1.
Gaston, K. J., Jackson, S. F., Cantú-Salazar, L. & Cruz-Piñón, G. The Ecological Performance of Protected Areas. Annu. Rev. Ecol. Evol. Syst. 39, 93–113 (2008).
Article Google Scholar
2.
Williams, S. E. et al. Research priorities for natural ecosystems in a changing global climate. Glob. Change Biol. 26, 410–416 (2020).
ADS Article Google Scholar
3.
Hoffmann, S., Irl, S. D. H. & Beierkuhnlein, C. Predicted climate shifts within terrestrial protected areas worldwide. Nat. Commun. 10, 4787 (2019).
ADS PubMed PubMed Central Article CAS Google Scholar
4.
IUCN & UNEP. The World Database on Protected Areas (WDPA). www.protectedplanet.net. (UNEP-WCMC, 2018).
5.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A. & Ziese, M. GPCC Full Data Monthly Product Version 2018 at 0.25°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historical Data. 10.5676/DWD_GPCC/FD_M_V2018_025; ftp://ftp.dwd.de/pub/data/gpcc/html/fulldata-monthly_v2018_doi_download.html; accessed on 26 March 2019. (2018).
6.
Schneider, U., Finger, P., Meyer-Christoffer, A., Ziese, M. & Becker, A. Global Precipitation Analysis Products of the GPCC. Deutscher Wetterdienst, Abt. Hydrometeorologie, Weltzentrum für Niederschlagsklimatologie (WZN) 17 (2018).
7.
Hofstra, N., Haylock, M., New, M. & Jones, P. D. Testing E-OBS European high-resolution gridded data set of daily precipitation and surface temperature. J. Geophys. Res. 114, D21101 (2009).
ADS Article Google Scholar
8.
Prein, A. F. & Gobiet, A. Impacts of uncertainties in European gridded precipitation observations on regional climate analysis: UNCERTAINTY IN EUROPEAN PRECIPITATION. Int. J. Climatol. 37, 305–327 (2017).
PubMed Article PubMed Central Google Scholar
9.
Zandler, H., Haag, I. & Samimi, C. Evaluation needs and temporal performance differences of gridded precipitation products in peripheral mountain regions. Sci. Rep. 9, 15118 (2019).
ADS PubMed PubMed Central Article CAS Google Scholar
10.
Liu, M. et al. Evaluation of high-resolution satellite rainfall products using rain gauge data over complex terrain in southwest China. Theor. Appl. Climatol. 119, 203–219 (2015).
ADS Article Google Scholar
11.
Fu, Y. et al. Assessment of multiple precipitation products over major river basins of China. Theor. Appl. Climatol. 123, 11–22 (2016).
ADS Article Google Scholar
12.
Hu, Z., Hu, Q., Zhang, C., Chen, X. & Li, Q. Evaluation of reanalysis, spatially interpolated and satellite remotely sensed precipitation data sets in central Asia: Central Asia Precipitation. J. Geophys. Res. Atmos. 121, 5648–5663 (2016).
Article Google Scholar
13.
Hu, Z. et al. Evaluation of three global gridded precipitation data sets in central Asia based on rain gauge observations. Int. J. Climatol. 38, 3475–3493 (2018).
Article Google Scholar
14.
Beck, H. E. et al. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrol. Earth Syst. Sci. 21, 6201–6217 (2017).
ADS CAS Article Google Scholar
15.
Iwasaki, H. NDVI prediction over Mongolian grassland using GSMaP precipitation data and JRA-25/JCDAS temperature data. J. Arid Environ. 73, 557–562 (2009).
ADS Article Google Scholar
16.
Gessner, U. et al. The relationship between precipitation anomalies and satellite-derived vegetation activity in Central Asia. Glob. Planet. Change 110, 74–87 (2013).
ADS Article Google Scholar
17.
Los, S. O. Testing gridded land precipitation data and precipitation and runoff reanalyses (1982–2010) between 45° S and 45° N with normalised difference vegetation index data. Hydrol. Earth Syst. Sci. 19, 1713–1725 (2015).
ADS Article Google Scholar
18.
Papagiannopoulou, C. et al. Vegetation anomalies caused by antecedent precipitation in most of the world. Environ. Res. Lett. 12, 074016 (2017).
ADS Article Google Scholar
19.
Chen, Z., Wang, W. & Fu, J. Vegetation response to precipitation anomalies under different climatic and biogeographical conditions in China. Sci. Rep. 10, 830 (2020).
ADS CAS PubMed PubMed Central Article Google Scholar
20.
Eckert, S., Hüsler, F., Liniger, H. & Hodel, E. Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. J. Arid Environ. 113, 16–28 (2015).
ADS Article Google Scholar
21.
Otto, M., Höpfner, C., Curio, J., Maussion, F. & Scherer, D. Assessing vegetation response to precipitation in northwest Morocco during the last decade: an application of MODIS NDVI and high resolution reanalysis data. Theor. Appl. Climatol. 123, 23–41 (2016).
ADS Article Google Scholar
22.
Formica, A. F., Burnside, R. J. & Dolman, P. M. Rainfall validates MODIS-derived NDVI as an index of spatio-temporal variation in green biomass across non-montane semi-arid and arid Central Asia. J. Arid Environ. 142, 11–21 (2017).
ADS Article Google Scholar
23.
Wang, X., Wu, C., Peng, D., Gonsamo, A. & Liu, Z. Snow cover phenology affects alpine vegetation growth dynamics on the Tibetan Plateau: Satellite observed evidence, impacts of different biomes, and climate drivers. Agric. For. Meteorol. 256–257, 61–74 (2018).
ADS Article Google Scholar
24.
Verbyla, D. & Kurkowski, T. A. NDVI–Climate relationships in high-latitude mountains of Alaska and Yukon Territory. Arct. Antarct. Alp. Res. 51, 397–411 (2019).
Article Google Scholar
25.
Breckle, S.-W. Flora and vegetation of Afghanistan. badr 1, 155–194 (2007).
Article Google Scholar
26.
Bedunah, D. J., Shank, C. C. & Alavi, M. A. Rangelands of Band-e-Amir National Park and Ajar Provisional Wildlife Reserve, Afghanistan. Rangelands 32, 41–52 (2010).
Article Google Scholar
27.
Pohl, E., Knoche, M., Gloaguen, R., Andermann, C. & Krause, P. Sensitivity analysis and implications for surface processes from a hydrological modelling approach in the Gunt catchment, high Pamir Mountains. Earth Surf. Dyn. 3, 333–362 (2015).
ADS Article Google Scholar
28
Soelberg, J. & Jäger, A. K. Comparative ethnobotany of the Wakhi agropastoralist and the Kyrgyz nomads of Afghanistan. J. Ethnobiol. Ethnomed. https://doi.org/10.1186/s13002-015-0063-x (2016).
Article PubMed PubMed Central Google Scholar
29.
Didan, K. MOD13Q1 MODIS/terra vegetation indices 16-day L3 global 250m SIN Grid V006. NASA EOSDIS Land Process. DAAC https://doi.org/10.5067/MODIS/MOD13Q1.006 (2015).
30.
Dinku, T. et al. Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Q. J. R. Meteorol. Soc. 144, 292–312 (2018).
ADS Article Google Scholar
31.
Sun, Q. et al. A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev. Geophys. 56, 79–107 (2018).
ADS Article Google Scholar
32.
Hall, D. K. & Riggs, G. A. MOD10A1 MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 6. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD10A1.006. Accessed on 25 March 2020. (2016).
33.
Wang, K. et al. Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau. Int. J. Digit. Earth 8, 58–75 (2013).
Article Google Scholar
34.
Chen, X., An, S., Inouye, D. W. & Schwartz, M. D. Temperature and snowfall trigger alpine vegetation green-up on the world’s roof. Glob. Change Biol. 21, 3635–3646 (2015).
ADS Article Google Scholar
35.
Asam, S. et al. Relationship between spatiotemporal variations of climate, snow cover and plant phenology over the Alps—an earth observation-based analysis. Remote Sens. 10, 1757 (2018).
ADS Article Google Scholar
36.
Funk, C. C. et al. CHIRPS-2.0. A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p. http://pubs.usgs.gov/ds/832/. Accessed on 25 March 2020. (2014).
37.
Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).
Article Google Scholar
38.
Copernicus Climate Change Service. C3S ERA5-Land reanalysis . Copernicus Climate Change Service, https://cds.climate.copernicus.eu/cdsapp#!/home. Accessed on 25 March 2020. (2019).
39.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A. & Ziese, M. GPCC Monitoring Product Version 6: Near Real-Time Monthly Land-Surface Precipitation from Rain-Gauges based on SYNOP and CLIMAT data. 10.5676/DWD_GPCC/MP_M_V6_100; ftp://ftp.dwd.de/pub/data/gpcc/monitoring_v6/. Accessed on 25 March 2020. (2018).
40.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J. & Jackson, T. GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC),https://doi.org/10.5067/GPM/IMERG/3B-MONTH/06. Accessed on 25 March 2020. (2019).
41.
Global Modeling and Assimilation Office. MERRA-2 tavgM_2d_flx_Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Surface Flux Diagnostics V5.12.4; https://doi.org/10.5067/0JRLVL8YV2Y4. Accessed on 25 March 2020. (Goddard Earth Sciences Data and Information Services Center (GES DISC), 2015).
42.
Unger-Shayesteh, K. et al. What do we know about past changes in the water cycle of Central Asian headwaters? A review. Glob. Planet. Change 110, 4–25 (2013).
ADS Article Google Scholar
43.
Amante, C. & Eakins, B. W. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24. National Geophysical Data Center, NOAA. https://doi.org/10.7289/V5C8276M, Accessed on 25 March 2020. (2009).
44.
Jpl, N. A. S. A. NASA shuttle radar topography mission global 1 arc second data set. NASA EOSDIS Land Process. DAAC. https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003 (2013).
45.
QGIS Development Team. GIS Geographic Information System. Version 3.12 București. Open Source Geospatial Foundation Project. http://qgis.osgeo.org/. (2020).
46.
Smallwood, P. D. & Shank, C. C. From buffer zone to national park: Afghanistan’s Wakhan National Park. In Collateral Values Vol. 25 (eds Lookingbill, T. R. & Smallwood, P. D.) 213–233 (Springer, Berlin, 2019).
Google Scholar
47.
Vanselow, K. A. The high-mountain pastures of the Eastern Pamirs (Tajikistan): an evaluation of the ecological basis and the pasture potential. (Erlangen, Nürnberg, Univ., Diss., 2011).
48.
Breckle, S. W. & Rafiqpoor, M. D. Field Guide Afghanistan—Flora and Vegetation. (Scientia Bonnensis, 2010).
49.
Moheb, Z. & Bradfield, D. Status of the common leopard in Afghanistan. ISSN 1027–2992. Cat News 61, (2014).
50.
Mohibbi, A. A. & Cochard, R. Residents’ resource uses and nature conservation in Band-e-Amir National Park, Afghanistan. Environ. Dev. 11, 141–161 (2014).
Article Google Scholar
51.
Moqanaki, E. M. et al. Distribution and status of the Pallas’s cat in the south-west part of its range. ISSN 1027–2992. Cat News Special Issue 13, (2019).
52.
Gray, T. I. & Tapley, B. D. Vegetation health: Nature’s climate monitor. Adv. Space Res. 5, 371–377 (1985).
ADS Article Google Scholar
53.
Sun, J. & Qin, X. Precipitation and temperature regulate the seasonal changes of NDVI across the Tibetan Plateau. Environ. Earth Sci. 75, 291 (2016).
Article Google Scholar
54.
Anyamba, A. & Tucker, C. J. Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. J. Arid Environ. 63, 596–614 (2005).
ADS Article Google Scholar
55.
Quetin, G. R. & Swann, A. L. S. Empirically derived sensitivity of vegetation to climate across global gradients of temperature and precipitation. J. Clim. 30, 5835–5849 (2017).
ADS Article Google Scholar
56
Meroni, M., Fasbender, D., Rembold, F., Atzberger, C. & Klisch, A. Near real-time vegetation anomaly detection with MODIS NDVI: timeliness vs. accuracy and effect of anomaly computation options. Remote Sens. Environ. 221, 508–521 (2019).
ADS PubMed PubMed Central Article Google Scholar
57.
Rita, A. et al. The impact of drought spells on forests depends on site conditions: the case of 2017 summer heat wave in southern Europe. Glob. Change Biol. 26, 851–863 (2020).
ADS Article Google Scholar
58.
Kandasamy, S., Baret, F., Verger, A., Neveux, P. & Weiss, M. A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products. Biogeosciences 10, 4055–4071 (2013).
ADS Article Google Scholar
59.
Liu, R., Shang, R., Liu, Y. & Lu, X. Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability. Remote Sens. Environ. 189, 164–179 (2017).
ADS Article Google Scholar
60.
Zandler, H., Brenning, A. & Samimi, C. Quantifying dwarf shrub biomass in an arid environment: comparing empirical methods in a high dimensional setting. Remote Sens. Environ. 158, 140–155 (2015).
ADS Article Google Scholar
61.
Hyndman, R. J. Discussion of ‘High-dimensional autocovariance matrices and optimal linear prediction’. Electron. J. Stat. 9, 792–796 (2015).
MathSciNet MATH Article Google Scholar
62.
Propastin, P. A., Kappas, M. & Muratova, N. R. Inter-annual changes in vegetation activities and their relationship to temperature and precipitation in Central Asia from 1982 to 2003. J. Environ. Inf. 12, 75–87 (2008).
Article Google Scholar
63
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
ADS Article Google Scholar
64.
Parker, W. S. Reanalyses and observations: what’s the difference?. Bull. Am. Meteorol. Soc. 97, 1565–1572 (2016).
ADS Article Google Scholar
65.
El Kenawy, A. M. & McCabe, M. F. A multi-decadal assessment of the performance of gauge- and model-based rainfall products over Saudi Arabia: climatology, anomalies and trends: RAINFALL PRODUCTS IN SAUDI ARABIA. Int. J. Climatol. 36, 656–674 (2016).
Article Google Scholar
66.
Song, S. & Bai, J. Increasing winter precipitation over arid Central Asia under global warming. Atmosphere 7, 139 (2016).
ADS Article Google Scholar
67.
Ahmed, K., Shahid, S., Wang, X., Nawaz, N. & Najeebullah, K. Evaluation of gridded precipitation datasets over arid regions of Pakistan. Water 11, 210 (2019).
Article Google Scholar
68.
Anjum, M. N. et al. Performance evaluation of latest integrated multi-satellite retrievals for Global Precipitation Measurement (IMERG) over the northern highlands of Pakistan. Atmos. Res. 205, 134–146 (2018).
Article Google Scholar
69.
Gelaro, R. et al. The modern-era retrospective analysis for research and applications, Version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).
ADS PubMed Article PubMed Central Google Scholar
70.
Reichle, R. H. et al. Land surface precipitation in MERRA-2. J. Clim. 30, 1643–1664 (2017).
ADS Article Google Scholar
71.
Peng, S., Piao, S., Ciais, P., Fang, J. & Wang, X. Change in winter snow depth and its impacts on vegetation in China. Glob. Change Biol. https://doi.org/10.1111/j.1365-2486.2010.02210.x (2010).
Article Google Scholar
72.
Qiu, B. et al. Satellite-observed solar-induced chlorophyll fluorescence reveals higher sensitivity of alpine ecosystems to snow cover on the Tibetan Plateau. Agric. For. Meteorol. 271, 126–134 (2019).
ADS Article Google Scholar
73.
Hall, D. K., Riggs, G. A., DiGirolamo, N. E. & Román, M. O. Evaluation of MODIS and VIIRS cloud-gap-filled snow-cover products for production of an Earth science data record. Hydrol. Earth Syst. Sci. 23, 5227–5241 (2019).
ADS Article Google Scholar
74.
Salomonson, V. V. & Appel, I. Development of the Aqua MODIS NDSI fractional snow cover algorithm and validation results. IEEE Trans. Geosci. Remote Sens. 44, 1747–1756 (2006).
ADS Article Google Scholar
75.
Riggs, G., Hall, D. & Román, M. O. VIIRS Snow Cover Algorithm Theoretical Basis Document (ATBD). 38 (2015).
76
Zhu, A.-X. Resampling Raster. In International Encyclopedia of Geography: People, the Earth, Environment and Technology (eds Richardson, D. et al.) 1–5 (Wiley, New York, 2017). https://doi.org/10.1002/9781118786352.wbieg0878.
Google Scholar
77.
Behnke, R. et al. Evaluation of downscaled, gridded climate data for the conterminous United States. Ecol. Appl. 26, 1338–1351 (2016).
CAS PubMed Article Google Scholar
78.
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
MATH Article Google Scholar
79.
Zandler, H. Wakhan Rangeland Assessment Report 2018. Unpublished report. (2018).
80.
Camberlin, P., Martiny, N., Philippon, N. & Richard, Y. Determinants of the interannual relationships between remote sensed photosynthetic activity and rainfall in tropical Africa. Remote Sens. Environ. 106, 199–216 (2007).
ADS Article Google Scholar
81.
Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl. Acad. Sci. 110, 52–57 (2013).
ADS CAS PubMed Article PubMed Central Google Scholar
82.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
MathSciNet MATH Google Scholar
83.
Peña, M. A., Brenning, A. & Sagredo, A. Constructing satellite-derived hyperspectral indices sensitive to canopy structure variables of a Cordilleran Cypress (Austrocedrus chilensis) forest. ISPRS J. Photogram. Remote Sens. 74, 1–10 (2012).
Article Google Scholar
84.
Zandler, H., Brenning, A. & Samimi, C. Potential of space-borne hyperspectral data for biomass quantification in an arid environment: advantages and limitations. Remote Sens. 7, 4565–4580 (2015).
ADS Article Google Scholar
85
Efron, B. & Tibshirani, R. An Introduction to the Bootstrap (Chapman & Hall, London, 1993).
Google Scholar
86.
Banik, S. & Kibria, B. M. Confidence intervals for the population correlation coefficient ρ. Int. J. Stats. Med. Res. 5, 99–111 (2016).
Article Google Scholar
87.
Mudelsee, M. Estimating Pearson’s correlation coefficient with bootstrap confidence interval from serially dependent time series. Math. Geol. 35, 651–665 (2003).
MATH Article Google Scholar
88.
Abdi, A. M. et al. The El Niño – La Niña cycle and recent trends in supply and demand of net primary productivity in African drylands. Clim. Change 138, 111–125 (2016).
ADS Article Google Scholar
89.
Lima, E., Davies, P., Kaler, J., Lovatt, F. & Green, M. Variable selection for inferential models with relatively high-dimensional data: Between method heterogeneity and covariate stability as adjuncts to robust selection. Sci. Rep. 10, 8002 (2020).
ADS CAS PubMed PubMed Central Article Google Scholar
90.
Degenhardt, F., Seifert, S. & Szymczak, S. Evaluation of variable selection methods for random forests and omics data sets. Brief. Bioinform. 20, 492–503 (2019).
PubMed Article PubMed Central Google Scholar
91
Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Soft. https://doi.org/10.18637/jss.v036.i11 (2010).
Article Google Scholar
92.
Diesing, M. Deep-sea sediments of the global ocean. https://essd.copernicus.org/preprints/essd-2020-22/ (2020) 10.5194/essd-2020-22.
93.
R Core Team. R: A Language and Environment for Statistical Computing. Version 4.0.3. https://www.R-project.org/. (R Foundation for Statistical Computing, 2020).
94.
Daham, A., Han, D., Rico-Ramirez, M. & Marsh, A. Analysis of NVDI variability in response to precipitation and air temperature in different regions of Iraq, using MODIS vegetation indices. Environ. Earth Sci. 77, 389 (2018).
Article Google Scholar
95.
Chen, S., Gan, T. Y., Tan, X., Shao, D. & Zhu, J. Assessment of CFSR, ERA-Interim, JRA-55, MERRA-2, NCEP-2 reanalysis data for drought analysis over China. Clim. Dyn. 53, 737–757 (2019).
Article Google Scholar
96
Kath, J. et al. Not so robust: robusta coffee production is highly sensitive to temperature. Glob. Change Biol. https://doi.org/10.1111/gcb.15097 (2020).
Article Google Scholar
97.
Mahto, S. S. & Mishra, V. Does ERA-5 outperform other reanalysis products for hydrologic applications in India?. J. Geophys. Res. Atmos. 124, 9423–9441 (2019).
ADS Article Google Scholar
98.
Royé, D., Íñiguez, C. & Tobías, A. Comparison of temperature–mortality associations using observed weather station and reanalysis data in 52 Spanish cities. Environ. Res. 183, 109237 (2020).
PubMed Article CAS PubMed Central Google Scholar
99.
Dee, D. P., Källén, E., Simmons, A. J. & Haimberger, L. Comments on “Reanalyses Suitable for Characterizing Long-Term Trends”. Bull. Am. Meteorol. Soc. 92, 65–70 (2011).
ADS Article Google Scholar
100.
Rasmussen, R. et al. How well are we measuring snow: the NOAA/FAA/NCAR winter precipitation test bed. Bull. Am. Meteorol. Soc. 93, 811–829 (2012).
ADS Article Google Scholar
101.
Yuan, X., Li, L. & Chen, X. Increased grass NDVI under contrasting trends of precipitation change over North China during 1982–2011. Remote Sens. Lett. 6, 69–77 (2015).
Article Google Scholar
102.
Wang, X., Ciais, P., Wang, Y. & Zhu, D. Divergent response of seasonally dry tropical vegetation to climatic variations in dry and wet seasons. Glob. Change Biol. 24, 4709–4717 (2018).
ADS Article Google Scholar
103
Basheer, M. & Elagib, N. A. Performance of satellite-based and GPCC 7.0 rainfall products in an extremely data-scarce country in the Nile Basin. Atmos. Res. 215, 128–140 (2019).
Article Google Scholar
104.
Piazzi, G. et al. Cross-country assessment of H-SAF snow products by sentinel-2 imagery validated against in-situ observations and webcam photography. Geosciences 9, 129 (2019).
ADS Article Google Scholar
105.
Lievens, H. et al. Snow depth variability in the Northern Hemisphere mountains observed from space. Nat. Commun. 10, 4629 (2019).
ADS PubMed PubMed Central Article CAS Google Scholar
106.
Sur, C., Park, S.-Y., Kim, T.-W. & Lee, J.-H. Remote sensing-based agricultural drought monitoring using hydrometeorological variables. KSCE J. Civ. Eng. 23, 5244–5256 (2019).
Article Google Scholar
107.
Geruo, A., Velicogna, I., Zhao, M., Colliander, A. & Kimball, J. S. Satellite detection of varying seasonal water supply restrictions on grassland productivity in the Missouri basin, USA. Remote Sens. Environ. 239, 111623 (2020).
ADS Article Google Scholar
108.
Lu, X. et al. Correcting GPM IMERG precipitation data over the Tianshan Mountains in China. J. Hydrol. 575, 1239–1252 (2019).
ADS Article Google Scholar
109.
Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).
PubMed PubMed Central Article Google Scholar
110.
Bai, L., Shi, C., Li, L., Yang, Y. & Wu, J. Accuracy of CHIRPS satellite-rainfall products over Mainland China. Remote Sens. 10, 362 (2018).
ADS Article Google Scholar
111.
Berg, A. A., Famiglietti, J. S., Walker, J. P. & Houser, P. R. Impact of bias correction to reanalysis products on simulations of North American soil moisture and hydrological fluxes. J. Geophys. Res. 108, ACL2-1-ACL2-5 (2003).
Google Scholar
112.
Sahoo, A. K., Sheffield, J., Pan, M. & Wood, E. F. Evaluation of the tropical rainfall measuring mission multi-satellite precipitation analysis (TMPA) for assessment of large-scale meteorological drought. Remote Sens. Environ. 159, 181–193 (2015).
ADS Article Google Scholar
113.
Zambrano, F., Wardlow, B., Tadesse, T., Lillo-Saavedra, M. & Lagos, O. Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile. Atmos. Res. 186, 26–42 (2017).
Article Google Scholar
114.
Dörre, A. Local knowledge-based water management and irrigation in the western pamirs. Int. J. EI 1, 254–266 (2018).
Article Google Scholar More