Land Surface Phenology and Seasonality Using Cool Earthlight in Croplands of Eastern Africa and the Linkages to Crop Production
Abstract
:1. Introduction
2. Study Region, Data and Methodology
2.1. Study Region
2.2. Data
2.2.1. Remote Sensing Data
2.2.2. Crop Production Data
2.3. Methods
3. Results
3.1. Land Surface Seasonalities of Precipitable Water Vapor
3.2. Land Surface Seasonality and Phenology of AMSR Land Parameters & TRMM Rainfall
3.3. Land Surface Seasonality of Soil Moisture
3.4. Land Surface Seasonality of Actual Evapotranspiration
3.5. El Niño/Southern Oscillation and the Indian Ocean Dipole
3.6. Land Surface Phenology Linkages to Crop Production Statistics
4. Discussion
4.1. Seasonal Peak and Moisture Time to Peak (MTP) for Land Surface Phenologies and Seasonalities
4.2. AMSR Variables Response to El Niña Southern Oscillation and Indian Ocean Dipole Modes
4.3. Crop Production Responses to Biophysical Factors
4.4. Seasonal Kendall Trend Test on Long-Term Rainfall Data
5. Conclusions and Recommendations
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
Legend Label | Year | Description |
---|---|---|
Ethiopia (Figure 16a) | ||
1 | 1974 | The Derg regime came to power. In 1975 the “Land to the Tiller” policy was declared, which granted to tenant farmers ownership on the lands they cultivated, with the objective of increasing cultivated area [94]. At the same time, there was huge national afforestation program, which may have reduced the area of marginal croplands. |
2 | 1991 | Tigray People Liberation Front (TPLF)-led Ethiopian People Revolutionary Democratic Front (EPRDF) came to power. |
3 | 1994 | The “Agriculture-Led Industrialization” (ADLI) policy was declared, which aimed at giving more resources to farmers and farm activity compared to urban dwellers and industrial and tertiary activities [97]. This change may have triggered expansion of cultivated lands on formerly uncultivated or preserved lands. Many of the lands forested during the Derg regime were abandoned and left to local farmers. |
4 | 1995 | The new constitution of 1995 approved and confirmed the state ownership of all land in Ethiopia, reversing the 1975 policy. As the land was owned by government, farmers had only use rights. This policy brought tenure insecurity to farmers and triggered a “tragedy of the commons” mentality: tenant farmers were reluctant to manage well the lands they cultivated or to invest in long-term agricultural production activity projects [97]. |
5 | 2010 | “Growth and Transformation Plan” was a medium-term strategic framework for the five-year period from 2010/11 to 2014/15. During this time the “Arab Spring” revolt occurred, and some observers have commented that this plan aimed to divert attention of the Ethiopian population [98]. Execution of this plan was poor. For example, among the ten mega-sugar factories planned, not one was completed by the end date [98]. |
Tanzania (Figure 16b) | ||
B | 1985 | Nyerere handed power over to Ali Hassan Mwinyi [99]. |
C | 1995 | Benjamin William Mkapa was sworn in as the new president of Tanzania in the first multi-party election [99]. |
D | 2005 | Jakaya Mrisho Kikwete was elected fourth president for a five-year term. |
E | 2015 | John Magufuli elected as fifth president of Tanzania [99]. |
F | 1967/68 | Arusha declaration; Ujamaa and Villagization. Nyerere introduced African socialism, or Ujamaa, literal meaning “family-hood” [100]. |
G | 1980 | Economic Liberalization and the National Agricultural Policy [100]. |
H | 1990 | Investment Promotion and the Transition to Multipartyism [100]. |
I | 1995 | National land policy enacted [101] |
J | 1997 | National land policy amended [101]. |
K | 2001 | Land Act no. 4 and village land act no. 5 enacted in 1999 become operational [101]. |
L | 2004 | Land Act no. 4 and village land act no. 5 enacted in 1999 amended [101]. |
M | 2007 | Land use plan act enacted [101]. |
Appendix B
Appendix B.1. Seasonal Kendall Test for Rainfall in Ethiopia
Two-Sided Homogeneity Test | |||||
H0: S = 0 (no trend) | |||||
HA: S != 0 (monotonic trend) | |||||
Statistics for individual seasons | |||||
S | Var S | Z | tau | p-value | |
1 | 56 | 20020 | 0.4 | 0.036 | 0.692 |
2 | − | 20020 | −2.0 | −0.183 | 0.046 |
3 | −102 | 20020 | −0.7 | −0.066 | 0.471 |
4 | −78 | 20020 | −0.6 | −0.051 | 0.581 |
5 | 6 | 20020 | 0.0 | 0.004 | 0.966 |
6 | −208 | 20020 | −1.5 | −0.135 | 0.142 |
7 | −366 | 20020 | −2.6 | −0.238 | 0.010 |
8 | −166 | 20020 | −1.2 | −0.108 | 0.241 |
9 | −120 | 20020 | −0.8 | −0.078 | 0.396 |
10 | −6 | 20020 | 0.0 | −0.004 | 0.966 |
11 | −88 | 20020 | −0.6 | −0.057 | 0.534 |
12 | 28 | 20020 | 0.2 | 0.018 | 0.843 |
Statistics for total series | |||||
S | Var S | Z | tau | p-value | |
1 | −1326 | 240240 | −2.7 | −0.072 | 0.007 |
Seasonal Sen’s slope and intercept | |||||
slope: −0.0925 mm/month | |||||
intercept: 104.3255 | |||||
number of observations: 672 |
Appendix B.2. Seasonal Kendall Test for Rainfall in Tanzania
Two-Sided Homogeneity Test | |||||
H0: S = 0 (no trend) | |||||
HA: S != 0 (monotonic trend) | |||||
Statistics for individual seasons | |||||
S | Var S | Z | tau | p-value | |
1 | 18 | 20020 | 0.1 | 0.012 | 0.899 |
2 | −214 | 20020 | −1.5 | −0.139 | 0.130 |
3 | −108 | 20020 | −0.8 | −0.070 | 0.445 |
4 | −266 | 20020 | −1.9 | −0.173 | 0.060 |
5 | −168 | 20020 | −1.2 | −0.109 | 0.235 |
6 | 0 | 20020 | 0.0 | 0.000 | 1.000 |
7 | −226 | 20020 | −1.6 | −0.147 | 0.110 |
8 | 76 | 20020 | 0.5 | 0.049 | 0.591 |
9 | 80 | 20020 | 0.6 | 0.052 | 0.572 |
10 | 112 | 20020 | 0.8 | 0.073 | 0.429 |
11 | −48 | 20020 | −0.3 | −0.031 | 0.734 |
12 | −66 | 20020 | −0.5 | −0.043 | 0.641 |
Statistics for total series | |||||
S | Var S | Z | tau | p-value | |
1 | −810 | 240240 | −1.7 | −0.044 | 0.098 |
Seasonal Sen’s slope and intercept | |||||
slope: −0.0362 mm/month | |||||
intercept: 81.0414 | |||||
number of observations: 672 |
References
- FAO. FAOSTAT. Available online: http://faostat.fao.org/site/567/default.aspx#ancor (accessed on 24 August 2012).
- Becker-Reshef, I.; Justice, C.; Sullivan, M.; Vermote, E.; Tucker, C.; Anyamba, A.; Small, J.; Pak, E.; Masuoka, E.; Schmaltz, J.; et al. Monitoring global croplands with coarse resolution earth observations: The Global Agriculture Monitoring (GLAM) project. Remote Sens. 2010, 2, 1589–1609. [Google Scholar] [CrossRef]
- Brown, M.E.; de Beurs, K.; Vrieling, A. The response of African land surface phenology to large scale climate oscillations. Remote Sens. Environ. 2010, 114, 2286–2296. [Google Scholar] [CrossRef]
- Adhikari, U.; Nejadhashemi, A.P.; Woznicki, S.A. Climate change and Eastern Africa: A review of impact on major crops. Food Energy Secur. 2015, 4, 110–132. [Google Scholar] [CrossRef]
- Funk, C.; Dettinger, M.D.; Michaelsen, J.C.; Verdin, J.P.; Brown, M.E.; Barlow, M.; Hoell, A. Warming of the Indian Ocean threatens eastern and southern African food security but could be mitigated by agricultural development. Proc. Natl. Acad. Sci. USA 2008, 105, 11081–11086. [Google Scholar] [CrossRef] [PubMed]
- Korotayev, A.; Zinkina, J. East Africa in the Malthusian trap? J. Dev. Soc. 2015, 31, 385–420. [Google Scholar] [CrossRef]
- Barton, A. Deforestation in Ethiopia. Available online: http://www.czp.cuni.cz/knihovna/SD_Case_Studies.pdf#page=227 (accessed on 30 June 2017).
- Dixon, J.; Gulliver, A.; Gibbon, D. Farming Systems and Poverty: Improving Farmers’ Livelihoods in a Changing World; FAO and World Bank: Rome, Italy, 2001; p. 407. [Google Scholar]
- Wang, G.; Schimel, D. Climate change, climate modes, and climate impacts. Annu. Rev. Environ. Resour. 2003, 28, 1–28. [Google Scholar] [CrossRef]
- Behera, S.K.; Luo, J.J.; Masson, S.; Delecluse, P.; Gualdi, S.; Navarra, A.; Yamagata, T. Paramount impact of the Indian Ocean dipole on the east African short rains: A CGCM study. J. Clim. 2005, 18, 4514–4530. [Google Scholar] [CrossRef]
- Funk, C.; Hoell, A.; Shukla, S.; Bladé, I.; Liebmann, B.; Roberts, J.B.; Robertson, F.R.; Husak, G. Predicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices. Hydrol. Earth Syst. Sci. 2014, 18, 4965–4978. [Google Scholar] [CrossRef]
- Black, E. The relationship between Indian Ocean sea–surface temperature and East African rainfall. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2005, 363, 43–47. [Google Scholar] [CrossRef] [PubMed]
- Owiti, Z.; Ogallo, L.A.; Mutemi, J. Linkages between the Indian Ocean dipole and east African seasonal rainfall anomalies. J. Kenya Meteorol. Soc. 2008, 2, 3–17. [Google Scholar]
- Nicholson, S.E. Long-term variability of the east African ‘short rains’ and its links to large-scale factors. Int. J. Climatol. 2015, 35, 3979–3990. [Google Scholar] [CrossRef]
- Williams, C.A.; Hanan, N.P. ENSO and IOD teleconnections for African ecosystems: Evidence of destructive interference between climate oscillations. Biogeosciences. 2011, 8, 27–41. [Google Scholar] [CrossRef] [Green Version]
- Marchant, R.; Mumbi, C.; Behera, S.; Yamagata, T. The Indian Ocean dipole—The unsung driver of climatic variability in east Africa. Afr. J. Ecol. 2007, 45, 4–16. [Google Scholar] [CrossRef]
- Hoell, A.; Funk, C. Indo-Pacific sea surface temperature influences on failed consecutive rainy seasons over eastern Africa. Clim. Dyn. 2014, 43, 1645–1660. [Google Scholar] [CrossRef]
- Hoell, A.; Funk, C.; Zinke, J.; Harrison, L. Modulation of the southern Africa precipitation response to the El Niño Southern Oscillation by the subtropical Indian Ocean dipole. Clim. Dyn. 2016, 48, 2529–2540. [Google Scholar] [CrossRef]
- Meyers, G.; McIntosh, P.; Pigot, L.; Pook, M. The years of El Niño, La Niña, and interactions with the tropical Indian Ocean. J. Clim. 2007, 20, 2872–2880. [Google Scholar] [CrossRef]
- Schreck, C.J.; Semazzi, F.H.M. Variability of the recent climate of eastern Africa. Int. J. Climatol. 2004, 24, 681–701. [Google Scholar] [CrossRef]
- Nicholson, S.E.; Kim, J. The relationship of the El Niño–Southern Oscillation to African rainfall. Int. J. Climatol. 1997, 17, 117–135. [Google Scholar] [CrossRef]
- Souverijns, N.; Thiery, W.; Demuzere, M.; Lipzig, N.P.M.V. Drivers of future changes in east African precipitation. Environ. Res. Lett. 2016, 11, 114011. [Google Scholar] [CrossRef]
- Saji, N.H.; Goswami, B.N.; Vinayachandran, P.N.; Yamagata, T. A dipole mode in the tropical Indian Ocean. Nature 1999, 401, 360–363. [Google Scholar] [CrossRef] [PubMed]
- Webster, P.J.; Moore, A.M.; Loschnigg, J.P.; Leben, R.R. Coupled ocean-atmosphere dynamics in the Indian Ocean during 1997–98. Nature 1999, 401, 356–360. [Google Scholar] [CrossRef] [PubMed]
- Pervez, M.S.; Henebry, G.M. Spatial and seasonal responses of precipitation in the Ganges and Brahmaputra river basins to ENSO and Indian Ocean Dipole modes: Implications for flooding and drought. Nat. Hazard. Earth Sys. 2015, 15, 147–162. [Google Scholar] [CrossRef]
- Lyon, B.; DeWitt, D.G. A recent and abrupt decline in the east African long rains. Geophys. Res. Lett. 2012, 39, L02702. [Google Scholar] [CrossRef]
- Brown, M.E.; de Beurs, K.M. Evaluation of multi-sensor semi-arid crop season parameters based on NDVI and rainfall. Remote Sens. Environ. 2008, 112, 2261–2271. [Google Scholar] [CrossRef]
- Rientjes, T.; Haile, A.T.; Fenta, A.A. Diurnal rainfall variability over the Upper Blue Nile basin: A remote sensing based approach. Int. J. Appl. Earth Obs. Geoinf. 2013, 21, 311–325. [Google Scholar] [CrossRef]
- Mathan, K.K. Influence of accumulated heat units and sunshine hours on the growth and yield of sorghum (var. Co 25). J. Agron. Crop. Sci. 1989, 163, 196–200. [Google Scholar] [CrossRef]
- Palmer, A.H. The agricultural significance of sunshine as illustrated in California. Mon. Weather Rev. 1920, 48, 151–154. [Google Scholar] [CrossRef]
- De Beurs, K.M.; Henebry, G.M. Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan. Remote Sens. Environ. 2004, 89, 497–509. [Google Scholar] [CrossRef]
- De Beurs, K.M.; Henebry, G.M. Spatio-temporal statistical methods for modelling land surface phenology. In Phenological Research: Methods for Environmental and Climate Change Analysis, 2nd ed.; Hudson, I.L., Keatley, M.R., Eds.; Springer: Dordrecht, The Netherlands, 2010; pp. 177–208. [Google Scholar]
- De Beurs, K.M.; Henebry, G.M. Northern annular mode effects on the land surface phenologies of Northern Eurasia. J. Clim. 2008, 21, 4257–4279. [Google Scholar] [CrossRef]
- Los, S.O.; Collatz, G.J.; Bounoua, L.; Sellers, P.J.; Tucker, C.J. Global interannual variations in sea surface temperature and land surface vegetation, air temperature, and precipitation. J. Clim. 2001, 14, 1535–1549. [Google Scholar] [CrossRef]
- Pitt, M.D.; Heady, H.F. Responses of annual vegetation to temperature and rainfall patterns in northern California. Ecology 1978, 59, 336–350. [Google Scholar] [CrossRef]
- Morisette, J.T.; Richardson, A.D.; Knapp, A.K.; Fisher, J.I.; Graham, E.A.; Abatzoglou, J.; Wilson, B.E.; Breshears, D.D.; Henebry, G.M.; Hanes, J.M.; et al. Tracking the rhythm of the seasons in the face of global change: Phenological research in the 21st century. Front. Ecol. Environ. 2009, 7, 253–260. [Google Scholar] [CrossRef]
- Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127–150. [Google Scholar] [CrossRef]
- Benedetti, R.; Rossini, P. On the use of NDVI profiles as a tool for agricultural statistics—The case-study of wheat yield estimate and forecast in Emilia-Romagna. Remote Sens. Environ. 1993, 45, 311–326. [Google Scholar] [CrossRef]
- Funk, C.; Budde, M.E. Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe. Remote Sens. Environ. 2009, 113, 115–125. [Google Scholar] [CrossRef]
- Maselli, F.; Conese, C.; Petkov, L.; Gilabert, M.A. Use of NOAA-AVHRR NDVI data for environmental monitoring and crop forecasting in the Sahel—Preliminary-results. Int. J. Remote Sens. 1992, 13, 2743–2749. [Google Scholar] [CrossRef]
- Rasmussen, M.S. Assessment of millet yields and production in northern Burkina-Faso using integrated NDVI from the AVHRR. Int. J. Remote Sens. 1992, 13, 3431–3442. [Google Scholar] [CrossRef]
- Mkhabela, M.S.; Bullock, P.; Raj, S.; Wang, S.; Yang, Y. Crop yield forecasting on the canadian prairies using MODIS NDVI data. Agric. For. Meteorol. 2011, 151, 385–393. [Google Scholar] [CrossRef]
- FEWS-NET. USGS FEWS NET Data Portal. Available online: http://earlywarning.usgs.gov/fews (accessed on 15 February 2017).
- FAO. GIEWS-Global Information and Early Warning System. Available online: http://www.fao.org/giews/en/ (accessed on 15 March 2017).
- Alemu, W.G.; Henebry, G.M. Comparing passive microwave with visible-to-near-infrared phenometrics in croplands of northern Eurasia. Remote Sens. 2017, 9, 613. [Google Scholar] [CrossRef]
- Alemu, W.G.; Henebry, G.M. Characterizing cropland phenology in major grain production areas of Russia, Ukraine, and Kazakhstan by the synergistic use of passive microwave and visible to near infrared data. Remote Sens. 2016, 8, 1016. [Google Scholar] [CrossRef]
- Alemu, W.G.; Henebry, G.M. Land surface phenologies and seasonalities using cool earthlight in mid-latitude croplands. Environ. Res. Lett. 2013, 8, 045002. [Google Scholar] [CrossRef]
- Ganguly, S.; Friedl, M.A.; Tan, B.; Zhang, X.; Verma, M. Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product. Remote Sens. Environ. 2010, 114, 1805–1816. [Google Scholar] [CrossRef]
- Sakamoto, T.; Van Nguyen, N.; Ohno, H.; Ishitsuka, N.; Yokozawa, M. Spatio–temporal distribution of rice phenology and cropping systems in the Mekong delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sens. Environ. 2006, 100, 1–16. [Google Scholar] [CrossRef]
- Zhang, X.Y.; Friedl, M.A.; Schaaf, C.B.; Strahler, A.H. Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Glob. Chang. Biol. 2004, 10, 1133–1145. [Google Scholar] [CrossRef]
- Henebry, G.M. Phenologies of North American grasslands and grasses. In Phenology: An Integrative Environmental Science; Schwartz, M.D., Ed.; Springer: Berlin, Germany, 2013; pp. 197–210. [Google Scholar]
- Henebry, G.M.; de Beurs, K.M. Remote sensing of land surface phenology: A prospectus. In Phenology: An Integrative Environmental Science; Schwartz, M.D., Ed.; Springer: Berlin, Germany, 2013; pp. 385–411. [Google Scholar]
- Du, J.; Kimball, J.S.; Shi, J.; Jones, L.A.; Wu, S.; Sun, R.; Yang, H. Inter-calibration of satellite passive microwave land observations from AMSR-E and AMSR2 using overlapping FY3B-MWRI sensor measurements. Remote Sens. 2014, 6, 8594–8616. [Google Scholar] [CrossRef]
- Senay, B.G.; Budde, M.; Verdin, P.J.; Melesse, M.A. A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields. Sensors 2007, 7, 979–1000. [Google Scholar] [CrossRef]
- Senay, G.B.; Bohms, S.; Singh, R.K.; Gowda, P.H.; Velpuri, N.M.; Alemu, H.; Verdin, J.P. Operational evapotranspiration mapping using remote sensing and weather datasets: A new parameterization for the SSEB approach. J. Am. Water Resour. Assoc. 2013, 49, 577–591. [Google Scholar] [CrossRef]
- Henebry, G.M.; de Beurs, K.M.; Wright, C.K.; Ranjeet, J.; Lioubimtseva, E. Dryland east Asia in hemispheric context. In Dryland East Asia: Land Dynamics amid Social and Climate Change; Chen, J., Wan, S., Henebry, G., Qi, J., Gutman, G., Sun, G., Kappas, M., Eds.; Higher Education Press and Walter de Gruyter GmbH: Berlin, Germany, 2013; pp. 23–43. [Google Scholar]
- USGS. 30 Meter Global Land Cover. Available online: https://landcover.usgs.gov/glc/ (accessed on 29 March 2016).
- Jones, L.A.; Kimball, J.S. Daily Global Land Surface Parameters Derived from AMSR-E. Boulder Colorado USA: National Snow and Ice Data Center. Available online: http://nsidc.org/data/nsidc-0451 (accessed on 30 June 2017).
- Bolten, J.D.; Crow, W.T. Improved prediction of quasi-global vegetation conditions using remotely-sensed surface soil moisture. Geophys. Res. Lett. 2012, 39, L19406. [Google Scholar] [CrossRef]
- Bolten, J.D.; Crow, W.T.; Xiwu, Z.; Jackson, T.J.; Reynolds, C.A. Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2010, 3, 57–66. [Google Scholar] [CrossRef]
- DAAC-LP. MODIS Products Table. Land Processes Distributed Active Archive Center. Available online: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table (accessed on 24 December 2014).
- TRMM. TRMM/TMPA 3B42 TRMM and Others Rainfall Estimate Data v7. Product Summary. Available online: https://mirador.gsfc.nasa.gov/collections/TRMM_3B42__007.shtml (accessed on 15 August 2015).
- TRMM. TRMM/TMPA 3B42 TRMM and Others Rainfall Estimate Data v7. Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC), 2011. Available online: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table (accessed on 15 August 2015).
- Schneider, U.; Becker, A.; Finger, P.; Meyer-Christoffer, A.; Rudolf, B.; Ziese, M. GPCC Full Data Reanalysis Version 6.0 at 1.0°: Monthly Land-Surface Precipitation from Rain-Gauges Built on GTS-Based and Historic Data. Available online: https://www.esrl.noaa.gov/psd/data/gridded/data.gpcc.html (accessed on 14 March 2017).
- NOAA. Monthly Atmospheric and Sea Surface Temperature Indices. National Weather Service Climatic Prediction Center, 2015. Available online: http://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices (accessed on 17 June 2015).
- JAMSTEC. Indian Ocean Dipole, 2012 ed.; JAMSTEC: Yokosuka, Japan, 2016; Available online: http://www.jamstec.go.jp/frcgc/research/d1/iod/DATA/dmi.monthly.txt (accessed on 15 August 2015).
- CSA. Agricultural Sample Survey, Time Series Data for National & Regional Level. Available online: http://www.csa.gov.et/images/general/news/agss_time_series%20report (accessed on 24 September 2015).
- CSA. Ethiopian National Data Archive (ENADA). Available online: http://213.55.92.105/enada/index.php/catalog/ (accessed on 24 September 2015).
- HarvestChoice. Agricultural Sample Survey. Available online: https://harvestchoice.org/topics/surveycensus-archive (accessed on 24 September 2015).
- HarvestChoice. National Sample Census of Agriculture 2007/2008 Small Holder Agriculture: Regional Report. Available online: https://harvestchoice.org/search/apachesolr_search/%5BTanzania%5D%20National%20Sample%20Census%20of%20Agriculture%202007/2008%3A%20Regional%20Report (accessed on 24 September 2015).
- McMaster, G.S.; Wilhelm, W.W. Growing degree-days: One equation, two interpretations. Agric. For. Meteorol. 1997, 87, 291–300. [Google Scholar] [CrossRef]
- Sarma, A.; Kumar, T.V.L.; Koteswararao, K. Development of an agroclimatic model for the estimation of rice yield. J. Indian Geophys. Union 2008, 12, 89–96. [Google Scholar]
- NOAA. Description of Changes to Oceanic Niño Index (ONI). Available online: http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_change.shtml (accessed on 22 June 2016).
- Bolton, D.K.; Friedl, M.A. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric. For. Meteorol. 2013, 173, 74–84. [Google Scholar] [CrossRef]
- Dagg, M.; Woodhead, T.; Rijks, D.A. Evaporation in east Africa. Int. Assoc. Sci. Hydrol. Bull. 1970, 15, 61–67. [Google Scholar] [CrossRef]
- Woodhead, T. Empirical relations between cloud amount, insolation and sunshine duration in east Africa: II. East. Afr. Agric. For. J. 1967, 32, 474–477. [Google Scholar]
- Woodhead, T. Empirical relations between cloud amount, insolation and sunshine duration in east Africa: I. East. Afr. Agric. For. 1966, 32, 211–213. [Google Scholar]
- Gashaw, W.; Legesse, D. Flood hazard and risk assessment using GIS and remote sensing in Fogera Woreda, northwest Ethiopia. In Nile River Basin: Hydrology, Climate and Water Use; Melesse, A.M., Ed.; Springer: Dordrecht, The Netherlands, 2011; pp. 179–206. [Google Scholar]
- IFRC. Ethiopia: Floods. Emergency Appeal No. MDRET003. 2006. Available online: http://www.ifrc.org/docs/appeals/06/MDRET003rev.pdf (accessed on 6 September 2006).
- DPPA. Joint Government and Humanitarian Partners Flash Appeal for the 2006 Flood Disaster in Ethiopia. 2006; p. 25. Available online: http://www.dppc.gov.et/downloadable/reports/appeal/2006/Flood%20Appeal%20II%20MASTER%20Final.pdf (accessed on 15 June 2016).
- UN-OCHA. Ocha Annual Report 2006: Activities and Use of Extrabudgetary Funds. Part III, Coordination Activities in the Field, Ethiopia. 2006. Available online: http://www.unocha.org/annualreport/2006/html/part3_ethiopia.html (accessed on 15 June 2016).
- Jury, M.R. Meteorological scenario of Ethiopian floods in 2006–2007. Theor. Appl. Climatol. 2011, 104, 209–219. [Google Scholar] [CrossRef]
- IFRC. Ethiopia: Response to Seasonal Floods. Available online: http://www.ifrc.org/docs/appeals/10/MDRET009.pdf (accessed on 28 September 2010).
- UNCIEF. Eastern and Southern Africa: Ethiopia. Available online: https://www.ifpri.org/division/eastern-and-southern-africa-office-esao (accessed on 30 June 2017).
- IFPRI. Ethiopia’s 2015 Drought: No Reason for a Famine. Available online: http://www.ifpri.org/blog/ethiopias-2015-drought-no-reason-famine (accessed on 21 November 2016).
- UN-WFP. Drought in Ethiopia: 10 Million People in Need. Available online: https://www.wfp.org/stories/drought-ethiopia-10-million-people-need (accessed on 21 Novemebr 2016).
- GEOGLAM. Early Warning Crop Monitor Report. Available online: http://www.cropmonitor.org/pages/archive.php (accessed on 21 November 2016).
- UN-OCHA. El Niño in East Africa. Available online: http://www.unocha.org/el-nino-east-africa (accessed on 21 November 2016).
- FEWS-NET. FEWS NET El Niño Monitoring Resources. Available online: http://www.fews.net/fews-net-el-ni%C3%B1o-monitoring-resources (accessed on 21 November 2016).
- Jones, M.O.; Jones, L.A.; Kimball, J.S.; McDonald, K.C. Satellite passive microwave remote sensing for monitoring global land surface phenology. Remote Sens. Environ. 2011, 115, 1102–1114. [Google Scholar] [CrossRef]
- Devereux, S.; Sharp, K. Trends in poverty and destitution in Wollo, Ethiopia. J. Dev. Stud. 2006, 42, 592–610. [Google Scholar] [CrossRef]
- FAO. FAO Statistical Yearbook for Africa; FAO Regional Office for Africa: Accra, Ghana, 2014; Available online: http://www.fao.org/3/a-i3620e.pdf (accessed on 30 June 2017).
- Bewket, W. Rainfall variability and crop production in Ethiopia: Case study in the Amhara Region. In Proceedings of the 16th International Conference of Ethiopian Studies, Trondheim, Norway, 2–6 July 2007. [Google Scholar]
- Crewett, W.; Bogale, A.; Korf, B. Land tenure in Ethiopia. Continuity and Change. Available online: http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/24652 (accessed on 1 September 2008).
- Liu, Y.Y.; van Dijk, A.I.J.M.; McCabe, M.F.; Evans, J.P.; de Jeu, R.A.M. Global vegetation biomass change (1988–2008) and attribution to environmental and human drivers. Glob. Ecol. Biogeogr. 2013, 22, 692–705. [Google Scholar] [CrossRef]
- Pohlert, T. Non-Parametric Trend Tests and Change-Point Detection. Available online: https://cran.r-project.org/web/packages/trend/vignettes/trend.pdf (accessed on 30 July 2017).
- Gudeta, Z. How successful the Agricultural Development Led Industrialisation Strategy (ADLI) will be by leaving existing landholding system intact. Econ. Focus 2002, 4, 16–20. [Google Scholar]
- Abbink, J. A Decade of Ethiopia: Politics, Economy and Society 2004–2016; Brill Publisher: Leiden, The Netherlands, 2017. [Google Scholar]
- Wikipedia. History of Tanzania. Available online: https://en.wikipedia.org/wiki/History_of_Tanzania (accessed on 18 August 2017).
- Ministry of Lands and Human Settlements Development. National Land Policy, 2nd ed.; Ministry of Lands and Human Settlements Development: Dar es Salaam, Tanzania, 1997. Available online: https://www.tanzania.go.tz/egov_uploads/documents/nationallandpolicy_sw.pdf (accessed on 30 June 2017).
- Myenzi, Y. Trends and Issues in the Tanzania Land Tenure System and the Kilimo Kwanza Strategy. Available online: http://www.hakiardhi.org/index.php?option=com_docman&task=doc_download&gid=61&Itemid=81 (accessed on 23 February 2010).
IOD/ENSO | Negative IOD | Neutral | Positive IOD |
---|---|---|---|
El Niño | −/+ | 0/+ | +/+ |
Neutral | −/0 * | 0/0 | +/0 |
La Niña | −/− | 0/− | +/− |
IOD/ENSO | −ve IOD | Neutral | +ve IOD |
---|---|---|---|
El Niño | -- | -- | 2015, 2016 |
Neutral | -- | 2003–2005, 2008–2009, 2013–2014 | 2006, 2012 |
La Niña | -- | 2007, 2010 | 2011 |
MTP GDD | MTP V | MTP vsm | MTP fw | MTP Rf | MTP ETa | MTP VODdd | MTT GDD | MTP VOD | |
---|---|---|---|---|---|---|---|---|---|
MTP GDD | 11 | 12 | 15 | 16 | 18 | 20 | 20 | 24 | |
MTP V | 1 | 4 | 5 | 7 | 9 | 9 | 12 | ||
MTP vsm | 3 | 4 | 6 | 7 | 8 | 11 | |||
MTP fw | 1 | 3 | 4 | 5 | 8 | ||||
MTP Rf | 2 | 3 | 4 | 7 | |||||
MTP ETa | 2 | 2 | 5 | ||||||
MTP VODdd | 0 | 4 | |||||||
MTT GDD | 3 | ||||||||
MTP VOD |
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Alemu, W.G.; Henebry, G.M. Land Surface Phenology and Seasonality Using Cool Earthlight in Croplands of Eastern Africa and the Linkages to Crop Production. Remote Sens. 2017, 9, 914. https://doi.org/10.3390/rs9090914
Alemu WG, Henebry GM. Land Surface Phenology and Seasonality Using Cool Earthlight in Croplands of Eastern Africa and the Linkages to Crop Production. Remote Sensing. 2017; 9(9):914. https://doi.org/10.3390/rs9090914
Chicago/Turabian StyleAlemu, Woubet G., and Geoffrey M. Henebry. 2017. "Land Surface Phenology and Seasonality Using Cool Earthlight in Croplands of Eastern Africa and the Linkages to Crop Production" Remote Sensing 9, no. 9: 914. https://doi.org/10.3390/rs9090914