Remote sensing based yield monitoring: Application to winter wheat in United States and Ukraine

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (15–18%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%).     

基于SPOT影像的可吸入颗粒物遥感反演

通过用SPOT 5遥感影像近红外与红光波段计算的差值植被指数(DVI)与实测的可吸入颗粒物(PM10)浓度数据建立模型,对2007年北京市城区SPOT 5影像进行PM10浓度反演试验,反演结果与实测数据所反映的PM10空间分布趋势基本一致.2007年北京市城区北部PM10污染较为严重,南部污染相对较弱; 绿地和水体附近...     

归一化植被指数与降水量,土壤湿度的关系

归一化植被指数是描述植被绿度及生长状况的指数，由于植被生长依赖于环境条件因此ＮＤＶＩ与环境参量的关系是应用ＮＤＶＩ监测环境状况的基础。分别应用位于中国北部的干旱及半干旱地区的降水量资料对ＮＤＶＩ与降水量之间的关系地分析，结果表明仅在干旱半干旱地区生长季末的累积降水量与累积ＮＤＶＩ存在着显著的非线性关系，相关系数为０．７８。