Detection of water deficit conditions in different soils by comparative analysis of standard precipitation index and normalized difference vegetation index

被引:4
作者
Medida, Sunil Kumar [1 ]
Rani, P. Prasuna [1 ]
Kumar, G. V. Suneel [1 ]
Sireesha, P. V. Geetha [1 ]
Kranthi, K. C. [1 ]
Vinusha, V. [1 ]
Sneha, L. [1 ]
Naik, B. S. S. S. [2 ]
Pramanick, Biswajit [3 ]
Brestic, Marian [4 ]
Gaber, Ahmed [5 ]
Hossain, Akbar [6 ]
机构
[1] ANGRAU, Geospatial Technol Ctr, RARS, Guntur 522034, Andhra Pradesh, India
[2] ANGRAU, Dept Agron, Guntur 522034, Andhra Pradesh, India
[3] Dr Rajendra Prasad Cent Agr Univ, Dept Agron, Pusa 848125, Bihar, India
[4] Slovak Univ Agr, Inst Plant & Environm Sci, Tr A Hlinku 2, Nitra 94901, Slovakia
[5] Taif Univ, Coll Sci, Dept Biol, POB 11099, Taif 21944, Saudi Arabia
[6] Bangladesh Wheat & Maize Res Inst, Div Soil Sci, Dinajpur 5200, Bangladesh
关键词
NDVI; SPI; Combined drought index; Agricultural drought; Light and heavy soils; AGRICULTURAL DROUGHT; RIVER-BASIN; NDVI; PATTERNS; REGION; CLIMATOLOGY; STRESS; SEASON; YIELD; AREA;
D O I
10.1016/j.heliyon.2023.e15093
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The detection of water deficit conditions in different soils of Prakasam district, Andhra Pradesh, India was assessed in consecutive two seasons of 2017-18 to 2019-20 cropping seasons using combined indicators developed from Standard Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI). Historical rainfall data during the study period of 56 administrative units were analyzed by using R software and derived three-month SPI. The MODIS satellite data from 2007 to 2020 was downloaded out of which the first ten years' data was used as mean monthly NDVI and the remaining period data was used to derive the anomaly index for the specific month. MODIS satellite data was downloaded, using LST and NDVI, and MSI values were calculated. The NDVI anomaly was derived using MODIS data to study the onset and intensity of water deficit conditions. Results indicated that SPI values gradually increased from the start of the Kharif season, reached their maximum during the August and September months, and decreased gradually with high variation among the mandals. The NDVI anomaly values were highest in October and December the for Kharif and Rabi seasons, respectively. The correlation coefficient between NDVI anomaly and SPI reveals that 79% and 61% of the variation were observed in light and heavy textured soils. The SPI values of -0.5 and -0.75; the NDVI anomaly values of -1.0 and -1.5 and SMI values of 0.28 and 0.26 were established as the thresholds for the onset of water deficit conditions in light and heavy textured soils, respectively. Overall, results suggest that the combined use of SMI, SPI, and NDVI anomaly is capable to provide a near-real-time indicator for water deficit conditions in light and heavy texture soils. Yield reduction was higher in lighttextured soils ranging from 6.1 to 34.5%. These results can further be used in devising tactics for the effective mitigation of drought.
引用
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页数:17
相关论文
共 59 条
[1]   Using SPOT data and leaf area index for rice yield estimation in Egyptian Nile delta [J].
Aboelghar, M. ;
Arafat, S. ;
Yousef, M. Abo ;
El-Shirbeny, M. ;
Naeem, S. ;
Massoud, A. ;
Saleh, N. .
EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES, 2011, 14 (02) :81-89
[2]   Analysis of Vegetation Behavior in a North African Semi-Arid Region, Using SPOT-VEGETATION NDVI Data [J].
Amri, Rim ;
Zribi, Mehrez ;
Lili-Chabaane, Zohra ;
Duchemin, Benoit ;
Gruhier, Claire ;
Chehbouni, Abdelghani .
REMOTE SENSING, 2011, 3 (12) :2568-2590
[3]   Brassinolide Application Improves the Drought Tolerance in Maize Through Modulation of Enzymatic Antioxidants and Leaf Gas Exchange [J].
Anjum, S. A. ;
Wang, L. C. ;
Farooq, M. ;
Hussain, M. ;
Xue, L. L. ;
Zou, C. M. .
JOURNAL OF AGRONOMY AND CROP SCIENCE, 2011, 197 (03) :177-185
[4]  
[Anonymous], 2016, QGIS GEOGR INF SYST
[5]  
Anyamba A, 2001, INT J REMOTE SENS, V22, P1847
[6]   Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya [J].
Barrett, Adam B. ;
Duivenvoorden, Steven ;
Salakpi, Edward E. ;
Muthoka, James M. ;
Mwangi, John ;
Oliver, Seb ;
Rowhani, Pedram .
REMOTE SENSING OF ENVIRONMENT, 2020, 248
[7]   Drought risk assessment using remote sensing and GIS techniques [J].
Belal, Abdel-Aziz ;
El-Ramady, Hassan R. ;
Mohamed, Elsayed S. ;
Saleh, Ahmed M. .
ARABIAN JOURNAL OF GEOSCIENCES, 2014, 7 (01) :35-53
[8]  
Boken V.K., 2005, MONITORING PREDICTIN, P369
[9]   Drought forecasting using the standardized precipitation index [J].
Cancelliere, A. ;
Di Mauro, G. ;
Bonaccorso, B. ;
Rossi, G. .
WATER RESOURCES MANAGEMENT, 2007, 21 (05) :801-819
[10]   Monitoring the Spring 2021 Drought Event in Taiwan Using Multiple Satellite-Based Vegetation and Water Indices [J].
Chou, Chien-Ben ;
Weng, Min-Chuan ;
Huang, Huei-Ping ;
Chang, Yu-Cheng ;
Chang, Ho-Chin ;
Yeh, Tzu-Ying .
ATMOSPHERE, 2022, 13 (09)