using Loess (STL) method, which was an effective means to remove the seasonality disturbances that affect the qualification of the relationship between SIF and the four climatic factors. The response of SIF had a time lag effect on these climatic factors, and the longer the response period, the greater the impact on the correlation of SIF with P, VPD, ES, and RSM. We also found, among the four factors, that the response of SIF to ES was the timeliest. The findings of this study emphasized the impact of the seasonality and time lag effect on the dynamic response between variables, and provided references to the attribution and monitoring of vegetation greenness and ecosystem productivity.
Qi Liu; Quan Liu; Xianglei Meng; Jiahua Zhang; Fengmei Yao; Hairu Zhang. The Impact of Seasonality and Response Period on Qualifying the Relationship between Ecosystem Productivity and Climatic Factors over the Eurasian Steppe. Remote Sensing 2021, 13, 3159 .
AMA StyleQi Liu, Quan Liu, Xianglei Meng, Jiahua Zhang, Fengmei Yao, Hairu Zhang. The Impact of Seasonality and Response Period on Qualifying the Relationship between Ecosystem Productivity and Climatic Factors over the Eurasian Steppe. Remote Sensing. 2021; 13 (16):3159.
Chicago/Turabian StyleQi Liu; Quan Liu; Xianglei Meng; Jiahua Zhang; Fengmei Yao; Hairu Zhang. 2021. "The Impact of Seasonality and Response Period on Qualifying the Relationship between Ecosystem Productivity and Climatic Factors over the Eurasian Steppe." Remote Sensing 13, no. 16: 3159.
Drought has devastating impacts on agriculture and other ecosystems, and its occurrence is expected to increase in the future. However, its spatiotemporal impacts on net primary productivity (NPP) in Mongolia have remained uncertain. Hence, this paper focuses on the impact of drought on NPP in Mongolia. The drought events in Mongolia during 2003–2018 were identified using the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The Boreal Ecosystem Productivity Simulator (BEPS)-derived NPP was computed to assess changes in NPP during the 16 years, and the impacts of drought on the NPP of Mongolian terrestrial ecosystems was quantitatively analyzed. The results showed a slightly increasing trend of the growing season NPP during 2003–2018. However, a decreasing trend of NPP was observed during the six major drought events. A total of 60.55–87.75% of land in the entire country experienced drought, leading to a 75% drop in NPP. More specifically, NPP decline was prominent in severe drought areas than in mild and moderate drought areas. Moreover, this study revealed that drought had mostly affected the sparse vegetation NPP. In contrast, forest and shrubland were the least affected vegetation types.
Lkhagvadorj Nanzad; Jiahua Zhang; Battsetseg Tuvdendorj; Shanshan Yang; Sonam Rinzin; Foyez Prodhan; Til Sharma. Assessment of Drought Impact on Net Primary Productivity in the Terrestrial Ecosystems of Mongolia from 2003 to 2018. Remote Sensing 2021, 13, 2522 .
AMA StyleLkhagvadorj Nanzad, Jiahua Zhang, Battsetseg Tuvdendorj, Shanshan Yang, Sonam Rinzin, Foyez Prodhan, Til Sharma. Assessment of Drought Impact on Net Primary Productivity in the Terrestrial Ecosystems of Mongolia from 2003 to 2018. Remote Sensing. 2021; 13 (13):2522.
Chicago/Turabian StyleLkhagvadorj Nanzad; Jiahua Zhang; Battsetseg Tuvdendorj; Shanshan Yang; Sonam Rinzin; Foyez Prodhan; Til Sharma. 2021. "Assessment of Drought Impact on Net Primary Productivity in the Terrestrial Ecosystems of Mongolia from 2003 to 2018." Remote Sensing 13, no. 13: 2522.
Drought is pervasive global hazard and seriously impacts ecology. Particularly, vegetation drought, which is chiefly driven by soil moisture (SM) deficiency, has a direct bearing on grain production and human livelihoods. Various drought indices associated with vegetation and SM conditions have been proposed to monitor and detect vegetation drought. In this study, we evaluated the performance of eight drought indices, including Drought Severity Index (DSI), Evaporation Stress Index (ESI), Normalized Vegetation Supply Water Index (NVSWI), Temperature-Vegetation Dryness Index (TVDI), Temperature Vegetation Precipitation Dryness Index (TVPDI), Vegetation Health Index (VHI), Self-calibrating Palmer Drought Severity Index (SC-PDSI) and Standardized Precipitation Evapotranspiration Index (SPEI), for capturing SM dynamic (derived from Copernicus Climate Change Service) across the six main vegetation coverage types of China. Our results showed DSI and ESI had the best overall performance. When exploring the reasons for the uncertainty of these indices (except SC-PDSI and SPEI) in the evaluation, we found that, in the non-arable regions, the time lag effect of drought indices on SM, the average state and rangeability of corresponding variables and the climatic conditions (precipitation and temperature) all impacted the performance of DSI, ESI, NVSWI, TVPDI and VHI. In the arable region, cropland types (paddy field and non-paddy field) and the uncertainty of SM data mainly caused the uncertainties of the above five indices. With regard to the TVDI, abnormalities of dry and wet edges fitting may be the primary factor affecting its performance. These results demonstrated that these drought indices with reliable and robust performance of capturing SM dynamics can be suggested to characterize the trend of SM. Certainly, this study can provide a reference for the improvement of existing drought indices and the establishment of new drought indices.
Qi Liu; Jiahua Zhang; Hairu Zhang; Fengmei Yao; Yun Bai; Sha Zhang; Xianglei Meng; Quan Liu. Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China. Science of The Total Environment 2021, 789, 147803 .
AMA StyleQi Liu, Jiahua Zhang, Hairu Zhang, Fengmei Yao, Yun Bai, Sha Zhang, Xianglei Meng, Quan Liu. Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China. Science of The Total Environment. 2021; 789 ():147803.
Chicago/Turabian StyleQi Liu; Jiahua Zhang; Hairu Zhang; Fengmei Yao; Yun Bai; Sha Zhang; Xianglei Meng; Quan Liu. 2021. "Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China." Science of The Total Environment 789, no. : 147803.
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.
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 StyleFoyez 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 StyleFoyez 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.
Basin geomorphology is a complete system of landforms and topographic features that play a crucial role in the basin-scale flood risk evaluation. Nepal is a country characterized by several rivers and under the influence of frequent floods. Therefore, identifying flood risk areas is of paramount importance. The East Rapti River, a tributary of the Ganga River, is one of the flood-affected basins, where two major cities are located, making it crucial to assess and mitigate flood risk in this river basin. A morphometric calculation was made based on the Shuttle Radar Topographic Mission (SRTM) 30-m Digital Elevation Model (DEM) in the Geographic Information System (GIS) environment. The watershed, covering 3037.29 km2 of the area has 14 sub-basins (named as basin A up to N), where twenty morphometric parameters were used to identify flash flood potential sub-basins. The resulting flash flood potential maps were categorized into five classes ranging from very low to very high-risk. The result shows that the drainage density, topographic relief, and rainfall intensity have mainly contributed to flash floods in the study area. Hence, flood risk was analyzed pixel-wise based on slope, drainage density, and precipitation. Existing landcover types extracted from the potential risk area indicated that flash flood is more frequent along the major Tribhuvan Rajpath highway. The landcover data shows that human activities are highly concentrated along the west (Eastern part of Bharatpur) and the east (Hetauda) sections. The study concludes that the high human concentrated sub-basin “B” has been categorized as a high flood risk sub-basin; hence, a flood-resilient city planning should be prioritized in the basin.
Til Pangali Sharma; Jiahua Zhang; Narendra Khanal; Foyez Prodhan; Lkhagvadorj Nanzad; Da Zhang; Pashupati Nepal. A Geomorphic Approach for Identifying Flash Flood Potential Areas in the East Rapti River Basin of Nepal. ISPRS International Journal of Geo-Information 2021, 10, 247 .
AMA StyleTil Pangali Sharma, Jiahua Zhang, Narendra Khanal, Foyez Prodhan, Lkhagvadorj Nanzad, Da Zhang, Pashupati Nepal. A Geomorphic Approach for Identifying Flash Flood Potential Areas in the East Rapti River Basin of Nepal. ISPRS International Journal of Geo-Information. 2021; 10 (4):247.
Chicago/Turabian StyleTil Pangali Sharma; Jiahua Zhang; Narendra Khanal; Foyez Prodhan; Lkhagvadorj Nanzad; Da Zhang; Pashupati Nepal. 2021. "A Geomorphic Approach for Identifying Flash Flood Potential Areas in the East Rapti River Basin of Nepal." ISPRS International Journal of Geo-Information 10, no. 4: 247.
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.
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 StyleShuaishuai 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 StyleShuaishuai 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.
Satellite-borne multispectral data are suitable for regional-scale grassland community classification owing to comprehensive coverage. However, the spectral similarity of different communities makes it challenging to distinguish them based on a single multispectral data. To address this issue, we proposed a support vector machine (SVM)–based method integrating multispectral data, two-band enhanced vegetation index (EVI2) time-series, and phenological features extracted from Chinese GaoFen (GF)-1/6 satellite with (16 m ) spatial and (2 d) temporal resolution. To obtain cloud-free images, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm was employed in this study. By using the algorithm on the coarse cloudless images at the same or similar time as the fine images with cloud cover, the cloudless fine images were obtained, and the cloudless EVI2 time-series and phenological features were generated. The developed method was applied to identify grassland communities in Ordos, China. The results show that the Caragana pumila Pojark, Caragana davazamcii Sanchir and Salix schwerinii E. L. Wolf grassland, the Potaninia mongolica Maxim, Ammopiptanthus mongolicus S. H. Cheng and Tetraena mongolica Maxim grassland, the Caryopteris mongholica Bunge and Artemisia ordosica Krasch grassland, the Calligonum mongolicum Turcz grassland, and the Stipa breviflora Griseb and Stipa bungeana Trin grassland are distinguished with an overall accuracy of 87.25%. The results highlight that, compared to multispectral data only, the addition of EVI2 time-series and phenological features improves the classification accuracy by 9.63% and 14.7%, respectively, and even by 27.36% when these two features are combined together, and indicate the advantage of the fine images in this study, compared to 500 m moderate-resolution imaging spectroradiometer (MODIS) data, which are commonly used for grassland classification at regional scale, while using 16 m GF data suggests a 23.96% increase in classification accuracy with the same extracted features. This study indicates that the proposed method is suitable for regional-scale grassland community classification.
Zhenjiang Wu; Jiahua Zhang; Fan Deng; Sha Zhang; Da Zhang; Lan Xun; Tehseen Javed; Guizhen Liu; Dan Liu; Mengfei Ji. Fusion of GF and MODIS Data for Regional-Scale Grassland Community Classification with EVI2 Time-Series and Phenological Features. Remote Sensing 2021, 13, 835 .
AMA StyleZhenjiang Wu, Jiahua Zhang, Fan Deng, Sha Zhang, Da Zhang, Lan Xun, Tehseen Javed, Guizhen Liu, Dan Liu, Mengfei Ji. Fusion of GF and MODIS Data for Regional-Scale Grassland Community Classification with EVI2 Time-Series and Phenological Features. Remote Sensing. 2021; 13 (5):835.
Chicago/Turabian StyleZhenjiang Wu; Jiahua Zhang; Fan Deng; Sha Zhang; Da Zhang; Lan Xun; Tehseen Javed; Guizhen Liu; Dan Liu; Mengfei Ji. 2021. "Fusion of GF and MODIS Data for Regional-Scale Grassland Community Classification with EVI2 Time-Series and Phenological Features." Remote Sensing 13, no. 5: 835.
The timely and accurate information of rape-cultivated area in Jianghan Plain is of great significance for crop yield estimation and food security in China. MODIS-EVI time series images were used to estimate rape-cultivated area of Jianghan Plain in 2015–2017 based on decision tree and mixed pixel decomposition methods. By analyzing the features of EVI time series curves of different objects, rape distribution was preliminarily extracted by establishing the decision tree model; however, due to the problem of mixed pixels, there are heterogeneous objects in a single pixel, which will affect the extraction accuracy of crop area by decision tree method. To solve this problem, according to EVI spectral curve of the endmembers, the linear spectral mixture model was adopted to further improve the precision of crop extraction, and rape-cultivated area was extracted accurately by using computed rape endmember abundance. The results showed that the average estimation accuracy of rape area was 96.6% with city-level R2 (coefficient of determination) greater than 0.93. It demonstrated that the vegetation index time series data with medium resolution can accurately extract the rape-cultivated area, which can provide reference for rape monitoring over a large scale.
Huan Yang; Fan Deng; Hancong Fu; Jiahua Zhang. Estimation of Rape-Cultivated Area Based on Decision Tree and Mixed Pixel Decomposition. Journal of the Indian Society of Remote Sensing 2021, 49, 1285 -1292.
AMA StyleHuan Yang, Fan Deng, Hancong Fu, Jiahua Zhang. Estimation of Rape-Cultivated Area Based on Decision Tree and Mixed Pixel Decomposition. Journal of the Indian Society of Remote Sensing. 2021; 49 (6):1285-1292.
Chicago/Turabian StyleHuan Yang; Fan Deng; Hancong Fu; Jiahua Zhang. 2021. "Estimation of Rape-Cultivated Area Based on Decision Tree and Mixed Pixel Decomposition." Journal of the Indian Society of Remote Sensing 49, no. 6: 1285-1292.
Precipitation and temperature are critical climatic variables that drive catastrophic climatic events including droughts and floods. These variables continue to fluctuate, thereby producing even more extreme weather events across different parts of East African region. Using quantile linear regression (QLR) method, this study interrogated wet and dry conditions over a period of 34 years across East African region. The spatio-temporal quantile trends (time coefficient of precipitation) analysis is presented in 5 conditions (quantiles): extreme dry (1st), dry (10th), median (50th), wet (90th) and extreme wet (99th). For annual precipitation, the quantiles indicated a trend value of − 0.294, 0.205, − 0.425, − 0.069 and 0.145, respectively. This shows that the extreme dry (wet) values in annual mean precipitation over the region are decreasing (increasing) over time, while the reverse is the case for the long and short seasons. Differences in the regression coefficients of precipitation variables for the inter-quantile differences show that any increase or decrease in average precipitation changes the shape of the distribution of hydrological parameters, increasing or decreasing spread between the extreme quantiles. The precipitation deciles at different quantiles over 34 years reveal marked variations in the annual mean and the long and short rainy seasons. Finally, the results indicate significant variations in extreme wet and dry conditions across eight ecological zones in East Africa with variable slope along various quantiles. In conclusion, QLR method has shown the ability to provide superior detailed information on extreme wet and dry climatic conditions required for flood mitigation and water resources planning and management.
Wilson Kalisa; Tertsea Igbawua; Fanan Ujoh; Igbalumun S. Aondoakaa; Jean Nepomuscene Namugize; Jiahua Zhang. Spatio-temporal variability of dry and wet conditions over East Africa from 1982 to 2015 using quantile regression model. Natural Hazards 2021, 106, 2047 -2076.
AMA StyleWilson Kalisa, Tertsea Igbawua, Fanan Ujoh, Igbalumun S. Aondoakaa, Jean Nepomuscene Namugize, Jiahua Zhang. Spatio-temporal variability of dry and wet conditions over East Africa from 1982 to 2015 using quantile regression model. Natural Hazards. 2021; 106 (3):2047-2076.
Chicago/Turabian StyleWilson Kalisa; Tertsea Igbawua; Fanan Ujoh; Igbalumun S. Aondoakaa; Jean Nepomuscene Namugize; Jiahua Zhang. 2021. "Spatio-temporal variability of dry and wet conditions over East Africa from 1982 to 2015 using quantile regression model." Natural Hazards 106, no. 3: 2047-2076.
Accurate rice area extraction and yield simulations are important for understanding how national agricultural policies and environmental issues affect regional spatial changes in rice farming. In this study, an improved regional parametric syntheses approach, that is, the rice zoning adaptability criteria and dynamic harvest index (RZAC-DHI), was established, which can effectively simulate the rice cultivation area and yield at the municipal level. The RZAC was used to extract the rice area using Moderate Resolution Imaging Spectroradiometer time-series data and phenological information. The DHI was calculated independently, and then yield was obtained based on the DHI and net primary productivity (NPP). Based on the above results, we analyzed the spatial–temporal patterns of the rice cultivation area and yield in Northeast China (NEC) during 2000–2015. The results revealed that the methods established in this study can effectively support the yearly mapping of the rice area and yield in NEC, the average precisions of which exceed 90 and 80%, respectively. The rice planting areas are mainly located on the Sanjiang, Songnen and Liaohe plains, China, which are distributed along the Songhua and Liaohe rivers. The rice cultivation area and yield in this region increased significantly from 2000 to 2015, with increases of nearly 58 and 90%, respectively. The rice crop area and yield increased the fastest in Heilongjiang Province, China, whereas small changes occurred in Jilin and Liaoning provinces, China. Their gravity centers exhibited evident northward and eastward shifts, with offset distances of 107 and 358 km, respectively. Moreover, Heilongjiang Province has gradually become the new main rice production region. The methodologies used in this study provide a valuable reference for other related studies, and the spatial-temporal variation characteristics of the rice activities have raised new attention as to how these shifts affect national food security and resource allocation.
Dan Cao; Jian-Zhong Feng; Lin-Yan Bai; Lan Xun; Hai-Tao Jing; Jin-Ke Sun; Jia-Hua Zhang. Delineating the rice crop activities in Northeast China through regional parametric synthesis using satellite remote sensing time-series data from 2000 to 2015. Journal of Integrative Agriculture 2021, 20, 424 -437.
AMA StyleDan Cao, Jian-Zhong Feng, Lin-Yan Bai, Lan Xun, Hai-Tao Jing, Jin-Ke Sun, Jia-Hua Zhang. Delineating the rice crop activities in Northeast China through regional parametric synthesis using satellite remote sensing time-series data from 2000 to 2015. Journal of Integrative Agriculture. 2021; 20 (2):424-437.
Chicago/Turabian StyleDan Cao; Jian-Zhong Feng; Lin-Yan Bai; Lan Xun; Hai-Tao Jing; Jin-Ke Sun; Jia-Hua Zhang. 2021. "Delineating the rice crop activities in Northeast China through regional parametric synthesis using satellite remote sensing time-series data from 2000 to 2015." Journal of Integrative Agriculture 20, no. 2: 424-437.
Exploration of the diurnal pattern of global dimming and brightening has been limited by the paucity of high temporal resolution observations. Based on 22‐years’ continuous observations of hourly surface solar radiation (SSR) over 96 stations across China for 1993–2014, this study evidences higher relative changes in diurnal SSR in terms of both frequency and magnitude at sunrise and sunset, with a longer pathway for the sun passing through the atmosphere, than the hours in between. This, in general, leads to a further shortening of the days between sunrise and sunset in the cold seasons and the polluted region of north China with low radiation levels but a further extension in the other seasons and regions with sufficient sunlight. Pollution reduction by 39.4%–69.7% during the 2014 Asia‐Pacific Economic Cooperation summit at Beijing are accompanied with a 10.4%–80.0% rebound of diurnal SSR in the forenoon and late afternoon.
Yawen Wang; Jiahua Zhang; Arturo Sanchez‐Lorenzo; Katsumasa Tanaka; Jörg Trentmann; Wenping Yuan; Martin Wild. Hourly Surface Observations Suggest Stronger Solar Dimming and Brightening at Sunrise and Sunset Over China. Geophysical Research Letters 2021, 48, 1 .
AMA StyleYawen Wang, Jiahua Zhang, Arturo Sanchez‐Lorenzo, Katsumasa Tanaka, Jörg Trentmann, Wenping Yuan, Martin Wild. Hourly Surface Observations Suggest Stronger Solar Dimming and Brightening at Sunrise and Sunset Over China. Geophysical Research Letters. 2021; 48 (2):1.
Chicago/Turabian StyleYawen Wang; Jiahua Zhang; Arturo Sanchez‐Lorenzo; Katsumasa Tanaka; Jörg Trentmann; Wenping Yuan; Martin Wild. 2021. "Hourly Surface Observations Suggest Stronger Solar Dimming and Brightening at Sunrise and Sunset Over China." Geophysical Research Letters 48, no. 2: 1.
Evaluating the climate potential productivity (CPP) of terrestrial vegetation is crucial to ascertain the threshold of vegetation productivity, to maximize the utilization of regional climate resources, and to fully display the productivity application level. In this study, the maximum net primary productivity (NPPmax) representing the highest possible productivity of vegetation was calculated using the FLUXNET maximum gross primary productivity (GPPmax) from 177 flux towers. The relationships between NPPmax and a set of climate variables were established using the classification and regression tree (CART) modeling framework. The CART algorithm was used to upscale the CPP to the global scale under the current climate baseline (1980–2018) and future climate scenarios. The spatiotemporal variations in CPP over the globe were analyzed and the impacts of climate factors on it were assessed. The results indicate that global CPPs range from 0 to 2000 g C/m2. The tropical rainforest area is the region with the highest CPP, whereas the lowest CPP occurs in arid/semiarid areas. These two regions were identified as the areas with the largest CPP reductions in the future. The findings reveal that CPP shows signs of productivity saturation and that future climate is not conducive to the increases in vegetation productivity in these regions. The increases in average annual temperature, minimum temperature, and solar radiation are beneficial to CPP increase in most parts of the globe under climate change. However, the negative contribution of maximum temperature increase and precipitation reduction to CPP is higher than the positive contribution of the above three rising factors to CPP in tropical and arid/semiarid areas. Our study is important to aid in creating targeted policies for future sustainable development, resource allocation, and vegetation management.
Dan Cao; Jiahua Zhang; Lan Xun; Shanshan Yang; Jingwen Wang; Fengmei Yao. Spatiotemporal variations of global terrestrial vegetation climate potential productivity under climate change. Science of The Total Environment 2021, 770, 145320 .
AMA StyleDan Cao, Jiahua Zhang, Lan Xun, Shanshan Yang, Jingwen Wang, Fengmei Yao. Spatiotemporal variations of global terrestrial vegetation climate potential productivity under climate change. Science of The Total Environment. 2021; 770 ():145320.
Chicago/Turabian StyleDan Cao; Jiahua Zhang; Lan Xun; Shanshan Yang; Jingwen Wang; Fengmei Yao. 2021. "Spatiotemporal variations of global terrestrial vegetation climate potential productivity under climate change." Science of The Total Environment 770, no. : 145320.
Accurately mapping of regional-scale evapotranspiration (ET) from the croplands using remote sensing is currently challenged by limited spatial information on crop and field management to properly characterize the biophysical constraints on ET. A multi-model ensemble can potentially address this challenge, however, conventional ensemble models using the simple average (MEAN) or Bayesian Model Average (BMA) assign a fixed weight to each model and may not fully utilize the strengths of individual models. To this end, we developed four ensemble ET Models (EEMs) that use different machine learning (ML) classifiers, namely K-nearest neighbors, random forest, support vector machine, and multi-layer perception neural network (MLP), to assign varying weights to assemble six physically-driven remote sensing-based ET models. These ML-based EEMs were compared against the six individual ET models and two conventional ensemble methods (MEAN and BMA) using latent heat fluxes (λE) observations from 47 cropland eddy covariance flux sites covering diverse environments across the globe. Results suggested that while MEAN and BMA can reduce some uncertainties in the individual models, ML-based EEMs can better integrate the capabilities of multiple biophysical constraints on ET used across the individual models. The four ML-based EEMs yielded daily λE for training, validation, and testing datasets with the coefficient of determination (R2) and root mean squared error (RMSE) within 0.75 – 0.83 and 18 – 21 W m−2, respectively, among which the MLP algorithm was found to be the most efficient with respect to accuracies and costs. These performance metrics were much better than those from the conventional ensemble models (R2 = 0.69 – 0.71, RMSE = 23 – 25 W m−2) and six individual ET models (R2 = 0.53 – 0.69, RMSE = 26 – 35 W m−2). Results suggested that ML-based EEMs perform much better than the conventional approaches and hence can be viable tools for mapping cropland ET across a wide environmental gradient.
Yun Bai; Sha Zhang; Nishan Bhattarai; Kaniska Mallick; Qi Liu; Lili Tang; Jungho Im; Li Guo; Jiahua Zhang. On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient. Agricultural and Forest Meteorology 2021, 298-299, 108308 .
AMA StyleYun Bai, Sha Zhang, Nishan Bhattarai, Kaniska Mallick, Qi Liu, Lili Tang, Jungho Im, Li Guo, Jiahua Zhang. On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient. Agricultural and Forest Meteorology. 2021; 298-299 ():108308.
Chicago/Turabian StyleYun Bai; Sha Zhang; Nishan Bhattarai; Kaniska Mallick; Qi Liu; Lili Tang; Jungho Im; Li Guo; Jiahua Zhang. 2021. "On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient." Agricultural and Forest Meteorology 298-299, no. : 108308.
Studying the significant impacts of drought on vegetation is crucial to understand its dynamics and interrelationships with precipitation, soil moisture, and temperature. In North and West Africa regions, the effects of drought on vegetation have not been clearly stated. Therefore, the present study aims to bring out the drought fluctuations within various types of Land Cover (LC) (Grasslands, Croplands, Savannas, and Forest) in North and West Africa regions. The drought characteristics were evaluated by analyzing the monthly Self-Calibrating Palmer Drought Severity Index (scPDSI) in different timescale from 2002 to 2018. Then, the frequency of droughts was examined over the same period. The results have revealed two groups of years (dry years and normal years), based on drought intensity. The selected years were used to compare the shifting between vegetation and desert. The Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), the Precipitation Condition Index (PCI), and the Soil Moisture Condition Index (SMCI) were also used to investigate the spatiotemporal variation of drought and to determine which LC class was more vulnerable to drought risk. Our results revealed that Grasslands and Croplands in the West region, and Grasslands, Croplands, and Savannas in the North region are more sensitive to drought. A higher correlation was observed among the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Tropical Rainfall Measuring Mission (TRMM), and Soil Moisture (SM). Our findings suggested that NDVI, TRMM, and SM are more suitable for monitoring drought over the study area and have a reliable accuracy (R2 > 0.70) concerning drought prediction. The outcomes of the current research could, explicitly, contribute progressively towards improving specific drought mitigation strategies and disaster risk reduction at regional and national levels.
Malak Henchiri; Qi Liu; Bouajila Essifi; Tehseen Javed; Sha Zhang; Yun Bai; Jiahua Zhang. Spatio-Temporal Patterns of Drought and Impact on Vegetation in North and West Africa Based on Multi-Satellite Data. Remote Sensing 2020, 12, 3869 .
AMA StyleMalak Henchiri, Qi Liu, Bouajila Essifi, Tehseen Javed, Sha Zhang, Yun Bai, Jiahua Zhang. Spatio-Temporal Patterns of Drought and Impact on Vegetation in North and West Africa Based on Multi-Satellite Data. Remote Sensing. 2020; 12 (23):3869.
Chicago/Turabian StyleMalak Henchiri; Qi Liu; Bouajila Essifi; Tehseen Javed; Sha Zhang; Yun Bai; Jiahua Zhang. 2020. "Spatio-Temporal Patterns of Drought and Impact on Vegetation in North and West Africa Based on Multi-Satellite Data." Remote Sensing 12, no. 23: 3869.
Heat-health risk is a growing concern in many regions of China due to the more frequent occurrence of extremely hot weather. Spatial indexes based on various heat assessment frameworks can be used for the assessment of heat risks. In this study, we adopted two approaches—Crichton’s risk triangle and heat vulnerability index (HVI) to identify heat-health risks in the Northern Jiangxi Province of China, by using remote sensing and socio-economic data. The Geographical Information System (GIS) overlay and principal component analysis (PCA) were separately used in two frameworks to integrate parameters. The results show that the most densely populated community in the suburbs, instead of city centers, are exposed to the highest heat risk. A comparison of two heat assessment mapping indicates that the distribution of HVI highlights the vulnerability differences between census tracts. In contrast, the heat risk index of Crichton’s risk triangle has a prominent representation for regions with high risks. The stepwise multiple linear regression zero-order correlation coefficient between HVI and outdoor workers is 0.715, highlighting the vulnerability of this particular group. Spearman’s rho nonparametric correlation and the mean test reveals that heat risk index is strongly correlated with HVI in most of the main urban regions in the study area, with a significantly lower value than the latter. The analysis of variance shows that the distribution of HVI exhibits greater variety across urban regions than that of heat risk index. Our research provides new insight into heat risk assessment for further study of heat health risk in developing countries.
Minxuan Zheng; Jiahua Zhang; Lamei Shi; Da Zhang; Til Pangali Sharma; Foyez Prodhan. Mapping Heat-Related Risks in Northern Jiangxi Province of China Based on Two Spatial Assessment Frameworks Approaches. International Journal of Environmental Research and Public Health 2020, 17, 6584 .
AMA StyleMinxuan Zheng, Jiahua Zhang, Lamei Shi, Da Zhang, Til Pangali Sharma, Foyez Prodhan. Mapping Heat-Related Risks in Northern Jiangxi Province of China Based on Two Spatial Assessment Frameworks Approaches. International Journal of Environmental Research and Public Health. 2020; 17 (18):6584.
Chicago/Turabian StyleMinxuan Zheng; Jiahua Zhang; Lamei Shi; Da Zhang; Til Pangali Sharma; Foyez Prodhan. 2020. "Mapping Heat-Related Risks in Northern Jiangxi Province of China Based on Two Spatial Assessment Frameworks Approaches." International Journal of Environmental Research and Public Health 17, no. 18: 6584.
The Himalayan region, a major source of fresh water, is recognized as a water tower of the world. Many perennial rivers originate from Nepal Himalaya, located in the central part of the Himalayan region. Snowmelt water is essential freshwater for living, whereas it poses flood disaster potential, which is a major challenge for sustainable development. Climate change also largely affects snowmelt hydrology. Therefore, river discharge measurement requires crucial attention in the face of climate change, particularly in the Himalayan region. The snowmelt runoff model (SRM) is a frequently used method to measure river discharge in snow-fed mountain river basins. This study attempts to investigate snowmelt contribution in the overall discharge of the Budhi Gandaki River Basin (BGRB) using satellite remote sensing data products through the application of the SRM model. The model outputs were validated based on station measured river discharge data. The results show that SRM performed well in the study basin with a coefficient of determination (R2) >0.880. Moreover, this study found that the moderate resolution imaging spectroradiometer (MODIS) snow cover data and European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological datasets are highly applicable to the SRM in the Himalayan region. The study also shows that snow days have slightly decreased in the last three years, hence snowmelt contribution in overall discharge has decreased slightly in the study area. Finally, this study concludes that MOD10A2 and ECMWF precipitation and two-meter temperature products are highly applicable to measure snowmelt and associated discharge through SRM in the BGRB. Moreover, it also helps with proper freshwater planning, efficient use of winter water flow, and mitigating and preventive measures for the flood disaster.
Til Prasad Pangali Sharma; Jiahua Zhang; Narendra Raj Khanal; Foyez Ahmed Prodhan; Basanta Paudel; Lamei Shi; Nirdesh Nepal. Assimilation of Snowmelt Runoff Model (SRM) Using Satellite Remote Sensing Data in Budhi Gandaki River Basin, Nepal. Remote Sensing 2020, 12, 1951 .
AMA StyleTil Prasad Pangali Sharma, Jiahua Zhang, Narendra Raj Khanal, Foyez Ahmed Prodhan, Basanta Paudel, Lamei Shi, Nirdesh Nepal. Assimilation of Snowmelt Runoff Model (SRM) Using Satellite Remote Sensing Data in Budhi Gandaki River Basin, Nepal. Remote Sensing. 2020; 12 (12):1951.
Chicago/Turabian StyleTil Prasad Pangali Sharma; Jiahua Zhang; Narendra Raj Khanal; Foyez Ahmed Prodhan; Basanta Paudel; Lamei Shi; Nirdesh Nepal. 2020. "Assimilation of Snowmelt Runoff Model (SRM) Using Satellite Remote Sensing Data in Budhi Gandaki River Basin, Nepal." Remote Sensing 12, no. 12: 1951.
The current approaches have known limitations to understanding the coupling of terrestrial ecosystem evapotranspiration (ET) and photosynthesis (referred to as gross primary productivity, GPP). To better characterize the relationship between ET and GPP, we developed a novel remote sensing (RS)-driven approach (RCEEP) based on the underlying water use efficiency (uWUE). RCEEP partitions transpiration (T) from ET using a RS vegetation index (VI)-derived ratio of T to ET (VI-fT) and then links T and GPP via RS VI-derived Gc (VI-Gc) rather than leaf-to-air vapor pressure difference. RCEEP and other two uWUE versions (VI-T or VI-G), which only incorporate VI-fT or VI-Gc , were evaluated and compared with the original uWUE model in terms of their performances (Nash-Sutcliffe efficiency, NSE) in estimating GPP from ET over 180 flux sites covering 11 biome types over the globe. Results revealed better performances of VI-T and VI-G compared to the original uWUE, implying remarkable contributions of VI-fT and VI-Gc to a more meaningful relationship between ET and GPP. RCEEP yielded the best performances with a reasonable mean NSE value of 0.70 (0.76) on a daily (monthly) scale and across all biome types. Further comparisons of RCEEP and approaches modified from recent studies revealed consistently better performances of RCEEP and thus, positive implications of introducing VI-fT and VI-Gc in bridging ecosystem ET and GPP. These results are promising in view of improving or developing algorithms on coupled estimates of ecosystem ET and GPP and understanding the GPP dynamics concerning ET on a global scale.
Yun BaiiD; Sha ZHANGiD; Jiahua Zhang; Shanshan Yang; Jingwen Wang; Enzo MagliuloiD; Luca VitaleiD; Yanchuang Zhao. Remote sensing vegetation indices enhance understanding of the coupling of terrestrial ecosystem evapotranspiration and photosynthesis on a global scale. 2020, 1 .
AMA StyleYun BaiiD, Sha ZHANGiD, Jiahua Zhang, Shanshan Yang, Jingwen Wang, Enzo MagliuloiD, Luca VitaleiD, Yanchuang Zhao. Remote sensing vegetation indices enhance understanding of the coupling of terrestrial ecosystem evapotranspiration and photosynthesis on a global scale. . 2020; ():1.
Chicago/Turabian StyleYun BaiiD; Sha ZHANGiD; Jiahua Zhang; Shanshan Yang; Jingwen Wang; Enzo MagliuloiD; Luca VitaleiD; Yanchuang Zhao. 2020. "Remote sensing vegetation indices enhance understanding of the coupling of terrestrial ecosystem evapotranspiration and photosynthesis on a global scale." , no. : 1.
East African region is susceptible to drought due to high variation in monthly precipitation. Studying drought at regional scale is vital since droughts are considered a ‘creeping’ disaster by nature with devasting and extended impact often requiring long periods to reverse the recorded damages. This study assessed drought exceedance and return years over East Africa from 1920 to 2016 using Climate Research Unit (CRU) precipitation data records. Meteorological drought, where precipitation is the central quantity of interest, was adopted in the work. Standardize Precipitation Index (SPI) was used to study long term meteorological droughts and also to assess drought magnitude, frequency, exceedance probability and return years using Joint Probability Density Function (JPDF). Also, Mann-Kendall trend analysis was applied to precipitation and SPI to investigate the trend changes. Results showed that years with high drought magnitude ranged from 1920−22, 1926−29, 1942−46 and 1947−51 with values corresponding to 2.2, 3.2, 3.4 and 2.6, respectively while years with low drought magnitude ranged from 1930−31, 1988−89 and 2001−02 with values as 0.2, 0.12 and 0.15, respectively. The longest droughts occurred from 1926−29, 1937−41, 1942−46, 1947−51, 1952−56, and 1958−61 with values in years as 3, 4, 4, 4, 4, and 3 years, respectively, while the shortest droughts occurred in time period of 1 year and ranged from 1930−31, 1964−65, 1979−80, 1981−82, 1983−84, 1988−89, 1991−92, 1993−94, 1996−97 and 2001−02. Also, it was demonstrated that probability of drought occurrence is high when severity is low and such droughts occur at short time intervals and not all severest drought took longer periods. The SPI trends indicate high positive (negative) pixels above (below) the zero-trend mark, indicating that drought prevails in both low and high elevation areas up to 2000 m. There was no direct link between ENSO and drought but arguably the association of drought in most El Niño and La Niña years suggests that the impact of ENSO cannot be ruled out since peak ENSO events occur during October to March periods which coincides with the short (SON) and long (MAM) rainy seasons of East Africa. The study is particularly relevant in being able to depict continuous and synoptic drought condition all over East Africa, providing vital information to farmers and policy makers, using very cost-effective method.
Wilson Kalisa; Jiahua Zhang; Tertsea Igbawua; Fanan Ujoh; Obas John Ebohon; Jean Nepomuscene Namugize; Fengmei Yao. Spatio-temporal analysis of drought and return periods over the East African region using Standardized Precipitation Index from 1920 to 2016. Agricultural Water Management 2020, 237, 106195 .
AMA StyleWilson Kalisa, Jiahua Zhang, Tertsea Igbawua, Fanan Ujoh, Obas John Ebohon, Jean Nepomuscene Namugize, Fengmei Yao. Spatio-temporal analysis of drought and return periods over the East African region using Standardized Precipitation Index from 1920 to 2016. Agricultural Water Management. 2020; 237 ():106195.
Chicago/Turabian StyleWilson Kalisa; Jiahua Zhang; Tertsea Igbawua; Fanan Ujoh; Obas John Ebohon; Jean Nepomuscene Namugize; Fengmei Yao. 2020. "Spatio-temporal analysis of drought and return periods over the East African region using Standardized Precipitation Index from 1920 to 2016." Agricultural Water Management 237, no. : 106195.