Blatchford, M. L., Mannaerts, C. M., Zeng, Y., Nouri, H. & Karimi, P. Status of accuracy in remotely sensed and in-situ agricultural water productivity estimates: A review. Remote Sensing of Environment 234, 111413, https://doi.org/10.1016/j.rse.2019.111413 (2019).
Google Scholar
Geerts, S. & Raes, D. Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas. Agricultural Water Management 96, 1275–1284, https://doi.org/10.1016/j.agwat.2009.04.009 (2009).
Google Scholar
Hellegers, P., Soppe, R., Perry, C. & Bastiaanssen, W. Combining remote sensing and economic analysis to support decisions that affect water productivity. Irrigation Science 27, 243–251, https://doi.org/10.1007/s00271-008-0139-7 (2009).
Google Scholar
Bastiaanssen, W. G. M. & Steduto, P. The water productivity score (WPS) at global and regional level: Methodology and first results from remote sensing measurements of wheat, rice and maize. The Science of the total environment 575, https://doi.org/10.1016/j.scitotenv.2016.09.032 (2017).
Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: A review. Earth Science Reviews 99, https://doi.org/10.1016/j.earscirev.2010.02.004 (2010).
Hu, X., Shi, L., Lin, L. & Zha, Y. Nonlinear boundaries of land surface temperature–vegetation index space to estimate water deficit index and evaporation fraction. Agricultural and Forest Meteorology 279, https://doi.org/10.1016/j.agrformet.2019.107736 (2019).
Bowen, I. S. The Ratio of Heat Losses by Conduction and by Evaporation from any Water Surface. Physical Review 27, 779–787, https://doi.org/10.1103/PhysRev.27.779 (1926).
Google Scholar
Penman, H. L. Natural evaporation from open water, hare soil and grass. Proceedings of the Royal Society of London. Series A, Mathematical and physical sciences 193, https://doi.org/10.1098/rspa.1948.0037 (1948).
Monteith, J. L. Evaporation and environment. The stage and movement of water in living organisms. Symp.soc.exp.biol.the Company of Biologists (1965).
Wang, K. & Dickinson, R. E. A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Reviews of Geophysics 50, https://doi.org/10.1029/2011RG000373 (2012).
Bastiaanssen, W. G. et al. A remote sensing surface energy balance algorithm for land (SEBAL) Part 1: Fomulation. Journal of hydrology 212, 213–229, https://doi.org/10.1016/S0022-1694(98)00253-4 (1998).
Google Scholar
Bastiaanssen, W. G. M. et al. A remote sensing surface energy balance algorithm for land (SEBAL) Part 2. Validation. Journal of Hydrology 212, https://doi.org/10.1016/S0022-1694(98)00254-6 (1998).
Su, Z. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Science 6, 85–99, https://doi.org/10.5194/hess-6-85-2002 (2002).
Google Scholar
Norman, J. M., Kustas, W. P. & Humes, K. S. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agricultural and Forest Meteorology 77, https://doi.org/10.1016/0168-1923(95)02265-y (1995).
Mu, Q., Heinsch, F. A., Zhao, M. & Running, S. W. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment 111, https://doi.org/10.1016/j.rse.2007.04.015 (2007).
Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment 115, 1781–1800, https://doi.org/10.1016/j.rse.2011.02.019 (2011).
Google Scholar
Fisher, J. B., Tu, K. P. & Baldocchi, D. D. Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sensing of Environment 112, 901–919, https://doi.org/10.1016/j.rse.2007.06.025 (2008).
Google Scholar
Kim, H. W., Hwang, K., Mu, Q., Lee, S. O. & Choi, M. Validation of MODIS 16 global terrestrial evapotranspiration products in various climates and land cover types in Asia. KSCE Journal of Civil Engineering 16, https://doi.org/10.1007/s12205-012-0006-1 (2012).
Velpuri, N. M., Senay, G. B., Singh, R. K., Bohms, S. & Verdin, J. P. A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET. Remote Sensing of Environment 139, https://doi.org/10.1016/j.rse.2013.07.013 (2013).
Jin, X. et al. Estimation of water productivity in winter wheat using the AquaCrop model with field hyperspectral data. Precision Agriculture 19, 1–17, https://doi.org/10.1007/s11119-016-9469-2 (2016).
Google Scholar
Felix, R., Clement, A., Igor, S. & Oscar, R. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sensing 5, 1704–1733, https://doi.org/10.3390/rs5041704 (2013).
Google Scholar
Lu, Y. et al. Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model. Agricultural Water Management 252, https://doi.org/10.1016/j.agwat.2021.106884 (2021).
Jin, X., Kumar, L., Li, Z., Feng, H. & Wang, J. A review of data assimilation of remote sensing and crop models. European Journal of Agronomy 92, https://doi.org/10.1016/j.eja.2017.11.002 (2018).
Weiss, M., Jacob, F. & Duveiller, G. Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment 236, https://doi.org/10.1016/j.rse.2019.111402 (2019).
Jin, X. et al. Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm. ISPRS Journal of Photogrammetry and Remote Sensing 126, 24–37 (2017).
Google Scholar
Tao, F., Rötter, R. P., Palosuo, T., Díaz-Ambrona, C. G. H. & Schulman, A. H. Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Global Change Biology 24, https://doi.org/10.1111/gcb.14019 (2017).
Jin, X. et al. A review of data assimilation of remote sensing and crop models. European Journal of Agronomy 92, 141–152, https://doi.org/10.1016/j.eja.2017.11.002 (2018).
Google Scholar
Anikó, K. et al. Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices. Agricultural and Forest Meteorology 260-261, 300–320, https://doi.org/10.1016/j.agrformet.2018.06.009 (2018).
Google Scholar
Wang, Y., Zhang, Z., Feng, L., Du, Q. & Runge, T. Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States. Remote Sensing 12, 1232, https://doi.org/10.3390/rs12081232 (2020).
Google Scholar
Franz, T. E. et al. The role of topography, soil, and remotely sensed vegetation condition towards predicting crop yield. Field Crops Research 252, https://doi.org/10.1016/j.fcr.2020.107788 (2020).
Noland, R. L. et al. Estimating alfalfa yield and nutritive value using remote sensing and air temperature. Field Crops Research 222, 189–196, https://doi.org/10.1016/j.fcr.2018.01.017 (2018).
Google Scholar
Cao, J., Zhang, Z., Luo, Y., Zhang, L. & Tao, F. Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine. European Journal of Agronomy, 126204, https://doi.org/10.1016/j.eja.2020.126204 (2021).
Jacinta, H. & Kerrie, M. Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review. Remote Sensing 10, 1365, https://doi.org/10.3390/rs10091365 (2018).
Google Scholar
Jin, X., Liu, S., Baret, F., Hemerlé, M. & Comar, A. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment 198, 105–114, https://doi.org/10.1016/j.rse.2017.06.007 (2017).
Google Scholar
Maimaitijiang, M. et al. Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment 237, 111599, https://doi.org/10.1016/j.rse.2019.111599 (2020).
Google Scholar
Hossein, A., Mohsen, A., Davoud, A., Salehi, S. H. & Soheil, R. Machine Learning Regression Techniques for the Silage Maize Yield Prediction Using Time-Series Images of Landsat 8 OLI. IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing PP, 1–15, https://doi.org/10.1109/JSTARS.2018.2823361 (2018).
Johansen, K. et al. Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest. Frontiers in Artificial Intelligence 3, 28, https://doi.org/10.3389/frai.2020.00028 (2020).
Google Scholar
Zhang, L., Ding, X., Shen, Y., Wang, Z. & Wang, X. Spatial Heterogeneity and Influencing Factors of Agricultural Water Use Efficiency in China. Resources and Environment in the Yangtze Basin 28, https://doi.org/10.11870/cjlyzyyhj201904008 (2019).
Cheng, M. et al. Satellite time series data reveal interannual and seasonal spatiotemporal evapotranspiration patterns in China in response to effect factors. Agric. Water Manage. 255, https://doi.org/10.1016/j.agwat.2021.107046 (2021).
Zhou, L. Comprehensive agricultural regionalization in China. (Agricultural Press of China, 1985).
Luo, Y., Zhang, Z., Chen, Y., Li, Z. & Tao, F. ChinaCropPhen1km: A high-resolution crop phenological dataset for three staple crops in China during 2000-2015 based on LAI products. Figshare https://doi.org/10.6084/m9.figshare.8313530.v6 (2019).
Luo, Y., Zhang, Z., Chen, Y., Li, Z. & Tao, F. ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products. Earth System Science Data 12, 197–214, https://doi.org/10.5194/essd-12-197-2020 (2020).
Google Scholar
Song, D. Second China Soil Survey. (Chinese Science Press, 1979).
Zhang, T., Yang, X., Wang, H., Li, Y. & Ye, Q. Climatic and technological ceilings for Chinese rice stagnation based on yield gaps and yield trend pattern analysis. Global Change Biology 20, 1289–1298, https://doi.org/10.1111/gcb.12428 (2014).
Google Scholar
Chen, Y., Zhang, Z. & Tao, F. Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data. European Journal of Agronomy 101, 163–173, https://doi.org/10.1016/j.eja.2018.09.006 (2018).
Google Scholar
Cheng, M. et al. Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China. Agricultural and Forest Meteorology 323, https://doi.org/10.1016/j.agrformet.2022.109057 (2022).
Amir, J. & Sinclair, T. A model of the temperature and solar-radiation effects on spring wheat growth and yield. Field Crops Research 28, 47–58, https://doi.org/10.1016/0378-4290(91)90073-5 (1991).
Google Scholar
Prince, S. D., Haskett, J., Steininger, M. & Wright, S. R. Net Primary Production of U.S. Midwest Croplands from Agricultural Harvest Yield Data. Ecological Applications 11, 1194–1205, https://doi.org/10.1890/1051-0761(2001)011[1194:NPPOUS]2.0.CO;2 (2001).
Google Scholar
Gilardelli, C. et al. Downscaling rice yield simulation at sub-field scale using remotely sensed LAI data. European journal of agronomy 103, 108–116, https://doi.org/10.1016/j.eja.2018.12.003 (2019).
Google Scholar
Shakoor, R., Hassan, M. Y., Raheem, A. & Wu, Y.-K. Wake effect modeling: A review of wind farm layout optimization using Jensen׳ s model. Renewable and Sustainable Energy Reviews 58, 1048–1059, https://doi.org/10.1016/j.rser.2015.12.229 (2016).
Google Scholar
Breiman, L. Random Forests. Machine Learning https://doi.org/10.1023/A:1010933404324 (2001).
Google Scholar
Li, L. et al. Crop yield forecasting and associated optimum lead time analysis based on multi-source environmental data across China. Agricultural and Forest Meteorology 308–309, https://doi.org/10.1016/j.agrformet.2021.108558 (2021).
Wang, L. A., Zhou, X., Zhu, X., Dong, Z. & Guo, W. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. The Crop Journal 4, 212–219, https://doi.org/10.1016/j.cj.2016.01.008 (2016).
Google Scholar
Feng, P. et al. Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique. Agricultural and Forest Meteorology 285-286, 107922, https://doi.org/10.1016/j.agrformet.2020.107922 (2020).
Google Scholar
Lu, F., Sun, Y. & Hou, F. Using UAV Visible Images to Estimate the Soil Moisture of Steppe. Water 12, 2334, https://doi.org/10.3390/w12092334 (2020).
Google Scholar
Wang, S. et al. High spatial resolution monitoring land surface energy, water and CO2 fluxes from an Unmanned Aerial System. Remote Sensing of Environment 229, 14–31, https://doi.org/10.1016/j.rse.2019.03.040 (2019).
Google Scholar
Chen, Y. et al. Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China. Remote Sensing of Environment 140, 279–293, https://doi.org/10.1016/j.rse.2013.08.045 (2014).
Google Scholar
Peralta, N., Assefa, Y., Du, J., Barden, C. & Ciampitti, I. Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield. Remote Sensing 8, 848, https://doi.org/10.3390/rs8100848 (2016).
Google Scholar
Russello, H. Convolutional neural networks for crop yield prediction using satellite images. IBM Center for Advanced Studies (2018).
You, J., Li, X., Low, M., Lobell, D. & Ermon, S. in Proceedings of the AAAI Conference on Artificial Intelligence.
Moran, P. A. Notes on continuous stochastic phenomena. Biometrika 37, 17–23 (1950).
Google Scholar
Imran, M., Stein, A. & Zurita-Milla, R. Using geographically weighted regression kriging for crop yield mapping in West Africa. International Journal of Geographical Information Systems 29, 234–257, https://doi.org/10.1080/13658816.2014.959522 (2015).
Google Scholar
Harries, K. Extreme spatial variations in crime density in Baltimore County, MD. Geoforum 37, 404–416, https://doi.org/10.1016/j.geoforum.2005.09.004 (2006).
Google Scholar
Ghulam, A. et al. Remote Sensing Based Spatial Statistics to Document Tropical Rainforest Transition Pathways. Remote Sensing 7, 6257–6279, https://doi.org/10.3390/rs70506257 (2015).
Google Scholar
Maimaitijiang, M., Ghulam, A., Sandoval, J. S. O. & Maimaitiyiming, M. Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data. International Journal of Applied Earth Observation Geoinformation 35, 161–174, https://doi.org/10.1016/j.jag.2014.08.020 (2015).
Google Scholar
Cheng, M. Long time series (2001-2015) high-resolution crop yield and water productivity dataset of China, Zenodo, https://doi.org/10.5281/zenodo.5121842 (2021).
Martens, B., Miralles, D. G., Lievens, H., Schalie, R. D. & Verhoest, N. GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development 10, https://doi.org/10.5194/gmd-10-1903-2017 (2017).
Wang, W., Cui, W., Wang, X. & Chen, X. Evaluation of GLDAS-1 and GLDAS-2 forcing data and Noah model simulations over China at the monthly scale. Journal of Hydrometeorology 17, 2815–2833, https://doi.org/10.1175/JHM-D-15-0191.1 (2016).
Google Scholar
Chen, X. et al. Development of a 10-year (2001–2010) 0.1° data set of land-surface energy balance for mainland China. Atmospheric Chemistry and Physics 14, 14471–14518, https://doi.org/10.5194/acp-14-13097-2014 (2014).
Google Scholar
Ramoelo, A. et al. Validation of Global Evapotranspiration Product (MOD16) using Flux Tower Data in the African Savanna, South Africa. Remote Sensing 6, https://doi.org/10.3390/rs6087406 (2014).
Yang, X., Yong, B., Ren, L., Zhang, Y. & Long, D. Multi-scale validation of GLEAM evapotranspiration products over China via ChinaFLUX ET measurements. International Journal of Remote Sensing https://doi.org/10.1080/01431161.2017.1346400 (2017).
Google Scholar
Hu, G., Jia, L. & Menenti, M. Comparison of MOD16 and LSA-SAF MSG evapotranspiration products over Europe for 2011. Remote Sensing of Environment 156, 510–526, https://doi.org/10.1016/j.rse.2014.10.017 (2015).
Google Scholar
Khan, M. S., Liaqat, U. W., Baik, J. & Choi, M. Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach. Agricultural and Forest Meteorology 252, 256–268, https://doi.org/10.1016/j.agrformet.2018.01.022 (2018).
Google Scholar
Glenn, E. P. et al. Scaling sap flux measurements of grazed and ungrazed shrub communities with fine and coarse-resolution remote sensing. Ecohydrology 1, 316–329, https://doi.org/10.1002/eco.19 (2008).
Google Scholar
Gamon, J. A. Reviews and Syntheses: optical sampling of the flux tower footprint. Biogeosciences 12, 4509–4523, https://doi.org/10.5194/bg-12-4509-2015 (2015).
Google Scholar
Cai, Y. et al. Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agricultural and Forest Meteorology 274, 144–159, https://doi.org/10.1016/j.agrformet.2019.03.010 (2019).
Google Scholar
Chen, X. et al. Prediction of Maize Yield at the City Level in China Using Multi-Source Data. Remote Sensing 13, https://doi.org/10.3390/rs13010146 (2021).
Guo, Y. et al. Integrated phenology and climate in rice yields prediction using machine learning methods. Ecological Indicators 120, 106935, https://doi.org/10.1016/j.ecolind.2020.106935 (2021).
Google Scholar
Yuan, W. et al. Estimating crop yield using a satellite-based light use efficiency model. Ecological Indicators 60, 702–709, https://doi.org/10.1016/j.ecolind.2015.08.013 (2016).
Google Scholar
Anandhi, A. Growing degree days – Ecosystem indicator for changing diurnal temperatures and their impact on corn growth stages in Kansas. Ecological Indicators 61, 149–158, https://doi.org/10.1016/j.ecolind.2015.08.023 (2016).
Google Scholar
Wart, J. V. Estimating Crop Yield Potential At National Scales. Field Crops Research 143, 34–43, https://doi.org/10.1016/j.fcr.2012.11.018 (2013).
Google Scholar
Kang, Y. S. et al. Yield prediction and validation of onion (Allium cepa L.) using key variables in narrowband hyperspectral imagery and effective accumulated temperature. Computers and Electronics in Agriculture 178, https://doi.org/10.1016/j.compag.2020.105667 (2020).
Long, D., Singh, V. P. & Li, Z.-L. How sensitive is SEBAL to changes in input variables, domain size and satellite sensor? Journal of Geophysical Research: Atmospheres 116, https://doi.org/10.1029/2011jd016542 (2011).
Liu, Z., Wang, L. & Wang, S. Comparison of Different GPP Models in China Using MODIS Image and ChinaFLUX Data. Remote Sensing 6, 10215–10231, https://doi.org/10.3390/rs61010215 (2014).
Google Scholar
Edreira, J., Guilpart, N., Sadras, V., Cassman, K. G. & Grassini, P. Water productivity of rainfed maize and wheat: A local to global perspective. Agricultural and Forest Meteorology 259, 364–373, https://doi.org/10.1016/j.agrformet.2018.05.019 (2018).
Google Scholar
Li, H. et al. Water Use Characteristics of Maize-Green Manure Intercropping Under Different Nitrogen Application Levels in the Oasis Irrigation Area Scientia Agricultura Sinica 54, 2608–2618 (2021).
Wang, S., Ibrom, A., Bauer-Gottwein, P. & Garcia, M. Incorporating diffuse radiation into a light use efficiency and evapotranspiration model: An 11-year study in a high latitude deciduous forest. Agricultural and Forest Meteorology https://doi.org/10.1016/j.agrformet.2017.10.023 (2018).
Google Scholar
Cheng, M. High-resolution crop yield and water productivity dataset generated using random forest and remote sensing. Zenodo https://doi.org/10.5281/zenodo.6444614 (2022).
Source: Ecology - nature.com