Abstract:

【Objective】 Based on the Landsat-5 cell level, analyzing the factors affecting the estimating veracity, exploring vegetation indexes to clear up the space information difference of the non-cotton background, ascertaining the optimal time to monitoring the existing cotton-seedling density, as a result, the information would be provided for the cotton yield estimation and zones management. 【Method】 Sixty group sample data, consisting of the existing cotton-seedling density, longitude/latitude, sowing time, emergence time, were obtained through investigating the thirteen fields (630 hm2), and three sample dot data in every sample area were averaged. EVI and DEVI were picked up from the images of five times from sowing time to full-flowering. And then sixty group sample data were divided into two equal parts to establish and text models. The linear models were established by data of the middle sowing time and the all three sowing times on the basis of EVI and DEVI, respectively, and the model veracity was tested by RMSE and REPE. At last, the existing cotton-seedling density at the country scale was retrieved by the best model.【Result】 The analysis results showed that the difference of seedling size caused by the different emergence times debased the estimation veracity; and as the sample of the different sowing times, the testing result of the middle sowing time models showed that the absolute error of the existing cotton-seedling number of each hectare on 9 June and 25 June was 2.05×104 plants/hm2 and 2.08×104 plants/hm2, respectively, the absolute error under the three sowing times was 2.80×104 plants/hm2 and 2.53×104 plants/hm2 respectively. DEVI Compared with EVI on 24 May cleared up the effect of the space difference of the non-cotton background to some extent, and then the estimation time advanced from 9 June to 24 May. Giving attention to veracity and time of models, the optimal time monitoring the existing cotton-seedling density was from budding to full-flowering. As an example, I function on 9 June was used to monitor the existing cotton-seedling density in the 148th farm of Xinjiang Construction Crops, the result could exhibit the distributing proportion and space characteristics rightly. 【Conclusion】 The result showed that emergence time and the space background difference are the main factors affecting the estimation veracity of the existing cotton-seedling density, and the models based on different sowing times could improve the estimation level, and DEVI could make the monitoring time in advance, and the optimal time for estimating the existing cotton-seedling density was from budding to full-flowering, and the demonstration indicated that the research result was feasible.

Key words: cotton, existing cotton-seedling density, cell level, monitoring