Millet yield estimates in the Sahel using satellite derived soil moisture time series

In the Sahel, crop growth and yield are strongly linked to climate fluctuations. The low and erratic rainfall the Sahel region has experienced for several years led to poor harvests, associated with dramatic food crises and famines. Consequently, numerous studies were conducted to develop innovative techniques to estimate crop yield based on satellite measurements. Unlike most approaches which use rainfall, temperature or vegetation indices to derive crop yield estimates, the present study investigates the potential of satellite-derived soil moisture products. This study focuses on millet, a major food crop in Africa. A first step was devoted to analyzing the relation between soil moisture and millet yield at the local scale using ground-based soil moisture and millet yield measurements obtained at ten site locations in Niger. Then, the statistical relationship obtained at the local scale was assessed at the regional scale (Niger, Mali, Senegal and Burkina Faso) using satellite-based soil moisture mapping (based on a simple land-surface model and a satellite precipitation product) and compared to millet yield estimates from the Food and Agriculture Organization (FAO) database. It was shown that millet yield variations are closely linked to soil moisture variations during two key periods of the plant growth: the “grain filling” and the “reproductive” periods. Soil moisture variations during these two periods led to explain 81% (R² = 0.81) of the FAO millet yield variations from 1998 to 2014 in the Sahel.

[1]  Martha C. Anderson,et al.  An Intercomparison of Drought Indicators Based on Thermal Remote Sensing and NLDAS-2 Simulations with U.S. Drought Monitor Classifications , 2013 .

[2]  C. Albergel,et al.  From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations , 2008 .

[3]  Thomas Gaiser,et al.  How satellite rainfall estimate errors may impact rainfed cereal yield simulation in West Africa , 2013 .

[4]  D. Lobell,et al.  Improving the monitoring of crop productivity using spaceborne solar‐induced fluorescence , 2016, Global change biology.

[5]  María Amparo Gilabert,et al.  Use of NOAA-AVHRR NDVI data for environmental monitoring and crop forecasting in the Sahel. Preliminary results , 1992 .

[6]  C. Baron,et al.  Assessing the benefits of weather and seasonal forecasts to millet growers in Niger , 2016 .

[7]  A. Al Bitar,et al.  Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation , 2016 .

[8]  Y. Kerr,et al.  Rainfall estimation by inverting SMOS soil moisture estimates: A comparison of different methods over Australia , 2016 .

[9]  Jasmeet Judge,et al.  Assimilation of SMOS Soil Moisture for Quantifying Drought Impacts on Crop Yield in Agricultural Regions , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  W. Wagner,et al.  A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data , 1999 .

[11]  Douglas K. Bolton,et al.  Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics , 2013 .

[12]  John P. Fulton,et al.  An overview of current and potential applications of thermal remote sensing in precision agriculture , 2017, Comput. Electron. Agric..

[13]  Christian Baron,et al.  From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[14]  S. Irwin,et al.  Forecast performance of WASDE price projections for U.S. corn , 2015 .

[15]  J. Eitzinger,et al.  The ASCAT Soil Moisture Product: A Review of its Specifications, Validation Results, and Emerging Applications , 2013 .

[16]  Shusen Wang,et al.  Crop yield forecasting on the Canadian Prairies using MODIS NDVI data , 2011 .

[17]  Martha C. Anderson,et al.  The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields. , 2016 .

[18]  Amine Merzouki,et al.  Estimation of Leaf Area Index (LAI) in corn and soybeans using multi-polarization C- and L-band radar data , 2015 .

[19]  Wade T. Crow,et al.  Evaluating the Utility of Remotely Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  Jiancheng Shi,et al.  The Soil Moisture Active Passive (SMAP) Mission , 2010, Proceedings of the IEEE.

[21]  J. Rockström,et al.  Water, nutrients and slope position in on-farm pearl millet cultivation in the Sahel , 1997, Plant and Soil.

[22]  Niall P. Hanan,et al.  AMMA-CATCH studies in the Sahelian region of West-Africa: an overview , 2009 .

[23]  Sylvie Galle,et al.  On-farm spatial and temporal variability of soil and water in pearl millet cultivation , 1999 .

[24]  Christian Baron,et al.  The onset of the rainy season and farmers' sowing strategy for pearl millet cultivation in Southwest Niger , 2011 .

[25]  Yi Y. Liu,et al.  Global long‐term passive microwave satellite‐based retrievals of vegetation optical depth , 2011 .

[26]  Yann Kerr,et al.  Correcting satellite-based precipitation products through SMOS soil moisture data assimilation in two land-surface models of different complexity: API and SURFEX , 2017 .

[27]  M. S. Moran,et al.  Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence , 2014, Proceedings of the National Academy of Sciences.

[28]  Yann Kerr,et al.  Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission , 2001, IEEE Trans. Geosci. Remote. Sens..

[29]  Heather McNairn,et al.  Radar Remote Sensing of Agricultural Canopies: A Review , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  C. Field,et al.  A reanalysis using improved leaf models and a new canopy integration scheme , 1992 .

[31]  D. Lobell,et al.  What aspects of future rainfall changes matter for crop yields in West Africa? , 2015 .

[32]  James Hansen,et al.  Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction , 2013 .

[33]  C. Thorncroft,et al.  African Monsoon Multidisciplinary Analysis: An International Research Project and Field Campaign , 2006 .

[34]  C. Gruhier,et al.  A simple and effective method for correcting soil moisture and precipitation estimates using AMSR-E measurements , 2013 .

[35]  Chris Funk,et al.  Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe , 2009 .

[36]  Danny Lo Seen,et al.  Crop Monitoring Using Vegetation and Thermal Indices for Yield Estimates: Case Study of a Rainfed Cereal in Semi-Arid West Africa , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[37]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[38]  Klaus Scipal,et al.  Validation of ERS scatterometer‐derived soil moisture data in the central part of the Duero Basin, Spain , 2005 .

[39]  A. Al Bitar,et al.  SMOS soil moisture product evaluation over West-Africa from local to regional scale , 2015 .

[40]  Christopher O. Justice,et al.  A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM) , 2015, Remote. Sens..

[41]  Kelly K. Caylor,et al.  Terrestrial hydrological controls on land surface phenology of African savannas and woodlands , 2014 .

[42]  J. Janowiak,et al.  CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution , 2004 .