基于3种遥感指数的东北春玉米干旱识别对比

以东北春玉米为研究对象，探究利用植被光合特性的日光诱导叶绿素荧光(solar-induced chlorophyll fluorescence，SIF)指数、近红外-短波红外波段构建的归一化差值水分指数(normalized difference water index，NDWI)和可见光-近红外波段构建的归一化差值植被指数(normalized difference vegetation index，NDVI)识别东北春玉米干旱的准确度和敏感度。研究发现：SIF指数、NDWI和NDVI对干旱识别准确度均超过80%，其中重度干旱准确度超过94%，且在春玉米苗期表现最佳；3种指数对比可知，SIF指数在春玉米干旱识别的准确度和敏感度方面均最佳，分别为89.27%和81.65%，NDWI敏感度次之，NDVI最差。表明基于光合特性的SIF指数在识别东北春玉米干旱方面优于基于地物光谱特性所构建的植被指数。

Comparison of Drought Recognition of Spring Maize in Northeast China Based on 3 Remote Sensing Indices

Drought is a complex and widespread natural disaster, which has brought serious environmental and social problems and caused huge economic losses to China. For nearly half a century, the trend of aridification in Northeast China has been very significant, the area of influence has increased, and the degree of drought has also intensified significantly. Drought index is the basis of judging the occurrence of drought events, evaluating the degree of drought, clarifying the spatiotemporal characteristics of drought, and formulating measures for drought prevention and mitigation. Numerous studies indicate that solar-induced chlorophyll fluorescence(SIF), normalized difference vegetation index(NDVI), enhanced vegetation index(EVI), and normalized difference water index(NDWI) can be used to identify agricultural drought, but the research on comparing the ability of SIF index, NDWI and NDVI for identifying agricultural drought has not been reported publicly. Taking spring maize in Northeast China as the research object, NDWI and NDVI are calculated using the surface reflectance data MOD09A1. Combined with SIF index, NDWI and NDVI, the time series dataset of remote sensing drought index is constructed, respectively, and the accuracy and sensitivity of these three indices for identifying the drought is further explored. It shows that the accuracy of three indices in indentifying maize drought are all higher than 80%, and the accuracy of SIF index is the highest, reaching 89.27%. The accuracy for identifying severe drought is higher than mild and moderate drought for three indices, all reaching more than 94%, and the accuracy of SIF index exceeds 95%. From the perspective of different developmental stages of spring maize, the monitoring accuracy is the highest at seedling stage, reaching more than 90%, and is the lowest at jointing-booting stage and grain filling-maturity stage. The drought identifying accuracies of SIF index during four developmental periods of spring maize are all better than those of NDWI and NDVI. The sensitivities of SIF index, NDWI and NDVI to the identification of maize drought are different, and the SIF index has the highest sensitivity to drought identification, followed by NDWI, and NDVI is slightly lower. In terms of drought grades, the identifying sensitivities of three indices to severe drought are all higher than those of mild and moderate drought. Above all, compared with NDWI and NDVI, SIF index has better accuracy and sensitivity in identifying the drought of spring maize in Northeast China, and can make timely and accurate response to maize drought in Northeast China. The results have important practical significance for accurately identifying and predicting drought of spring maize in Northeast China, and taking effective drought-resistant measures in a timely and objective manner to minimize the damage to crops.