Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series

Figures & data

Figure 1. Cropland distribution in the Fergana Valley. Rivers, intake points, and irrigation canals are shown in shades of blue. The settlements, in yellow, were derived by means of on-screen digitization of GoogleEarth™ images from 2016. Crop yield and acreage statistics were available for the districts in the eastern and south eastern parts solely (grey polygons).

Table 1. Number of reference fields per year and per crop. In total, there were 3,211 fieldswith known crop type.

Class	Number of reference fields
	2010	2011	2012	2014
Cotton	240	160	262	234
Winter wheat – Other	244	266	190	200
Orchards	124	114	137	102
Fallow	77	116	115	55
Rice	67	95	61	50
Other crops	130	20	56	30
Fishponds	15	18	19	14
Total number	897	789	840	685

Figure 2. Workflow of the method and its associated maps: crop type, crop yield, water-use efficiency, agricultural indicators, and identification of homogeneous areas.

Figure 3. Example of data fusion of NDVI could free maps in 2004. Left: original Landsat-7 image with SLC-off, middle: MODIS image, right: cloud- and gap-free synthetic image.

Figure 4. Generalized scheme of NDVI phenological metrics for the growing season. The phenological metrics are in fact statistical descriptors of the NDVI trajectory of a field.

Table 2. Multi-year cropland use intensity indicators, their purpose, and selected references.

Description	Formula	Purpose of use and options for management	References
Cropping frequency (CF)
Cropland-use intensity, counts the number of years $i$ a field was used/harvested. In this study, the maximum number of years $Y$ was 11 (2004–2014)	$\mathrm{C}\mathrm{F}=\sum _{\mathrm{i}=1}^{\mathrm{Y}}\mathrm{y}\left(\mathrm{i}\right)$	Facilitates identification of land affected by land degradation, and priority sites for alternative land use options. Supports assessment of potentials for intensification and the identification of abandoned land.	Alcantara et al. (2012); Dubovyk et al. (2013); Estel et al. (2016); Löw et al. (2016c)
Fallow cycles (FCI)
Number of recurring, consecutive sequences of fallow cropland (i.e., not sown, cropped, or ploughed). Recurring periods of 1 (S1), 2 (S2), and 3 years (S3) were considered. $N_S$ gives the maximum number of possible sequences in the observation period (e.g. 6 times S1). The maximum number of years $Y$ was 11 (2004–2014).	$FCI=\sum _{i=1}^{N_{S1}}\left(S1\ast \frac{N_{S1}}{Y}\right)+\left(S2\ast \frac{N_{S2}}{Y}\right)+\left(S3\ast \frac{N_{S3}}{Y}\right)$	Supports assessing management practices (fallow periods, soil regeneration) and intensity of land management.	Estel et al. (2016)
Multi cropping (MC)
Number of harvests over several years. The class wheat-other equals to 2 harvests, the class fallow equal to zero harvest, the reminder classes equal to one harvest.	$\mathrm{M}\mathrm{C}=0.5\ast \sum _{\mathrm{i}=2}^{22}\mathrm{s}\left(\mathrm{i}\right)$	Assessment of cropping intensity, crop practices and input demands (irrigation water, fertilizer)	Christmann et al. (2009); Estel et al. (2016)
Major crop class (MOD)
Most frequently (modus) cultivated crop in the observation period.	$\mathrm{M}\mathrm{O}\mathrm{D}=\bar{x}\left(i\right)$	Enables identification of monoculture, assessment of environmental impact of monoculture (e.g. land degradation, reduction of soil fertility), track diversification of cropping systems toward more integrative systems, for example double cropping with wheat or intercropping (not assessed in this study), interventions to sustain food production	Christmann et al. (2009)
Ratio actual vs. attainable crop yield (RYIELD)
Ratio of actual yield (as median crop yield over years) to attainable yield defined as the 95% percentile $Y_{95}$on the probability density function of crop yield.	$RYIELD=\frac{\overline{Y_{crop}}}{Y_{95}}$	Facilitates identification of local yield gaps and yield growth opportunities. Enables assessment of yield constraining factors.	Lobell (2013); Lobell, Ortiz-Monasterio, and Lee (2010); Sadras et al. (2015)
Ratio actual vs. attainable WUE (RWUE)
Ratio of actual WUE (as median WUE yield over years) to attainable WUE defined as the 95% percentile $WU{E}_{95}$on the probability density function of WUE.	$RWUE=\frac{\overline{WU{E}_{crop}}}{WU{E}_{95}}$	Facilitates identification of opportunities for improving WUE. Supports assessing potentials to save irrigation water, and manageable and agro-ecological system properties that determine spatial pattern of WUE.	Bastiaanssen and Harshadeep (2005); Cai et al. (2011); González-Dugo and Mateos (2008); Molden and Sakthivadivel (1999)

Table 3. Overall and per-class classification accuracies (F-score) of land cover maps. Pooled refers to the classification based on training data from four years pooled together. Accuracies of individual years with test data are given separately.

Class	2010	2011	2012	2014	Pooled	Average accuracies
Cotton	0.96	0.91	0.88	0.81	0.93	0.90
Fallow	1.00	1.00	1.00	0.61	1.00	0.92
Orchard	0.86	0.85	0.85	0.72	0.91	0.84
Other	0.87	0.79	0.75	0.86	0.81	0.84
Rice	0.90	0.79	0.86	0.59	0.88	0.79
Water/fishponds	1.00	1.00	1.00	0.90	1.00	0.98
Winter wheat – Other	0.92	0.92	1.00	0.87	0.98	0.94
Overall accuracy	0.91	0.91	0.90	0.89	0.93	0.91
Multi-class ROC curves	0.93	0.92	0.93	0.90	0.94	0.92

Figure 5. (a) Estimated crop area through the LUE-model by district and year against reported crop yields for 2010–2012. (b) Same as (a) but for crop yield.

Figure 6. Maps for assessing cropland use intensity and productivity pattern (based on yield and water-use efficiency) at the field level.

Figure 6. (Continued).

Table 4. Crop class statistics in Fergana Valley. Cultivated area per crop class are given in square kilometres. Statistics were derived from annual crop maps.

Year	Crop class (km^2)
	Fallow	Cotton	Rice	Other	Orchard	Winter wheat – Other	Water
2004	291.06	2387.71	348.8	183.82	1339.63	1795.51	15.61
2005	232.8	2363.48	457.22	242.07	1343.79	1707.27	15.52
2006	259.54	2523.62	123.24	227.82	1325.08	1887.33	15.52
2007	285.26	1957.95	661.85	175.6	1333.97	1931.91	15.61
2008	454.55	2225.54	181.5	341.5	1355.71	1787.84	15.52
2009	265.57	1495.41	570.48	239.75	1350.17	2425.24	15.52
2010	169.32	2294.58	193.89	344.48	1364.51	1979.86	15.52
2011	336.81	2451.2	125.02	239.09	1378.22	1815.97	15.84
2012	172.53	2563.05	198.99	379.29	1361.25	1671.28	15.78
2013	227.59	1948.27	631.63	289.11	1364.9	1880.27	20.38
2014	253.97	2378.35	239.89	323.12	1385.09	1760.54	21.19
Median	259.54	2363.48	239.89	242.07	1355.71	1815.97	15.61
Average	268.09	2235.38	339.32	271.42	1354.76	1876.64	16.55
Standard deviation	78.97	317.43	206.19	68.22	18.42	204.28	2.11

Figure 7. Homogeneous regions (clusters) with similar cropping intensity and productivity properties, based on the portioning-around-medoids clustering. Clustering was not performed for those fields never planted with cotton or winter wheat.

Figure 8. For three regions (clusters, see Figure 7), (a) distances to irrigation canals, intake points, settlements, and roads; (b) CF = cropping frequency, MC = multi cropping; (c) median values for AYW/AYC = average attainable yield for wheat/cotton, AWW/AWC = average water-use efficiency for wheat/cotton (Table 1), FCI = fallow cycles. Colours of the bars correspond to clusters in Figure 7.

Alcantara, C., T. Kuemmerle, A. V. Prishchepov, and V. C. Radeloff. 2012. “Mapping Abandoned Agriculture with Multi-Temporal MODIS Satellite Data.” Remote Sens Environment 124: 334–347. doi:10.1016/j.rse.2012.05.019.

 Web of Science ®Google Scholar

Dubovyk, O., G. Menz, C. Conrad, E. Kan, M. Machwitz, and A. Khamzina. 2013. “Spatio-Temporal Analyses of Cropland Degradation in the Irrigated Lowlands of Uzbekistan Using Remote-Sensing and Logistic Regression Modeling.” Environment Monitoring Assessment 185: 4775–4790. doi:10.1007/s10661-012-2904-6.

 PubMed Web of Science ®Google Scholar

Estel, S., T. Kuemmerle, C. Levers, M. Baumann, and P. Hostert. 2016. “Mapping Cropland-Use Intensity across Europe Using MODIS NDVI Time Series.” Environment Researcher Letters 11: 24015. doi:10.1088/1748-9326/11/2/024015.

 Web of Science ®Google Scholar

Christmann, S., Martius, C., Bedoshvili, D., Bobojonov, I., Carli, C., Devkota, K., Ibragimov, Z. & Mavlyanova, R., 2009. Food security and climate change in Central Asia and the Caucasus. CGIAR-PFU, ICARDA, Tashkent, Uzbekistan, 75.

 Google Scholar

Lobell, D. B. 2013. “The Use of Satellite Data for Crop Yield Gap Analysis.” F Crop Researcher 143: 56–64. doi:10.1016/j.fcr.2012.08.008.

 Web of Science ®Google Scholar

Lobell, D. B., J. I. Ortiz-Monasterio, and A. S. Lee. 2010. “Satellite Evidence for Yield Growth Opportunities in Northwest India.” F Crop Researcher 118: 13–20. doi:10.1016/j.fcr.2010.03.013.

 Web of Science ®Google Scholar

Sadras, V. O., K. G. G. Cassman, P. Grassini, A. J. Hall, W. G. M. Bastiaanssen, A. G. Laborte, A. E. Milne, G. Sileshi, and P. Steduto, 2015. Yield Gap Analysis of Field Crops, Methods and Case Studies. FAO Water Reports 41.

 Google Scholar

Bastiaanssen, W. G. M., and N. R. Harshadeep. 2005. “Managing Scarce Water Resources in Asia: The Nature of the Problem and Can Remote Sensing Help?.” Irrig Drain Systems 19: 269–284. doi:10.1007/s10795-005-5188-y.

 Google Scholar

Cai, X., D. Molden, M. Mainuddin, B. Sharma, M.-D. Ahmad, and P. Karimi. 2011. “Producing More Food with Less Water in a Changing World: Assessment of Water Productivity in 10 Major River Basins.” Water International 36: 42–62. doi:10.1080/02508060.2011.542403.

 Web of Science ®Google Scholar

González-Dugo, M. P., and L. Mateos. 2008. “Spectral Vegetation Indices for Benchmarking Water Productivity of Irrigated Cotton and Sugarbeet Crops.” Agricultural Water Managed 95: 48–58. doi:10.1016/j.agwat.2007.09.001.

 Web of Science ®Google Scholar

Molden, D., and R. Sakthivadivel. 1999. “Water Accounting to Assess Use and Productivity of Water.” International Journal Water Resources Developments 15: 55–71. doi:10.1080/07900629948934.

 Google Scholar