L-band vegetation optical depth seasonal metrics for crop yield assessment

REMOTE SENSING OF ENVIRONMENT

卷:212

Article

Chaparro, David^1,2  Piles, Maria³  Vall-Llossera, Merce^1,2  Camps, Adriano^1,2  Konings, Alexandra G.⁴  Entekhabi, Dara⁵ 

[1] Univ Politecn Cataluna, CommSensLab, Jordi Girona 1-3, E-08034 Barcelona, Spain

[2] IEEC UPC, Jordi Girona 1-3, E-08034 Barcelona, Spain

[3] Univ Valencia, IPL, Valencia 46980, Spain

[4] Stanford Univ, Dept Earth Syst Sci, Stanford, CA 94305 USA

[5] MIT, Dept Civil & Environm Engn, 77 Massachusetts Ave, Cambridge, MA 02139 USA

关键词: Crop yield;    Vegetation optical depth;    L-band radiometry;    SMAP;    Agroecosystems;   

DOI  :  10.1016/j.rse.2018.04.049

来源: Elsevier

【 摘 要 】

Attenuation of surface microwave emission due to the overlying vegetation is proportional to the density of the canopy and to its water content. The vegetation optical depth (VOD) parameter measures this attenuation. VOD could be a valuable source of information on agroecosystems, especially at lower frequencies for which greater portion of the vegetation canopy contributes to the observed brightness temperature. In the past, visible-infrared indices have been used to provide yield estimates based on measuring the photosynthetic activity from the surface canopy layer. These indices are affected by clouds and apply only in the presence of solar illumination. In this study we instead use the L-band microwave radiometer on board of the SMAP mission that provides VOD estimates in all weather and regardless of illumination. This study proposes a series of L-band VOD metrics for crop yield assessment using the first annual cycle of SMAP data (April 2015 to March 2016) over north-central United States. Maps of yield and crop proportion from the US Department of Agriculture are compared to VOD retrieved from SMAP with the Multi-Temporal Dual Channel Algorithm (MT-DCA). The yield-VOD relationship is explored using principal components regressions. Results show that 66% of yield variance is explained over the whole region by the first principal component (PC1). In corn-soy crops, PC1 explains 78% of yield amount, and maximum, standard deviation, and range of VOD capture the yield spatial patterns. Mixture of crops and scene heterogeneity reduced the unique relationships between VOD metrics and yield for specific crops. Hence, in wheat and mixed crops, PC1 explains 43% of yield variance. Results suggest that complementary information on maximum biomass, growth rate, and VOD amplitude can provide robust yield estimates, and that the uncertainty of these estimates depends on crop composition and heterogeneity. This study provides evidence that L-band VOD metrics can potentially be used to enhance crop yield forecasts.

【 授权许可】

Free