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The enhancement of cultivated land quality is an important measure to improve the production capability and ensure the food safety. At the construction course of cultivated land quality informatization, novel methods and techniques are promising to improve the current working pattern as well as raise the efficiency of cultivated land quality management. Irrigation guarantee capability is an important element of cultivated land quality monitoring, evaluation and construction. Evapotranspiration (ET) derived from remote sensing can be used for rapid monitoring of irrigation guarantee capability of cultivated land at the regional scale. In order to improve the acquisition efficiency of the regional cultivated land quality monitoring, a new evaluation method was proposed for irrigation capability evaluation of cultivated land based on Moderate Resolution Imaging Spectroradiometer (MODIS) ET products. First, in order to reduce the representative errors of meteorological data, the relationship between the monthly potential evapotranspiration (PET) parameter derived from MODIS evaporation products and the reference crop evapotranspiration (ET0) calculated from observed meteorological data with the Penman-Monteith (P-M) formula was analyzed. The continuous ET0 in space was obtained from the regression model. And then, water requirements for crop were obtained by the map of crop type from the classification of Landsat TM data and space-continuous ET0. Furthermore, the annual water requirement was calculated according to the difference of the water requirement for crop and the rainfall which was effectively consumed by crop. Additionally, based on the regional water balance principle, the actual quantity of irrigation was obtained from the difference of actual evaporation derived from ET products of MODIS and effective rainfall. The evaluation index of irrigation guarantee capability is defined as the ratio of the annual irrigation requirement and the actual quantity of irrigation, and used for the monitoring and evaluation of cultivated land irrigation guarantee capability. Finally, application and analysis were conducted in Hengshui City, Hebei Province, China. The R2 of the regression model between the PET and ET0 from meteorological stations reached 0.89. And the calculated results of the evaluation index of irrigation guarantee capability from 2005 to 2012 accurately reflected temporal-spatial variations for the cultivated land irrigation capability. Meanwhile, the comprehensive evaluation of irrigation capability, which was from the supervised classification results based on actual survey, had a good agreement with the classification results of irrigation probability in the gradation on agriculture land quality which had been updated in 2012. The area with no grade difference was up to 37% of the total cells, 38% of the study area had one-grade difference, and the area with two-grade difference was 19% of the study area. The area with three-grade difference only accounted for 6%. The grade difference of more than 75% of the cultivated land was less than one grade. Errors were mainly concentrated in the cultivated land around the city, because the MOD16A2 product with 1-km resolution was inadequate to accurately represent the information of that complex planting structure of cultivated land, and this problem would be solved with the development and improvement of remote sensing earth observation systems with higher resolution. The evaluation index of irrigation capability proposed in this paper has the advantage of clear physical meaning and easy data accessing. The experiment results indicate that the method can meet the needs of the regional cultivated land quality monitoring and evaluation. [ABSTRACT FROM AUTHOR]

Assimilating biophysical parameters derived from remote sensing into crop growth model is an important approach to improve performance of regional crop yield estimation. Currently, most researches adopt single remote sensing data source and single variable assimilation strategy, which cannot accurately reflect the interactive process among radiation, temperature and water, limiting the performance of data assimilation model. Leaf area index (LAI) and evapotranspiration (ET) are two key biophysical variables related to crop growth and grain yield. The study presents an assimilation framework to assimilate MODIS LAI product (MCD15A3) and MODIS ET product (MOD16A2) into the Soil-Water-Atmosphere-Plant (SWAP) model to improve the estimates of winter wheat yield at the regional scale. The spatial scale is one of the most challenging issues in the field of remote sensing, and the mismatching between remote sensing observations and state variables of crop model has an important impact on the performance of data assimilation model. MODIS LAI and ET products in 1 km scale are severely underestimated compared to the ground-based observations because of the mixed pixel effect and the heterogeneity within pixel, and hence the scale factors of 1 km MODIS products and the crop model's simulated parameters are totally different. So the direct assimilation of 1 km MODIS products would cause abnormal results. At present, there are two types of solutions to mitigate the scale issue; one is to scale down remote sensing parameters or scale up crop model's simulated variables, and the other is to assimilate the time series trend characteristics derived from remote sensing into crop model. In this study, two types of cost functions were constructed through comparing the generalized vector angle or first order difference of the observations and modeled LAI and ET time series trends during the growing season. Two key model parameters (i.e. irrigation water depth and emergence date) were selected as the reinitialized parameters needed to be optimized through minimizing the cost function using the SCE-UA optimization algorithm, and then the optimized parameters were input into the SWAP model for winter wheat yield estimation. Winter wheat yield assimilation estimation accuracy was evaluated for two cost functions (e.g., vector angle and first order difference) at field and regional scales. The results showed that yield estimation accuracy had been greatly improved with assimilation of LAI and ET trends than without assimilation. Furthermore, vector angle strategy (r=0.75, RMSE=494 kg/hm2) had achieved higher accuracy than first order difference (r=0.73, RMSE=667 kg/hm2). In this study, equal weights were given to LAI and ET in the cost function. Giving different weights according to the errors of the LAI and ET data at different crop phenological stages would further improve the performance of data assimilation model. LAI and ET were selected as the assimilation variables in the cost function. Additional important state variables (e.g., soil moisture) that also closely related to grain yield should be incorporated into data assimilation framework to test the impacts to the crop yield. So, a more robust approach needs to be developed to simultaneously assimilate multiple biophysical variables (e.g., LAI, ET/PET, soil moisture), and hybrid approaches, such as combining the use of EnKF and 4DVar, would allow simultaneous estimates and updating of the model parameters and state variables, and would further improve crop yield estimation at field and regional scales. [ABSTRACT FROM AUTHOR]