利用水稻冠层光谱特征诊断土壤氮素营养状况

系统测定了不同秸秆还田和氮肥处理下水稻(Oryza sativa)关键生育期的冠层反射光谱及土壤速效氮含量,并对两者之间的关系进行了详尽的分析。结果表明: 土壤速效氮含量在整个水稻生育期内均与可见光波段反射率呈负相关,与近红外波段反射率呈正相关。归一化及比值植被指数与土壤速效氮含量有更好的相关性,分蘖期要优于其它生育时期,以870、1 220 nm波段与560和710 nm波段的组合最佳,但两者的关系易受土壤等背景的干扰。而转换型土壤调节植被指数TSAVI能较好地消除分蘖期土壤背景的影响,两生态点可用统一的方程来拟合,用该研究中所筛选出的最佳波段组合计算出的TSAVI的表现更好,尤其是870 nm波段和710 nm波段的组合,决定系数(R²)由0.46提高到0.60。抽穗期和灌浆期由1 220和760 nm计算的比值指数R(1 220, 760)和新土壤调节植被指数SAVI(1 220,760)与土壤速效氮含量的关系则不受生态点的影响,可用统一回归方程来拟合。这说明水稻冠层反射光谱可以用来评价稻田土壤肥力状况,但仍需进一步研究。

ESTIMATION OF SOIL NITROGEN STATUS WITH CANOPY REFLECTANCE SPECTRA IN RICE

Background and Aims Making nitrogen (N) recommendations without knowing the N supply capability of a soil can lead to inefficient use of N and potential pollution of the ground water. Conventional soil test techniques are destructive and time consuming. Remote sensing of canopy reflectance has the capacity of non_destructive and rapid estimating crop N status, and could be potentially used in evaluating soil N supply status. Methods Thus, rice (Oryza sativa) canopy reflectance spectra and soil available N content (NH4+_N+NO3-_N) of different straw and N treatments were measured at key development stages of rice at two sites with different soil types and rice varieties. All possible normalized difference vegetation indices (NDVI) and ratio vegetation indices (RVI) composed by two bands, and some hybrid vegetation indices such as SAVI (soil adjusted vegetation index),TSAVI (transformed soil adjusted vegetation index) were calculated. Then the correlations between these VIs and soil available N were analyzed and the best regression equation was also investigated. Key Results The correlations of soil available N content and canopy reflectance were negative at visible band, while positive at near infrared bands during the whole growing cycle. NDVI and RVI were well correlated with soil available N content, with the best stage of tillering and the best indices of the combination of 870, 1 220 nm and 560, 710 nm. Relationship between soil available N content and the best vegetation indices screened at tillering stage were influenced by soil background. While TSAVI was proved to be the best choice for removing the effect of soil background, and the relationship was consistent at two sites, with the best regression equation in exponential form. TSAVI calculated with the best band combinations screened in this study can improve the relationship, especially for the TSAVI calculated with 870 and 710 nm, with the decision coefficient(R2) increased from 0.46 to 0.60. At the heading and fillings tage, the ratios of vegetation index and new SAVI calculated by 1 220 and 760 nm were well related to soil available N content independent of sites. Conclusions Our observations suggest that evaluating soil N status at rice growing stage of with canopy reflectance spectra is feasible, but more research still need to be conducted to test and improve the soil N prediction model.