Skip to main content
SpringerLink
Log in

Integrating Remotely Sensed Soil Moisture in Assessing the Effects of Climate Change on Food Production: A Review of Applications in Crop Production in Africa

  • Chapter
  • First Online:
Handbook of Climate Change Across the Food Supply Chain

Part of the book series: Climate Change Management ((CCM))

Abstract

Remotely sensed soil moisture is crucial in enhancing our understanding of how climate change influences food production. Conventionally, the acquisition of soil moisture data has always been based on in-situ measurements, which are costly, labour-intensive, spatially restricted and time-consuming to acquire. These limitations justify why most resource-constrained developing countries have been paying increasing attention to remote sensing. Although remote sensing has established potentials to address these challenges, progress in the application of this technology to crop production in Africa has not been properly documented. This chapter attempts to bridge this gap by providing a comprehensive review of the progress that has been accomplished to date and the gaps that need to be filled in and, successes and opportunities that have to strengthened and exploited.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Ahmadalipour A, Moradkhani H, Yan H, Zarekarizi M (2017) Remote sensing of drought: vegetation, soil moisture, and data assimilation. In: Lakshmi V (ed) Springer Remote sensing/photogrammetry. Remote sensing of hydrological extremes. Springer, Cham, pp 121–149

    Google Scholar 

  • Ahmed A, Zhang Y, Nichols S (2011) Review and evaluation of remote sensing methods for soil-moisture estimation. SPIE Rev 2(1):28001

    Google Scholar 

  • Alexandridis TK, Cherif I, Kalogeropoulos C, Monachou S, Eskridge K, Silleos N (2013) Rapid error assessment for quantitative estimations from Landsat 7 gap-filled images. Remote Sens Lett 4(9):920–928

    Google Scholar 

  • Ali I, Greifeneder F, Stamenkovic J, Neumann M, Notarnicola C (2015) Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data. Remote Sens 7(12):16398–16421

    Google Scholar 

  • Babaeian E, Sadeghi M, Franz TE, Jones S, Tuller M (2018) Mapping soil moisture with the OPtical TRApezoid Model (OPTRAM) based on long-term MODIS observations. Remote Sens Environ 211:425–440

    Google Scholar 

  • Bao Y, Lin L, Wu S, Deng KA, Petropoulos GP (2018) Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model. Int J Appl Earth Obs Geoinf 72:76–85

    Google Scholar 

  • Barrett BW, Dwyer E, Whelan P (2009) Soil moisture retrieval from active spaceborne microwave observations: an evaluation of current techniques. Remote Sens 1(3):210–242

    Google Scholar 

  • Breiman L (2001) Mach Learn 45(1):5–32

    Google Scholar 

  • Brocca L, Ciabatta L, Massari C, Camici S, Tarpanelli A (2017) Soil moisture for hydrological applications: open questions and new opportunities. Water 9(2):140

    Google Scholar 

  • Brocca L, Hasenauer S, Lacava T, Melone F, Moramarco T, Wagner W, Dorigo W, Matgen P, Martínez-Fernández J, Llorens P, Latron J (2011) Soil moisture estimation through ASCAT and AMSR-E sensors: an intercomparison and validation study across Europe. Remote Sens Environ 115(12):3390–3408

    Google Scholar 

  • Chatterjee S, Dey N, Sen S (2020) Soil moisture quantity prediction using optimized neural supported model for sustainable agricultural applications. Sustain Comput Inf Syst 28:100279

    Google Scholar 

  • Dash J, Ogutu BO (2016) Recent advances in space-borne optical remote sensing systems for monitoring global terrestrial ecosystems. Progr Phys Geogr Earth Environ 40(2):322–351

    Google Scholar 

  • Escobar VM, Srinivasan M, Arias SD (2016) Improving NASA’s earth observation systems and data programs through the engagement of mission early adopters BT–earth science satellite applications: current and future prospects. In: Hossain F (ed). Springer, Cham, pp 223–267

    Google Scholar 

  • Etwire PM (2020) The impact of climate change on farming system selection in Ghana. Agric Syst 179:102773

    Google Scholar 

  • Fathololoumi S, Vaezi AR, Alavipanah SK, Ghorbani A, Biswas A (2020) Comparison of spectral and spatial-based approaches for mapping the local variation of soil moisture in a semi-arid mountainous area. Sci Total Environ 138319

    Google Scholar 

  • Fauchereau N, Trzaska S, Rouault M, Richard Y (2003) Rainfall variability and changes in southern Africa during the 20th century in the global warming context. Nat Hazards 29(2):139–154

    Google Scholar 

  • Filion R, Bernier M, Paniconi C, Chokmani K, Melis M, Soddu A, Talazac M, Lafortune FX (2016) Remote sensing for mapping soil moisture and drainage potential in semi-arid regions: applications to the Campidano plain of Sardinia, Italy. Sci Total Environ 543:862–876

    Google Scholar 

  • Finn MP, Lewis M, Bosch DD, Giraldo M, Yamamoto K, Sullivan DG, Kincaid R, Luna R, Allam GK, Kvien C, Williams MS (2011) Remote sensing of soil moisture using airborne hyperspectral data. Gisci Remote Sens 48(4):522–540

    Google Scholar 

  • Forkuor G, Hounkpatin OK L, Welp G, Thiel M (2017) High resolution mapping of soil properties using remote sensing variables in South-Western Burkina Faso: a comparison of machine learning and multiple linear regression models. PLOS ONE 12(1):e0170478

    Google Scholar 

  • Gill MK, Asefa T, Kemblowski MW, McKee M (2006) Soil moisture prediction using support vector machines. J Am Water Resour Assoc 42(4):1033–1046

    Google Scholar 

  • Gruber A, Scanlon T, van der Schalie R, Wagner V, Dorigo W (2019) Evolution of the ESA CCI Soil moisture climate data records and their underlying merging methodology. Earth Syst Sci Data 11(2):717–739

    Google Scholar 

  • Guo S, Bai X, Chen Y, Zhang S, Hou H, Zhu Q, Du P (2019) An improved approach for soil moisture estimation in gully fields of the Loess Plateau using Sentinel-1A radar images. Remote Sens 11(3):349

    Google Scholar 

  • Hertel TW, Baldos ULC (2016) Overview of global land use, food security and the environment. Global change and the challenges of sustainably feeding a growing planet. Springer International Publishing, Cham, pp 1–12

    Google Scholar 

  • Hollmann R, Merchant CJ, Saunders R, Downy C, Buchwitz M, Cazenave A, Chuvieco E, Defourny P, de Leeuw G, Forsberg R, Holzer-Popp T (2013) The ESA climate change initiative: satellite data records for essential climate variables. Bull Am Meteor Soc 94(10):1541–1552

    Google Scholar 

  • Huuskonen J, Oksanen T (2018) Soil sampling with drones and augmented reality in precision agriculture. Comput Electron Agric 154:25–35

    Google Scholar 

  • Im J, Jensen, JR (2008) Hyperspectral remote sensing of vegetation. Geography Compass 2(6):1943–1961

    Google Scholar 

  • Jiang H, Cotton WR (2004) Soil moisture estimation using an artificial neural network: a feasibility study. Can J Remote Sens 30(5):827–839

    Google Scholar 

  • Jung C, Lee Y, Cho Y, Kim S (2017) A study of spatial soil moisture estimation using a multiple linear regression model and MODIS land surface temperature data corrected by conditional merging. Remote Sens 9(8):870

    Google Scholar 

  • Jung HC, Getirana A, Arsenault KR, Kumar S, Maigary I (2019) Improving surface soil moisture estimates in West Africa through GRACE data assimilation. J Hydrol 575:192–201

    Google Scholar 

  • Karlsen SR, Tolvanen A, Kubin E, Poikolainen J, Høgda KA, Johansen B, Danks FS, Aspholm P, Wielgolaski FE, Makarova O (2008) MODIS-NDVI-based mapping of the length of the growing season in northern Fennoscandia. Int J Appl Earth Obs Geoinf 10(3):253–266

    Google Scholar 

  • Lakhankar T, Ghedira H, Temimi M, Azar AE, Khanbilvardi R (2009) Effect of land cover heterogeneity on soil moisture retrieval using active microwave remote sensing data. Remote Sens 1(2):80–91

    Google Scholar 

  • Lei F, Crow, WT, Kustas, WP, Dong J, Yang Y, Knipper KR, Anderson MC, Gao F, Notarnicola C, Greifeneder F, McKee LM (2020) Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard. Remote Sens Environ 239:111622

    Google Scholar 

  • Liang S, Wang J (2020) A systematic view of remote sensing. In: Advanced remote sensing: terrestrial information extraction and applications, 2nd ed. Academic Press, pp 1–57. https://doi.org/10.1016/B978-0-12-815826-5.00001-5

  • Liu YY, Parinussa RM, Dorigo WA, De Jeu RA, Wagner W, Van Dijk A, McCabe MF, Evans J (2011) Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrol Earth Syst Sci 15(2):425–436

    Google Scholar 

  • Lobell DB, Azzari G, Marshall B, Gourlay S, Jin Z, Kilic T, Murray S (2020) Eyes in the sky, boots on the ground: assessing satellite- and ground-based approaches to crop yield measurement and analysis. Am J Agr Econ 102(1):202–219

    Google Scholar 

  • McColl KA, Alemohammad SH, Akbar R, Konings AG, Yueh S, Entekhabi D (2017) The global distribution and dynamics of surface soil moisture. Nat Geosci 10:100

    CAS  Google Scholar 

  • McNally A, Shukla S, Arsenault KR, Wang S, Peters-Lidard CD, Verdin JP (2016) Evaluating ESA CCI soil moisture in East Africa. Int J Appl Earth Obs Geoinf 48:96–109

    Google Scholar 

  • Mearns LO, Rosenzweig C, Goldberg R (1997) Mean and variance change in climate scenarios: methods, agricultural applications, and measures of uncertainty. Clim Change 35:367–396

    Google Scholar 

  • Mohanty BP, Cosh MH, Lakshmi MC (2017) Soil moisture remote sensing: state-of-the-science. Vadose Zone J 16(1):1–9

    Google Scholar 

  • Morrow JG, Huggins DR, Carpenter-Boggs LA, Reganold JP (2016) Evaluating measures to assess soil health in long-term agroecosystem trials. Soil Sci Soc Am J 80(2):450–462

    CAS  Google Scholar 

  • Myeni L, Moeletsi ME, Clulow AD (2019) Present status of soil moisture estimation over the African continent. J Hydrol Reg Stud 21:14–24

    Google Scholar 

  • NASA (2015) Why it matters | Mission–soil moisture active passive (SMAP). https://smap.jpl.nasa.gov/mission/why-it-matters/. Accessed 11 July 2019

  • Nhamo L, Mabhaudhi T, Modi AT (2019) Preparedness or repeated short-term relief aid? Building drought resilience through early warning in southern Africa. Water SA 45(1):75–85

    Google Scholar 

  • Nicolai-Shaw N, Gudmundsson L, Hirschi M, Seneviratne SI (2016) Long-term predictability of soil moisture dynamics at the global scale: persistence versus large-scale drivers. Geophys Res Lett 43(16):8554–8562

    Google Scholar 

  • Palombo A, Pascucci S, Loperte A, Lettino A, Castaldi F, Muolo MR, Santini F (2019) Soil moisture retrieval by integrating TASI-600 airborne thermal data, WorldView 2 satellite data and field measurements: petacciato case study. Sensors 19(7):1515

    Google Scholar 

  • Peng J, Loew A, Merlin O, Verhoest NEC (2017) A review of spatial downscaling of satellite remotely sensed soil moisture. Rev Geophys 55(2):341–366

    Google Scholar 

  • Periasamy S, Shanmugam RS (2017) Multispectral and microwave remote sensing models to survey soil moisture and salinity. Land Degrad Dev 28(4):1412–1425

    Google Scholar 

  • Petropoulos GP, Ireland G, Barrett B (2015) Surface soil moisture retrievals from remote sensing: current status, products & future trends. Phys Chem Earth, Parts a/b/c 83:36–56

    Google Scholar 

  • Piao S, Ciais P, Huang Y, Shen Z, Peng S, Li J, Zhou L, Liu H, Ma Y, Ding Y, Friedlingstein P (2010) The impacts of climate change on water resources and agriculture in China. Nature 467(7311):43–51

    CAS  Google Scholar 

  • Plummer S, Lecomte P, Doherty M (2017) The ESA climate change initiative (CCI): a European contribution to the generation of the global climate observing system. Remote Sens Environ 203:2–8

    Google Scholar 

  • Raghavendra NS, Deka PC (2014) Support vector machine applications in the field of hydrology: a review. Appl Soft Comput 19:372–386

    Google Scholar 

  • Roy PS, Behera MD, Srivastav SK (2017) Satellite remote sensing: sensors, applications and techniques. Proc Natl Acad Sci India Sect A 87(4):465–472

    Google Scholar 

  • Sadeghi M, Babaeian E, Tuller M, Jones SB (2017) The optical trapezoid model: a novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote Sens Environ 198:52–68

    Google Scholar 

  • Sawada Y, Koike T (2016) Towards ecohydrological drought monitoring and prediction using a land data assimilation system: a case study on the Horn of Africa drought (2010–2011). J Geophys Res Atmos 121(14):8229–8242

    Google Scholar 

  • Seneviratne SI, Corti T, Davin EL, Hirschi M, Jaeger EB, Lehner I, Orlowsky B, Teuling AJ (2010) Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci Rev 99(3–4):125–161

    CAS  Google Scholar 

  • Shang W, Wu X, Zhao L, Yue G, Zhao Y, Qiao Y, Li Y (2016) Seasonal variations in labile soil organic matter fractions in permafrost soils with different vegetation types in the central Qinghai-Tibet Plateau. CATENA 137:670–678

    CAS  Google Scholar 

  • Siegfried J, Longchamps L, Khosla R (2019) Multispectral satellite imagery to quantify in-field soil moisture variability. J Soil Water Conserv 74(1):33–40

    Google Scholar 

  • Srivastava PK (2017) Satellite soil moisture: Review of theory and applications in water resources. Water Resour Manage 31(10):3161–3176

    Google Scholar 

  • Srivastava PK, Han D, Ramirez RMA, Islam T (2013) Appraisal of SMOS soil moisture at a catchment scale in a temperate maritime climate. J Hydrol 498:292–304

    Google Scholar 

  • Srivastava P, Pandey V, Suman S, Gupta M, Islam T (2016) Available data sets and satellites for terrestrial soil moisture estimation. In: Satellite soil moisture retrieval. Elsevier, pp 29–44

    Google Scholar 

  • Suepa T, Qi J, Lawawirojwong S, Messina JP (2016) Understanding spatio-temporal variation of vegetation phenology and rainfall seasonality in the monsoon Southeast Asia. Environ Res 147:621–629

    CAS  Google Scholar 

  • Tuttle S, Salvucci G (2016) Empirical evidence of contrasting soil moisture-precipitation feedbacks across the United States. Science 352(6287):825–828

    CAS  Google Scholar 

  • Walker JP, Willgoose GR, Kalma JD (2004) In situ measurement of soil moisture: a comparison of techniques. J Hydrol 293(1–4):85–99

    CAS  Google Scholar 

  • Walz Y, Min A, Dall K, Duguru M, de Leon JCV, Graw V, Dubovyk O, Sebesvari Z, Jordaan A, Post J (2020) Monitoring progress of the Sendai framework using a geospatial model: the example of people affected by agricultural droughts in Eastern Cape, South Africa. Progr Disaster Sci 5:100062

    Google Scholar 

  • Wang J, Ling Z, Wang Y, Zeng H (2016) Improving spatial representation of soil moisture by integration of microwave observations and the temperature–vegetation–drought index derived from MODIS products. ISPRS J Photogramm Remote Sens 113:144–154

    Google Scholar 

  • Wang Q, Li P, Pu Z, Chen X (2011) Calibration and validation of salt-resistant hyperspectral indices for estimating soil moisture in arid land. J Hydrol 408(3):276–285

    Google Scholar 

  • Wang Y, Yang J, Chen Y, Fang G, Duan W, Li Y, De Maeyer P (2019) Quantifying the effects of climate and vegetation on soil moisture in an arid area, China. Water 11(4):767

    Google Scholar 

  • Win TL (2020) Climate change opens up “frontier” farmland, but at what cost? https://news.trust.org/item/20200215072446-pl7ap/. Accessed 13 May 2020

  • Xing C, Chen N, Zhang X, Gong J (2017) A machine learning based reconstruction method for satellite remote sensing of soil moisture images with in situ observations. Remote Sens 9(5):484

    Google Scholar 

  • Xu Y, Wang L, Ross K, Liu C, Berry K (2018) Standardized soil moisture index for drought monitoring based on soil moisture active passive observations and 36 years of North American land data assimilation system data: a case study in the Southeast United States. Remote Sens 10(3):301

    Google Scholar 

  • Zabel F, Putzenlechner B, Mauser W (2014) Global agricultural land resources–a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions. PloS one 9(9):e107522

    Google Scholar 

  • Zaman B, McKee M, Neale CMU (2012) Fusion of remotely sensed data for soil moisture estimation using relevance vector and support vector machines. Int J Remote Sens 33(20):6516–6552

    Google Scholar 

  • Zhang D, Zhou G (2016) Estimation of soil moisture from optical and thermal remote sensing: a review. Sensors 16(8):1308

    Google Scholar 

  • Zhang H, Chang J, Zhang L, Wang Y, Li Y, Wang X (2018) NDVI dynamic changes and their relationship with meteorological factors and soil moisture. Environ Earth Sci 77(16):582

    Google Scholar 

  • Zhang X, Tang Q, Liu X, Leng G, Li Z (2016) Soil moisture drought monitoring and forecasting using satellite and climate model data over Southwestern China. J Hydrometeorol 18(1):5–23

    Google Scholar 

  • Zhuo L (2019) Satellite remote sensing of soil moisture for hydrological applications: a review of issues to be solved. In: The handbook of environmental chemistry. Springer, Berlin, Heidelberg

    Google Scholar 

  • Zhu Z, Wulder MA, Roy DP, Woodcock CE, Hansen MC, Radeloff VC, Healey SP, Schaaf C, Hostert P, Strobl P, Pekel J-F, Lymburner L, Pahlevan N, Scambos TA (2019) Benefits of the free and open landsat data policy. Remote Sens Environ 224:382–385

    Google Scholar 

Download references

Acknowledgements

The authors thank South Africa’s Water Research Commission project K5/2496/4 for funding, the numerous respondents who provided some of the information and the anonymous referees, whose comments helped us to improve this paper.

Author information

Authors and Affiliations

  1. Department of Geography & Environmental Science, University of Fort Hare, 1 King William’s Town Road, Private Bag X1314, Alice, 5700, Eastern Cape, South Africa

    Martin Munashe Chari & Hamisai Hamandawana

  2. Risk & Vulnerability Science Centre (RVSC), University of Fort Hare, 1 King William’s Town road, Private Bag X1314, Alice, 5700, Eastern Cape, South Africa

    Leocadia Zhou

Authors
  1. Martin Munashe Chari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamisai Hamandawana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leocadia Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors declare no conflict of interest.

Corresponding author

Correspondence to Martin Munashe Chari .

Editor information

Editors and Affiliations

  1. FTZ-NK, HAW Hamburg, Hamburg, Germany

    Walter Leal Filho

  2. Faculty of Agriculture, University of Belgrade, Zemun-Belgrade, Serbia

    Ilija Djekic

  3. DIL German Institute of Food Technologies, Quakenbrück, Niedersachsen, Germany

    Sergiy Smetana

  4. FTZ-NK, HAW Hamburg, Hamburg, Germany

    Marina Kovaleva

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chari, M.M., Hamandawana, H., Zhou, L. (2022). Integrating Remotely Sensed Soil Moisture in Assessing the Effects of Climate Change on Food Production: A Review of Applications in Crop Production in Africa. In: Leal Filho, W., Djekic, I., Smetana, S., Kovaleva, M. (eds) Handbook of Climate Change Across the Food Supply Chain. Climate Change Management. Springer, Cham. https://doi.org/10.1007/978-3-030-87934-1_12

Download citation

Publish with us

Policies and ethics

Search

Navigation