Remote Sensing Based Assessment Method For Crop Water Productivity And Planting Suitability

In arid region,agricultural production relies on heavily irrigation.The base for ensuring food production with decreasing agricultural water use is the quantitative assessment of crop water productivity so as to find effective ways to improve regional crop water productivity.In this paper,Hetao Irrigation District(HID)in Inner Mogolia,the largest irrigation district in arid area of China,is taken as the study area.By combining remote sensing data with field investigation and measurements,remote sensing(RS)based models for regional evapotranspiration,crop identification,and yield estiametion are developed in the study region.Furthermore,water productivity and planting suitability of main crops in HID are assessed,and crop planting structure of main crops in HID are optimized with different optimization objectives and spatial scales.Firstly,using the Normalized Difference Vegetation Index(NDVI)time series with the spatial resolution of 30 m retrieved from HJ-1A/1B satellite data,the planting distributions of main crops(maize and sunflower)in HID from 2009 to 2015 are identified based on the crop identification model of vegetation and phenological indexes space.The results show that the crop identification precision is high.For verification points,the overall accuracy is more than 70%.For the whole irrigation district,the relative error is less than 15% compared with the statistical crop planting area.Based on the results of crop identification,crop yield estimation models using vegetation and phenological indexes are established using random forest(RF)regression algorithm,and then multi-year yield distributions of maize and sunflower in HID are estimated.The results show that the optimal yield estimation model for maize is NDVI time series from 120 to 210 days with the interval of 10 days,while that for sunflower is the combination of NDVI and phenological indexes.The root mean square error(RMSE)of the estimated maize and sunflower yields is 0.93 and 0.34 t/ha,respectively.A novel dual-source remote sensing evapotranspiration model(HTEM-ABL)is developed by coupling the remote sensing evapotranspiration model(HTEM)with an atmospheric boundary layer(ABL)model.HTEM-ABL model uses remote sensing land surface temperature(LST)at two transit times of satellites in a day to estimate the canopy and soil temperature.In the estimation of instantaneous evapotranspiration,the air temperature that is greatly affected by underlying surface type is avoided,and the HTEMABL model has better applicability in areas with sparse ground meteorological observation staions.Based on the identified crop distributions,the estimated crop yield and water consumption during the crop growth periods,multi-year water productivity of maize and sunflower in HID is estimated at 30 m pixel scale.A crop planting suitability index is constructed based on the frequency distribution of crop water productivity,and then the planting suitability of main crops in HID is assessed.The results show that maize is mainly suitable for planting in Dengkou and Hangjinhouqi,while sunflower in Linhe and Wuyuan.Two optimization models of crop planting structure aiming at maximizing economic and water-saving benefits are developed.The crop planting structure of maize and sunflower in HID is optimized at the scales of county,3000 m and 300 m grids,respectively.The results show that the proportion of maize planted in most years after optimization is less than the current situation,while sunflower is greater than the current situation.The smaller the optimization scale,the more significant improvement of net income and the reduction of total crop water consumption during the crop growth periods in HID.These results can provide technical support for rational use of water and land resources.