Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

Abstract

Sugarcane is a semi-perennial grass whose cultivation is characterized by an extended harvest season lasting several months leading to very high spatio-temporal variability of the crop development and radiometry. The objective of this paper is to understand this variability in order to propose appropriate spectral indicators for yield forecast. To do this, we used ground observations and SPOT4 and SPOT5 time series acquired monthly over a 2-year period over Reunion Island and Guadeloupe (French West Indies). We showed that variations in the NDVI (Normalized Difference Vegetation Index) of sugarcane at the field scale are the result of the interaction between the sugarcane crop calendar and plant phenology in a given climatic environment. We linked these variations to crop variables measured in the field (LAI and leaf color), and derived simple, appropriate NDVI-based indicators of sugarcane yield components at the field scale (cane yield and sugar content). For biomass forecast, the best correlation (r² = 0.78) was obtained with images acquired about 2 months before the harvest season, when all the fields are fully developed but before the maturation stage. For sugar content, a polynomial relationship (r² = 0.75) was observed between the field NDVI acquired during the maturation stage and sugar content in the stalk.

Remote Sensing Images in Support of Environmental Protocol: Monitoring the Sugarcane Harvest in São Paulo State, Brazil

“…Because it is a semi-perennial crop, the adequate image acquisition period for correct crop identification and mapping is extended, increasing the probability of available cloud-free images. Monitoring the harvest practice is further benefitted as the harvest season coincides with the period of less cloud persistence [6,9,14].…”

Forecasting Regional Sugarcane Yield Based on Time Integral and Spatial Aggregation of MODIS NDVI

“…Generally, time integration of NDVI is done throughout the calendar year [2,11,18]. At the field scale, [10,20] is considered a seasonal integration approach which utilized either the sowing or the harvesting date, while at the regional scale, [4] used growing degree days to compute in season NDVI for estimating yield and obtained good results. At regional scale in Portugal, [30] correlated yield of the current year with a 10-day NDVI data to develop a yield estimation model which explained 77%-88% of wine yield.…”

“…The value 15 corresponds to the length of the usual cropping cycle of the sugarcane (in months), while the value 11 corresponds to the length of the vegetative part (in months) which is mainly related to cane yield [10]. (1) where, NDVI m is the value of the NDVI for month m, w m is a coefficient equal to the NDVI normalized weight (Figure 3), and i is the length of the time integration (in months).…”

“…The advantage of remote sensing over ground systems, such as that used by the millers, is that they cover wide areas explicitly, providing timely spatial and temporal data. Such temporal data has been commended for monitoring vegetation development in response to changes in the environment and in response to human management practices [7][8][9][10]. These conditions vary over large areas due to diverse topography, soil type, rainfall distribution and management practices, to which sugarcane phenology and productivity is dependent [11].…”

Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island

“…Mulianga et al [12] proposed temporally weighing the NDVI integration in consideration of the sugarcane cropping calendar in western Kenya. In [13], the authors compared the use of maximum NDVI values and integrated NDVI values to estimate yield at the field scale on Reunion and Guadeloupe islands. These authors also reported that the relationship between yield and the maximum NDVI is exponential, which is a major limitation for future extrapolation or geographic-scale changes, whereas the relationship with the time integral of NDVI is linear.…”