Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection
">
<p>Illustration of positive filtering effects on satellite-derived (10-daily) NDVI time series (from Atzberger and Eilers [<a href="#b31-remotesensing-05-01704" class="html-bibr">31</a>]; modified). For filtering and gap filling, the Whittaker smoother was used. The NDVI time series are from SPOT-VGT. (<b>a</b>) Example NDVI profiles from different land cover types before (top) and after (bottom) smoothing with the Whittaker filter. The profiles were randomly extracted within the state of Mato Grosso in Brazil; (<b>b</b>) Effects of the smoothing on vegetation anomalies (z-scores) over a randomly selected grassland pixel in Mato Grosso (Brazil).</p> ">
<p>Global map of NDVI anomalies during the 2012 growing season (August). Negative anomalies are visible mainly in central US, central Asia and northern Brazil, while a positive situation is evident in eastern China and southern Brazil. Data are from SPOT VEGETATION (as compared to 1999–2010 average). Anomalies are expressed in absolute NDVI units.</p> ">
<p>Z-scores (standardized differences from long term average (LTA)) of cumulated <span class="html-italic">NDVI</span> and rainfall estimates over the two crop seasons (called Gu and Deyr) of an agro-pastoral district in Southern Somalia (Bardera). The two failed seasons in 2010 and 2011, which lead to a major famine in the country are clearly visible. In most cases a major <span class="html-italic">NDVI</span> anomaly is explained by a similar rainfall anomaly, but this is not always the case, as for example in the Gu season of 2003. In such cases temporal distribution of rainfall has to be taken into consideration as well as non-rainfall related factors influencing the <span class="html-italic">NDVI</span>, such as changes in crop area.</p> ">
<p>NDVI/yield linear regressions for cereals in North Africa (from Maselli and Rembold [<a href="#b46-remotesensing-05-01704" class="html-bibr">46</a>]; modified). (<b>Top</b>) Evolution of the coefficient of determination (R2) between radiometric variable and yield over time. (<b>Bottom</b>) Scatter plots between NDVI and cereal yield. Each dot corresponds to the annual yield for agricultural areas at national level and to the monthly NDVI best correlated to yield.</p> ">
<p>Dependence of <span class="html-italic">ε<sub>b</sub></span> (chickpea) on water stress (Singh and Sri Rama [<a href="#b77-remotesensing-05-01704" class="html-bibr">77</a>], from Atzberger [<a href="#b56-remotesensing-05-01704" class="html-bibr">56</a>]).</p> ">
<p>Linear regression between the seasonally (from sowing to harvest) integrated absorbed <span class="html-italic">PAR</span> and dry matter at harvest (g·m<sup>−2</sup>) of nine commercial winter wheat plots from Atzberger [<a href="#b56-remotesensing-05-01704" class="html-bibr">56</a>].</p> ">
<p>Simplified scheme of a crop process model. Model state variables such as development phase, organ dry mass, or leaf area index are linked to input variables, including weather, and geographic and management variables (from Delecolle <span class="html-italic">et al.</span>[<a href="#b50-remotesensing-05-01704" class="html-bibr">50</a>]).</p> ">
<p>Schematic description of the re-calibration method using radiometric information as inputs. The crop growth model simulates the leaf development (<span class="html-italic">LAI</span>) over time. This information is used to simulate the canopy reflectance using an appropriate canopy reflectance model. In the non-linear minimization procedure, new model coefficients are assigned to the crop growth model such that the residues between observed and simulated reflectances are minimized (Atzberger [<a href="#b56-remotesensing-05-01704" class="html-bibr">56</a>] from Delecolle <span class="html-italic">et al.</span>[<a href="#b50-remotesensing-05-01704" class="html-bibr">50</a>]).</p> ">
<p>Schematic description of the “forcing” method. The complete time profile of a crop state variable (here: <span class="html-italic">LAI</span>) is reconstructed from remote sensing data and introduced into the dynamic crop growth model at each time step in the simulation (from Delecolle <span class="html-italic">et al.</span>[<a href="#b50-remotesensing-05-01704" class="html-bibr">50</a>]).</p> ">
Abstract
:1. Introduction and Short History
1.1. Structure of the Review
- qualitative crop monitoring
- quantitative crop yield predictions by regression modeling
- quantitative yield forecasts using (mechanistic and dynamic) crop growth models
1.2. Definition of “Low Resolution Images”
1.3. Data Quality Issues
2. Qualitative Crop Monitoring
3. Quantitative Crop Yield Predictions by Regression Modeling
3.1. Use of Remotely Sensed Indicators for Crop Yield Prediction
3.2. Concomitant Use of Remotely Sensed Indicators Together with Bio-Climatic Indicators
4. Quantitative Yield Forecasts Using Crop Growth Models
4.1. Estimation and Mapping of Absorbed Photosynthetically Active Radiation for Use in Monteith’s Efficiency Equation
- where: ΔDM: increase in dry matter (DM) or net primary production (NPP) (g·m−2·d−1)
- PAR: incident photosynthetically active solar radiation (MJ·m−2·d−1)
- fAPAR: Fraction of intercepted and absorbed PAR; fAPAR is estimated from the remotely sensed NDVI by means of a linear equation, suggested by Myneni and Williams [81] (dimensionless)
- εb: Photosynthetic efficiency, [82] (g·MJ−1)
- CO2fert: Normalized CO2 fertilization factor, [85] (dimensionless)
- r(T): fraction of assimilated photosynthesis consumed by autotrophic respiration; r is modeled as a simple linear function of daily mean air temperature, [60].
4.2. Parameterization and Re-Calibration of Crop Growth Models
- they are dynamic, in that they operate on a time step for ordering input data and updating state variables
- they contain parameters that allow a general scheme of equations to be adopted to the specific growth behavior of different crop species
- they include a strategy for describing phenological development of a crop to order organ appearance and assimilate portioning.
- Re-calibration or re-parameterization
- Re-initialization
- Forcing
- Updating
5. Conclusions
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Sensor | Platform | Spectral Range | Number of Bands | Resolution | Swath Width | Repeat Coverage | Launch |
---|---|---|---|---|---|---|---|
AVHHR | NOAA POES 6–19 | VIS, NIR, MIR | 5 | 1,100 m | 2,400 km | 12 h | 1978 |
AVHRR | METOP | VIS, NIR, SWIR, MIR | 5 | 1,100 m | 2,400 km | 12 h | 2007 |
SEAWIFS | Orbview-2 | VIS, NIR | 8 | 1,100 m 4,500 m | 1,500 km 2,800 km | 1 day | 1997 |
VEGETATION | SPOT 4, 5 | VIS, NIR, SWIR | 4 | 1,100 m | 2,200 km | 1 day | 1998 |
MODIS | EOS AM1/PM1 | VIS, NIR, SWIR, TIR | 36 | 250–1,000 m | 2,330 km | <2 days | 1999 |
MERIS | ENVISAT | VIS, NIR | 15 | 300 m (1,200 m) | 1,150 km | <3 days | 2000 |
PROBA-V | PROBA-V | VIS, NIR, SWIR | 4 | 300 m (1,000 m) | 2,250 km | 1 day | Foreseen 2014 |
SENTINEL 3 | SENTINEL | VIS, NIR, SWIR | 21 | 300 m | 1,270 km | <2 days | Foreseen 2014 |
No. | Model Parameters | Year | r2 | Standard Error of Estimate kg·ha−a | Residual Mean Square | F-ratio | Tabled F at P < 0.05 | n |
---|---|---|---|---|---|---|---|---|
1 | aiNDVIb | 1990 | 0.606 | 254 | 64,745 | 15.38 | 4.96 | 12 |
2 | aiNDVI + b | 1991 | 0.738 | 186 | 34,639 | 39.43 | 4.60 | 16 |
3 | aiNDVI + b | 90 + 91 | 0.645 | 220 | 48,349 | 47.20 | 4.23 | 28 |
4 | a ∑8iNDVI PAR) + b | 1990 | 0.523 | 280 | 78,358 | 10.97 | 4.96 | 12 |
5 | a ∑2iNDVI PAR) + b | 1991 | 0.690 | 202 | 40,995 | 31.15 | 4.60 | 16 |
6 | a ∑6iNDVI PAR) + b | 90 + 91 | 0.202 | 329 | 108,570 | 6.60 | 4.23 | 28 |
7 | aiNDVI + b AGRIPRC + c | 90 + 91 | 0.660 | 208 | 43,246 | 22.30 | 3.42 | 26 |
8 | aiNDVI + b > TLUDEN + c | 90 + 91 | 0.695 | 197 | 38,815 | 26.16 | 3.42 | 26 |
9 | AGRIPRC > 22.5 aiNDVI+b | 90 + 91 | 0.729 | 166 | 27,534 | 35.04 | 4.67 | 26 |
10 | AGRIPRC> 22.5 aiNDVI + b AGRIPRC + c | 90 + 91 | 0.814 | 143 | 20,530 | 26.22 | 3.89 | 15 |
11 | AGRIPRC > 22.5 aiNDVI + b TLUDEN + c | 90 + 91 | 0.883 | 113 | 12,858 | 45.44 | 3.89 | 15 |
12 | AGRIPRC < 22.5 aiNDVI + b | 90 + 91 | 0.663 | 244 | 59,637 | 21.68 | 4.84 | 13 |
13 | AGRIPRC < 22.5 aiNDVI + b AGRIPRC + c | 90 + 91 | 0.763 | 212 | 44,846 | 12.87 | 4.46 | 11 |
14 | AGRIPRC < 22.5 aiNDVI + b TLUDEN + c | 90 + 91 | 0.685 | 244 | 59,537 | 8.70 | 4.46 | 11 |
Share and Cite
Rembold, F.; Atzberger, C.; Savin, I.; Rojas, O. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sens. 2013, 5, 1704-1733. https://doi.org/10.3390/rs5041704
Rembold F, Atzberger C, Savin I, Rojas O. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sensing. 2013; 5(4):1704-1733. https://doi.org/10.3390/rs5041704
Chicago/Turabian StyleRembold, Felix, Clement Atzberger, Igor Savin, and Oscar Rojas. 2013. "Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection" Remote Sensing 5, no. 4: 1704-1733. https://doi.org/10.3390/rs5041704