Abstract
The accurate and timely monitoring and evaluation of the regional grain crop yield is more significant for formulating import and export plans of agricultural products, regulating grain markets and adjusting the planting structure. In this study, an improved Carnegie–Ames–Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate winter wheat yield. Firstly, in 2009 the entire growing season of winter wheat in the two districts of Tongzhou and Shunyi of Beijing was divided into 54 stages at five-day intervals. Net Primary Production (NPP) of winter wheat was estimated by the improved CASA model with HJ-1A/B satellite images from 39 transits. For the 15 stages without HJ-1A/B transit, MOD17A2H data products were interpolated to obtain the spatial distribution of winter wheat NPP at 5-day intervals over the entire growing season of winter wheat. Then, an NPP-yield conversion model was utilized to estimate winter wheat yield in the study area. Finally, the accuracy of the method to estimate winter wheat yield with remote sensing images was verified by comparing its results to the ground-measured yield. The results showed that the estimated yield of winter wheat based on remote sensing images is consistent with the ground-measured yield, with R2 of 0.56, RMSE of 1.22 t ha−1, and an average relative error of −6.01%. Based on time-series satellite remote sensing images, the improved CASA model can be used to estimate the NPP and thereby the yield of regional winter wheat. This approach satisfies the accuracy requirements for estimating regional winter wheat yield and thus may be used in actual applications. It also provides a technical reference for estimating large-scale crop yield.
Highlights
Food is the basis of human survival and development
Research has focused on methods to calculate the net primary production (NPP) of crops based on remote sensing data to estimate the mass of crop dry matter quality and the associated crop yield [17]
The major objectives of this study are (1) to optimize the important parameters of the original Carnegie–Ames–Stanford approach (CASA) model in order to fit the growth of winter wheat in the study area; (2) to obtain the high temporal and spatial distribution of winter wheat NPP at five-day cycle intervals in the growing season based on the improved CASA model; (3) to analyze the distribution of winter wheat yield in 2009 combined with the NPP-yield conversion model
Summary
Food is the basis of human survival and development. in recent years, with the increase in population and human activity, the global ecosystem has been threatened. The high-precision crop-growth models: Crop Environment Resource Synthesis (CERES) [22], Soil Water Atmosphere Plant (SWAP) [23], and World Food Study (WOFOST) [24,25] take full account of the physiological process of crop formation These models are too complex and involve a large number of input parameters, so they are only suitable for small areas [8,26]. Parameter model combines the characteristics of empirical and mechanistic models and offers the advantages of both types of models, such as simple structure, less complicated parameters, easy access, and strong applicability It meets the requirements of national and even global large-scale NPP estimates, but can be used on regional scales. It is a good model for estimating yield by simulating crop NPP [27,28,29]
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