Institute of Agricultural Remote Sensing & Information Application, Zijingang Campus, Zhejiang University, Hangzhou, China.
PLoS One. 2013 Aug 13;8(8):e70816. doi: 10.1371/journal.pone.0070816. eCollection 2013.
Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha(-1). Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly.
利用遥感数据进行谷物产量预测已经在小麦和玉米研究中得到了广泛的研究，但在水稻、大麦、燕麦和大豆中，此类信息有限。本研究提出了一种新的水稻产量预测框架，可以消除技术发展、施肥和管理改进的影响，可用于省级水稻产量预测的开发和实施。该技术需要在足够的时间内收集遥感数据，并记录该地区的作物产量。为了进行水稻产量预测，较长的归一化差异植被指数（NDVI）时间序列比较短的时间序列更为可取，因为较长时间序列中对比鲜明的季节为建立具有广泛应用范围的回归模型提供了机会。产量与年份的回归分析表明，水稻产量每年增加 50 至 128 公斤/公顷。已经为中国五个典型的水稻种植省份开发了用于遥感水稻产量预测的逐步回归模型。遥感水稻产量预测模型表明，NDVI 对水稻产量的影响始终是正向的。1982 年至 2004 年，预测产量与实际产量之间的相关性非常显著，没有明显的异常值。独立验证发现，总体相对误差约为 5.82%，2005 年和 2006 年的大部分相对误差小于 5%，具体取决于研究区域。所提出的模型可用于省级水稻产量的业务预测。本文描述的方法可以应用于任何具有足够 NDVI 数据时间序列和相应历史产量信息的作物，只要历史产量显著增加。
